U.S. patent application number 16/293913 was filed with the patent office on 2020-09-10 for breaking down the load time of a web page into coherent components.
The applicant listed for this patent is Cisco Technology, Inc.. Invention is credited to Puneet Anand, Swathi Bhat, Adam Ross Cath, Maxwell Taylor Virgil, Brian Robert Zaik.
Application Number | 20200287956 16/293913 |
Document ID | / |
Family ID | 1000004132428 |
Filed Date | 2020-09-10 |
![](/patent/app/20200287956/US20200287956A1-20200910-D00000.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00001.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00002.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00003.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00004.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00005.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00006.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00007.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00008.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00009.png)
![](/patent/app/20200287956/US20200287956A1-20200910-D00010.png)
View All Diagrams
United States Patent
Application |
20200287956 |
Kind Code |
A1 |
Cath; Adam Ross ; et
al. |
September 10, 2020 |
BREAKING DOWN THE LOAD TIME OF A WEB PAGE INTO COHERENT
COMPONENTS
Abstract
In one embodiment, a monitoring process monitors timing of
navigations and resources of a web page load, and attributes timing
of the navigations and resources into corresponding timing
components. In particular, the monitoring process may attribute
timing of static first-party resources and timing of hypertext
transfer protocol (HTTP) redirects to a core frontend timing
component, and may attribute timing of dynamic first-party
resources to a core backend timing component. The monitoring
process may also attribute timing of content delivery network (CDN)
resources to a CDN timing component, and may attribute timing of
non-CDN third-party resources to a third-party timing component.
Lastly, the monitoring process may further attribute timing of
network infrastructure connections to a core connections timing
component. Accordingly, the monitoring process may then perform one
or more actions based on the attributing steps.
Inventors: |
Cath; Adam Ross; (San
Francisco, CA) ; Bhat; Swathi; (Campbell, CA)
; Virgil; Maxwell Taylor; (San Francisco, CA) ;
Zaik; Brian Robert; (San Francisco, CA) ; Anand;
Puneet; (Saratoga, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cisco Technology, Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
1000004132428 |
Appl. No.: |
16/293913 |
Filed: |
March 6, 2019 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 41/0681 20130101;
H04L 41/22 20130101; H04L 61/1511 20130101; H04L 67/2814 20130101;
H04L 67/025 20130101 |
International
Class: |
H04L 29/08 20060101
H04L029/08; H04L 12/24 20060101 H04L012/24; H04L 29/12 20060101
H04L029/12 |
Claims
1. A method, comprising: monitoring, by a monitoring process,
timing of navigations and resources of a web page load;
attributing, by the monitoring process, timing of static
first-party resources and timing of hypertext transfer protocol
(HTTP) redirects to a core frontend timing component; attributing,
by the monitoring process, timing of dynamic first-party resources
to a core backend timing component; attributing, by the monitoring
process, timing of content delivery network (CDN) resources to a
CDN timing component; attributing, by the monitoring process,
timing of non-CDN third-party resources to a third-party timing
component; attributing, by the monitoring process, timing of
network infrastructure connections to a core connections timing
component; and performing, by the monitoring process, one or more
actions based on the attributing steps.
2. The method as in claim 1, further comprising: dividing the
timing of the web page load into discrete spans, each span
beginning at a corresponding start of either a navigation or a
resource; and in response to a plurality of particular timing
components being concurrently active during any particular span,
prorating that particular span across the plurality of particular
timing components.
3. The method as in claim 2, wherein prorating comprises:
distributing a timing of that particular span to each of the
plurality of particular timing components based on a number of
resources and navigations in each respective timing component of
the plurality of particular timing components.
4. The method as in claim 2, further comprising: attributing idle
time spans to the core frontend timing component.
5. The method as in claim 1, further comprising: determining CDN
resources based on a corresponding internet protocol (IP) address
being located within IP address blocks for one or more known
CDNs.
6. The method as in claim 1, further comprising: determining
first-party resources based on a domain name server (DNS)
lookup.
7. The method as in claim 1, further comprising: determining
third-party resources based on resources being neither CDN
resources nor first-party resources.
8. The method as in claim 1, further comprising: determining
network infrastructure connections as one or more of a domain name
server (DNS) connection, a transmission control protocol (TCP)
connection, and a secure socket layer (SSL) connection.
9. The method as in claim 8, further comprising: distinguishing
DNS, TCP, and SSL connections as sub-components of the core
connections timing component.
10. The method as in claim 1, further comprising: determining
network infrastructure connections as only connections under at
least partial first-party control.
11. The method as in claim 1, wherein static first-party resources
comprise one or more of stylesheets, javascript files, or images,
and wherein dynamic first-party resources comprise one or more of
Ajax requests or Extensible Markup Language Hypertext Transfer
Protocol Requests (XHRs).
12. The method as in claim 1, wherein the one or more actions
comprise at least one of: displaying attributions of timing to
respective timing components; reporting the attributions of timing
to respective timing components; triggering one or more alarms
based on the attributions of timing to respective timing
components; and detecting and mitigating one or more anomalies
based on the attributions of timing to respective timing
components.
13. A tangible, non-transitory, computer-readable medium storing
program instructions that cause a computer to execute a process
comprising: monitoring timing of navigations and resources of a web
page load; attributing timing of static first-party resources and
timing of hypertext transfer protocol (HTTP) redirects to a core
frontend timing component; attributing timing of dynamic
first-party resources to a core backend timing component;
attributing timing of content delivery network (CDN) resources to a
CDN timing component; attributing timing of non-CDN third-party
resources to a third-party timing component; attributing timing of
network infrastructure connections to a core connections timing
component; and performing one or more actions based on the
attributing steps.
14. The computer-readable medium as in claim 13, wherein the
process further comprises: dividing the timing of the web page load
into discrete spans, each span beginning at a corresponding start
of either a navigation or a resource; and in response to a
plurality of particular timing components being concurrently active
during any particular span, prorating that particular span across
the plurality of particular timing components.
15. The computer-readable medium as in claim 14, wherein prorating
comprises: distributing a timing of that particular span to each of
the plurality of particular timing components based on a number of
resources and navigations in each respective timing component of
the plurality of particular timing components.
16. The computer-readable medium as in claim 13, wherein the
process further comprises: determining CDN resources based on a
corresponding internet protocol (IP) address being located within
IP address blocks for one or more known CDNs; determining
first-party resources based on a domain name server (DNS) lookup;
and determining third-party resources based on resources being
neither CDN resources nor first-party resources.
17. The computer-readable medium as in claim 13, wherein the
process further comprises: determining network infrastructure
connections as one or more of a domain name server (DNS)
connection, a transmission control protocol (TCP) connection, and a
secure socket layer (SSL) connection.
18. The computer-readable medium as in claim 13, wherein the
process further comprises: determining network infrastructure
connections as only connections under at least partial first-party
control.
19. The computer-readable medium as in claim 13, wherein the one or
more actions comprise at least one of: displaying attributions of
timing to respective timing components; reporting the attributions
of timing to respective timing components; triggering one or more
alarms based on the attributions of timing to respective timing
components; and detecting and mitigating one or more anomalies
based on the attributions of timing to respective timing
components.
20. An apparatus, comprising: one or more network interfaces to
communicate with a network; a processor coupled to the network
interfaces and configured to execute one or more processes; and a
memory configured to store a process executable by the processor,
the process when executed configured to: monitor timing of
navigations and resources of a web page load; attribute timing of
static first-party resources and timing of hypertext transfer
protocol (HTTP) redirects to a core frontend timing component;
attribute timing of dynamic first-party resources to a core backend
timing component; attribute timing of content delivery network
(CDN) resources to a CDN timing component; attribute timing of
non-CDN third-party resources to a third-party timing component;
attribute timing of network infrastructure connections to a core
connections timing component; and perform one or more actions based
on the attributing steps.
Description
TECHNICAL FIELD
[0001] The present disclosure relates generally to computer
networks, and, more particularly, to breaking down the load time of
a web page into coherent components.
BACKGROUND
[0002] The Internet and the World Wide Web have enabled the
proliferation of web services available for virtually all types of
businesses. Due to the accompanying complexity of the
infrastructure supporting the web services, it is becoming
increasingly difficult to maintain the highest level of service
performance and user experience to keep up with the increase in web
services. For example, it can be challenging to piece together
monitoring and logging data across disparate systems, tools, and
layers in a network architecture. Moreover, even when data can be
obtained, it is difficult to directly connect the chain of events
and cause and effect.
[0003] In one particular example, the performance of a webpage is
influenced by multiple architectural components, often owned by
different teams within a customer organization. Identifying which
component is the bottleneck is a key step in performance
optimization/troubleshooting, because it narrows the investigation
greatly and allows customers to route problems to the right
team.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The embodiments herein may be better understood by referring
to the following description in conjunction with the accompanying
drawings in which like reference numerals indicate identically or
functionally similar elements, of which:
[0005] FIGS. 1A-1B illustrate an example computer network;
[0006] FIG. 2 illustrates an example computing device/node;
[0007] FIG. 3 illustrates an example application intelligence
platform;
[0008] FIG. 4 illustrates an example system for an
application-aware intrusion detection system;
[0009] FIG. 5 illustrates an example computing system implementing
the disclosed technology;
[0010] FIG. 6 illustrates an example anatomy of a web page
load;
[0011] FIG. 7 illustrates an example of a single resource load;
[0012] FIG. 8 illustrates an example of a typical waterfall (time
chart) of a page load;
[0013] FIG. 9 illustrates an example of resource concurrency;
[0014] FIGS. 10A-10B illustrate an example chart of time components
and associated actionable root causes; and
[0015] FIG. 11 illustrates an example simplified procedure for
breaking down the load time of a web page into coherent components
in accordance with one or more embodiments described herein.
DESCRIPTION OF EXAMPLE EMBODIMENTS
Overview
[0016] According to one or more embodiments of the disclosure, a
monitoring process monitors timing of navigations and resources of
a web page load, and attributes timing of the navigations and
resources into corresponding timing components. In particular, the
monitoring process may attribute timing of static first-party
resources and timing of hypertext transfer protocol (HTTP)
redirects to a core frontend timing component, and may attribute
timing of dynamic first-party resources to a core backend timing
component. The monitoring process may also attribute timing of
content delivery network (CDN) resources to a CDN timing component,
and may attribute timing of non-CDN third-party resources to a
third-party timing component. Lastly, the monitoring process may
further attribute timing of network infrastructure connections to a
core connections timing component. Accordingly, the monitoring
process may then perform one or more actions based on the
attributing steps.
[0017] According to one or more further embodiment herein, the
monitoring process may also divide the timing of the web page load
into discrete spans, each span beginning at a corresponding start
of either a navigation or a resource, and in response to a
plurality of particular timing components being concurrently active
during any particular span, may prorate that particular span across
the plurality of particular timing components (e.g., distributing a
timing of that particular span to each of the plurality of
particular timing components based on a number of resources and
navigations in each respective timing component of the plurality of
particular timing components).
[0018] Other embodiments are described below, and this overview is
not meant to limit the scope of the present disclosure.
Description
[0019] A computer network is a geographically distributed
collection of nodes interconnected by communication links and
segments for transporting data between end nodes, such as personal
computers and workstations, or other devices, such as sensors, etc.
Many types of networks are available, ranging from local area
networks (LANs) to wide area networks (WANs). LANs typically
connect the nodes over dedicated private communications links
located in the same general physical location, such as a building
or campus. WANs, on the other hand, typically connect
geographically dispersed nodes over long-distance communications
links, such as common carrier telephone lines, optical lightpaths,
synchronous optical networks (SONET), synchronous digital hierarchy
(SDH) links, or Powerline Communications (PLC), and others. The
Internet is an example of a WAN that connects disparate networks
throughout the world, providing global communication between nodes
on various networks. Other types of networks, such as field area
networks (FANs), neighborhood area networks (NANs), personal area
networks (PANs), enterprise networks, etc. may also make up the
components of any given computer network.
[0020] The nodes typically communicate over the network by
exchanging discrete frames or packets of data according to
predefined protocols, such as the Transmission Control
Protocol/Internet Protocol (TCP/IP). In this context, a protocol
consists of a set of rules defining how the nodes interact with
each other. Computer networks may be further interconnected by an
intermediate network node, such as a router, to extend the
effective "size" of each network.
[0021] Smart object networks, such as sensor networks, in
particular, are a specific type of network having spatially
distributed autonomous devices such as sensors, actuators, etc.,
that cooperatively monitor physical or environmental conditions at
different locations, such as, e.g., energy/power consumption,
resource consumption (e.g., water/gas/etc. for advanced metering
infrastructure or "AMI" applications) temperature, pressure,
vibration, sound, radiation, motion, pollutants, etc. Other types
of smart objects include actuators, e.g., responsible for turning
on/off an engine or perform any other actions. Sensor networks, a
type of smart object network, are typically shared-media networks,
such as wireless or power-line communication networks. That is, in
addition to one or more sensors, each sensor device (node) in a
sensor network may generally be equipped with a radio transceiver
or other communication port, a microcontroller, and an energy
source, such as a battery. Generally, size and cost constraints on
smart object nodes (e.g., sensors) result in corresponding
constraints on resources such as energy, memory, computational
speed and bandwidth.
[0022] FIG. 1A is a schematic block diagram of an example computer
network 100 illustratively comprising nodes/devices, such as a
plurality of routers/devices interconnected by links or networks,
as shown. For example, customer edge (CE) routers 110 may be
interconnected with provider edge (PE) routers 120 (e.g., PE-1,
PE-2, and PE-3) in order to communicate across a core network, such
as an illustrative network backbone 130. For example, routers 110,
120 may be interconnected by the public Internet, a multiprotocol
label switching (MPLS) virtual private network (VPN), or the like.
Data packets 140 (e.g., traffic/messages) may be exchanged among
the nodes/devices of the computer network 100 over links using
predefined network communication protocols such as the Transmission
Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol
(UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay
protocol, or any other suitable protocol. Those skilled in the art
will understand that any number of nodes, devices, links, etc. may
be used in the computer network, and that the view shown herein is
for simplicity.
[0023] In some implementations, a router or a set of routers may be
connected to a private network (e.g., dedicated leased lines, an
optical network, etc.) or a virtual private network (VPN), such as
an MPLS VPN thanks to a carrier network, via one or more links
exhibiting very different network and service level agreement
characteristics.
[0024] FIG. 1B illustrates an example of network 100 in greater
detail, according to various embodiments. As shown, network
backbone 130 may provide connectivity between devices located in
different geographical areas and/or different types of local
networks. For example, network 100 may comprise local/branch
networks 160, 162 that include devices/nodes 10-16 and
devices/nodes 18-20, respectively, as well as a data center/cloud
environment 150 that includes servers 152-154. Notably, local
networks 160-162 and data center/cloud environment 150 may be
located in different geographic locations. Servers 152-154 may
include, in various embodiments, any number of suitable servers or
other cloud-based resources. As would be appreciated, network 100
may include any number of local networks, data centers, cloud
environments, devices/nodes, servers, etc.
[0025] In some embodiments, the techniques herein may be applied to
other network topologies and configurations. For example, the
techniques herein may be applied to peering points with high-speed
links, data centers, etc. Furthermore, in various embodiments,
network 100 may include one or more mesh networks, such as an
Internet of Things network. Loosely, the term "Internet of Things"
or "IoT" refers to uniquely identifiable objects (things) and their
virtual representations in a network-based architecture. In
particular, the next frontier in the evolution of the Internet is
the ability to connect more than just computers and communications
devices, but rather the ability to connect "objects" in general,
such as lights, appliances, vehicles, heating, ventilating, and
air-conditioning (HVAC), windows and window shades and blinds,
doors, locks, etc. The "Internet of Things" thus generally refers
to the interconnection of objects (e.g., smart objects), such as
sensors and actuators, over a computer network (e.g., via IP),
which may be the public Internet or a private network.
[0026] Notably, shared-media mesh networks, such as wireless
networks, are often on what is referred to as Low-Power and Lossy
Networks (LLNs), which are a class of network in which both the
routers and their interconnect are constrained: LLN routers
typically operate with constraints, e.g., processing power, memory,
and/or energy (battery), and their interconnects are characterized
by, illustratively, high loss rates, low data rates, and/or
instability. LLNs are comprised of anything from a few dozen to
thousands or even millions of LLN routers, and support
point-to-point traffic (between devices inside the LLN),
point-to-multipoint traffic (from a central control point such at
the root node to a subset of devices inside the LLN), and
multipoint-to-point traffic (from devices inside the LLN towards a
central control point). Often, an IoT network is implemented with
an LLN-like architecture. For example, as shown, local network 160
may be an LLN in which CE-2 operates as a root node for
nodes/devices 10-16 in the local mesh, in some embodiments.
[0027] FIG. 2 is a schematic block diagram of an example computing
device (e.g., apparatus) 200 that may be used with one or more
embodiments described herein, e.g., as any of the devices shown in
FIGS. 1A-1B above, and particularly as specific devices as
described further below. The device may comprise one or more
network interfaces 210 (e.g., wired, wireless, etc.), at least one
processor 220, and a memory 240 interconnected by a system bus 250,
as well as a power supply 260 (e.g., battery, plug-in, etc.).
[0028] The network interface(s) 210 contain the mechanical,
electrical, and signaling circuitry for communicating data over
links coupled to the network 100, e.g., providing a data connection
between device 200 and the data network, such as the Internet. The
network interfaces may be configured to transmit and/or receive
data using a variety of different communication protocols. For
example, interfaces 210 may include wired transceivers, wireless
transceivers, cellular transceivers, or the like, each to allow
device 200 to communicate information to and from a remote
computing device or server over an appropriate network. The same
network interfaces 210 also allow communities of multiple devices
200 to interconnect among themselves, either peer-to-peer, or up
and down a hierarchy. Note, further, that the nodes may have two
different types of network connections 210, e.g., wireless and
wired/physical connections, and that the view herein is merely for
illustration. Also, while the network interface 210 is shown
separately from power supply 260, for devices using powerline
communication (PLC) or Power over Ethernet (PoE), the network
interface 210 may communicate through the power supply 260, or may
be an integral component of the power supply.
[0029] The memory 240 comprises a plurality of storage locations
that are addressable by the processor 220 and the network
interfaces 210 for storing software programs and data structures
associated with the embodiments described herein. The processor 220
may comprise hardware elements or hardware logic adapted to execute
the software programs and manipulate the data structures 245. An
operating system 242, portions of which are typically resident in
memory 240 and executed by the processor, functionally organizes
the device by, among other things, invoking operations in support
of software processes and/or services executing on the device.
These software processes and/or services may comprise one or more
functional processes 246, and on certain devices, an illustrative
"Page Load Monitoring" process 248, as described herein. Notably,
functional processes 246, when executed by processor(s) 220, cause
each particular device 200 to perform the various functions
corresponding to the particular device's purpose and general
configuration. For example, a router would be configured to operate
as a router, a server would be configured to operate as a server,
an access point (or gateway) would be configured to operate as an
access point (or gateway), a client device would be configured to
operate as a client device, and so on.
[0030] It will be apparent to those skilled in the art that other
processor and memory types, including various computer-readable
media, may be used to store and execute program instructions
pertaining to the techniques described herein. Also, while the
description illustrates various processes, it is expressly
contemplated that various processes may be embodied as modules
configured to operate in accordance with the techniques herein
(e.g., according to the functionality of a similar process).
Further, while the processes have been shown separately, those
skilled in the art will appreciate that processes may be routines
or modules within other processes.
[0031] ----Application Intelligence Platform ----
[0032] The embodiments herein relate to an application intelligence
platform for application performance management. In one aspect, as
discussed with respect to FIGS. 3-5 below, performance within a
networking environment may be monitored, specifically by monitoring
applications and entities (e.g., transactions, tiers, nodes, and
machines) in the networking environment using agents installed at
individual machines at the entities. As an example, applications
may be configured to run on one or more machines (e.g., a customer
will typically run one or more nodes on a machine, where an
application consists of one or more tiers, and a tier consists of
one or more nodes). The agents collect data associated with the
applications of interest and associated nodes and machines where
the applications are being operated. Examples of the collected data
may include performance data (e.g., metrics, metadata, etc.) and
topology data (e.g., indicating relationship information). The
agent-collected data may then be provided to one or more servers or
controllers to analyze the data.
[0033] FIG. 3 is a block diagram of an example application
intelligence platform 300 that can implement one or more aspects of
the techniques herein. The application intelligence platform is a
system that monitors and collects metrics of performance data for
an application environment being monitored. At the simplest
structure, the application intelligence platform includes one or
more agents 310 and one or more servers/controllers 320. Note that
while FIG. 3 shows four agents (e.g., Agent 1 through Agent 4)
communicatively linked to a single controller, the total number of
agents and controllers can vary based on a number of factors
including the number of applications monitored, how distributed the
application environment is, the level of monitoring desired, the
level of user experience desired, and so on.
[0034] The controller 320 is the central processing and
administration server for the application intelligence platform.
The controller 320 serves a browser-based user interface (UI) 330
that is the primary interface for monitoring, analyzing, and
troubleshooting the monitored environment. The controller 320 can
control and manage monitoring of business transactions (described
below) distributed over application servers. Specifically, the
controller 320 can receive runtime data from agents 310 (and/or
other coordinator devices), associate portions of business
transaction data, communicate with agents to configure collection
of runtime data, and provide performance data and reporting through
the interface 330. The interface 330 may be viewed as a web-based
interface viewable by a client device 340. In some implementations,
a client device 340 can directly communicate with controller 320 to
view an interface for monitoring data. The controller 320 can
include a visualization system 350 for displaying the reports and
dashboards related to the disclosed technology. In some
implementations, the visualization system 350 can be implemented in
a separate machine (e.g., a server) different from the one hosting
the controller 320.
[0035] Notably, in an illustrative Software as a Service (SaaS)
implementation, a controller instance 320 may be hosted remotely by
a provider of the application intelligence platform 300. In an
illustrative on-premises (On-Prem) implementation, a controller
instance 320 may be installed locally and self-administered.
[0036] The controllers 320 receive data from different agents 310
(e.g., Agents 1-4) deployed to monitor applications, databases and
database servers, servers, and end user clients for the monitored
environment. Any of the agents 310 can be implemented as different
types of agents with specific monitoring duties. For example,
application agents may be installed on each server that hosts
applications to be monitored. Instrumenting an agent adds an
application agent into the runtime process of the application.
[0037] Database agents, for example, may be software (e.g., a Java
program) installed on a machine that has network access to the
monitored databases and the controller. Database agents query the
monitored databases in order to collect metrics and pass those
metrics along for display in a metric browser (e.g., for database
monitoring and analysis within databases pages of the controller's
UI 330). Multiple database agents can report to the same
controller. Additional database agents can be implemented as backup
database agents to take over for the primary database agents during
a failure or planned machine downtime. The additional database
agents can run on the same machine as the primary agents or on
different machines. A database agent can be deployed in each
distinct network of the monitored environment. Multiple database
agents can run under different user accounts on the same
machine.
[0038] Standalone machine agents, on the other hand, may be
standalone programs (e.g., standalone Java programs) that collect
hardware-related performance statistics from the servers (or other
suitable devices) in the monitored environment. The standalone
machine agents can be deployed on machines that host application
servers, database servers, messaging servers, Web servers, etc. A
standalone machine agent has an extensible architecture (e.g.,
designed to accommodate changes).
[0039] End user monitoring (EUM) may be performed using browser
agents and mobile agents to provide performance information from
the point of view of the client, such as a web browser or a mobile
native application. Through EUM, web use, mobile use, or
combinations thereof (e.g., by real users or synthetic agents) can
be monitored based on the monitoring needs. Notably, browser agents
(e.g., agents 310) can include Reporters that report monitored data
to the controller.
[0040] Monitoring through browser agents and mobile agents are
generally unlike monitoring through application agents, database
agents, and standalone machine agents that are on the server. In
particular, browser agents may generally be embodied as small files
using web-based technologies, such as JavaScript agents injected
into each instrumented web page (e.g., as close to the top as
possible) as the web page is served, and are configured to collect
data. Once the web page has completed loading, the collected data
may be bundled into a beacon and sent to an EUM process/cloud for
processing and made ready for retrieval by the controller. Browser
real user monitoring (Browser RUM) provides insights into the
performance of a web application from the point of view of a real
or synthetic end user. For example, Browser RUM can determine how
specific Ajax or iframe calls are slowing down page load time and
how server performance impact end user experience in aggregate or
in individual cases.
[0041] A mobile agent, on the other hand, may be a small piece of
highly performant code that gets added to the source of the mobile
application. Mobile RUM provides information on the native mobile
application (e.g., iOS or Android applications) as the end users
actually use the mobile application. Mobile RUM provides visibility
into the functioning of the mobile application itself and the
mobile application's interaction with the network used and any
server-side applications with which the mobile application
communicates.
[0042] Application Intelligence Monitoring: The disclosed
technology can provide application intelligence data by monitoring
an application environment that includes various services such as
web applications served from an application server (e.g., Java
virtual machine (JVM), Internet Information Services (IIS),
Hypertext Preprocessor (PHP) Web server, etc.), databases or other
data stores, and remote services such as message queues and caches.
The services in the application environment can interact in various
ways to provide a set of cohesive user interactions with the
application, such as a set of user services applicable to end user
customers.
[0043] Application Intelligence Modeling: Entities in the
application environment (such as the JBoss service, MQSeries
modules, and databases) and the services provided by the entities
(such as a login transaction, service or product search, or
purchase transaction) may be mapped to an application intelligence
model. In the application intelligence model, a business
transaction represents a particular service provided by the
monitored environment. For example, in an e-commerce application,
particular real-world services can include a user logging in,
searching for items, or adding items to the cart. In a content
portal, particular real-world services can include user requests
for content such as sports, business, or entertainment news. In a
stock trading application, particular real-world services can
include operations such as receiving a stock quote, buying, or
selling stocks.
[0044] Business Transactions: A business transaction representation
of the particular service provided by the monitored environment
provides a view on performance data in the context of the various
tiers that participate in processing a particular request. A
business transaction, which may each be identified by a unique
business transaction identification (ID), represents the end-to-end
processing path used to fulfill a service request in the monitored
environment (e.g., adding items to a shopping cart, storing
information in a database, purchasing an item online, etc.). Thus,
a business transaction is a type of user-initiated action in the
monitored environment defined by an entry point and a processing
path across application servers, databases, and potentially many
other infrastructure components. Each instance of a business
transaction is an execution of that transaction in response to a
particular user request (e.g., a socket call, illustratively
associated with the TCP layer). A business transaction can be
created by detecting incoming requests at an entry point and
tracking the activity associated with request at the originating
tier and across distributed components in the application
environment (e.g., associating the business transaction with a
4-tuple of a source IP address, source port, destination IP
address, and destination port). A flow map can be generated for a
business transaction that shows the touch points for the business
transaction in the application environment. In one embodiment, a
specific tag may be added to packets by application specific agents
for identifying business transactions (e.g., a custom header field
attached to a hypertext transfer protocol (HTTP) payload by an
application agent, or by a network agent when an application makes
a remote socket call), such that packets can be examined by network
agents to identify the business transaction identifier (ID) (e.g.,
a Globally Unique Identifier (GUID) or Universally Unique
Identifier (UUID)).
[0045] Performance monitoring can be oriented by business
transaction to focus on the performance of the services in the
application environment from the perspective of end users.
Performance monitoring based on business transactions can provide
information on whether a service is available (e.g., users can log
in, check out, or view their data), response times for users, and
the cause of problems when the problems occur.
[0046] A business application is the top-level container in the
application intelligence model. A business application contains a
set of related services and business transactions. In some
implementations, a single business application may be needed to
model the environment. In some implementations, the application
intelligence model of the application environment can be divided
into several business applications. Business applications can be
organized differently based on the specifics of the application
environment. One consideration is to organize the business
applications in a way that reflects work teams in a particular
organization, since role-based access controls in the Controller UI
are oriented by business application.
[0047] A node in the application intelligence model corresponds to
a monitored server or JVM in the application environment. A node is
the smallest unit of the modeled environment. In general, a node
corresponds to an individual application server, JVM, or Common
Language Runtime (CLR) on which a monitoring Agent is installed.
Each node identifies itself in the application intelligence model.
The Agent installed at the node is configured to specify the name
of the node, tier, and business application under which the Agent
reports data to the Controller.
[0048] Business applications contain tiers, the unit in the
application intelligence model that includes one or more nodes.
Each node represents an instrumented service (such as a web
application). While a node can be a distinct application in the
application environment, in the application intelligence model, a
node is a member of a tier, which, along with possibly many other
tiers, make up the overall logical business application.
[0049] Tiers can be organized in the application intelligence model
depending on a mental model of the monitored application
environment. For example, identical nodes can be grouped into a
single tier (such as a cluster of redundant servers). In some
implementations, any set of nodes, identical or not, can be grouped
for the purpose of treating certain performance metrics as a unit
into a single tier.
[0050] The traffic in a business application flows among tiers and
can be visualized in a flow map using lines among tiers. In
addition, the lines indicating the traffic flows among tiers can be
annotated with performance metrics. In the application intelligence
model, there may not be any interaction among nodes within a single
tier. Also, in some implementations, an application agent node
cannot belong to more than one tier. Similarly, a machine agent
cannot belong to more than one tier. However, more than one machine
agent can be installed on a machine.
[0051] A backend is a component that participates in the processing
of a business transaction instance. A backend is not instrumented
by an agent. A backend may be a web server, database, message
queue, or other type of service. The agent recognizes calls to
these backend services from instrumented code (called exit calls).
When a service is not instrumented and cannot continue the
transaction context of the call, the agent determines that the
service is a backend component. The agent picks up the transaction
context at the response at the backend and continues to follow the
context of the transaction from there.
[0052] Performance information is available for the backend call.
For detailed transaction analysis for the leg of a transaction
processed by the backend, the database, web service, or other
application need to be instrumented.
[0053] The application intelligence platform uses both self-learned
baselines and configurable thresholds to help identify application
issues. A complex distributed application has a large number of
performance metrics and each metric is important in one or more
contexts. In such environments, it is difficult to determine the
values or ranges that are normal for a particular metric; set
meaningful thresholds on which to base and receive relevant alerts;
and determine what is a "normal" metric when the application or
infrastructure undergoes change. For these reasons, the disclosed
application intelligence platform can perform anomaly detection
based on dynamic baselines or thresholds.
[0054] The disclosed application intelligence platform
automatically calculates dynamic baselines for the monitored
metrics, defining what is "normal" for each metric based on actual
usage. The application intelligence platform uses these baselines
to identify subsequent metrics whose values fall out of this normal
range. Static thresholds that are tedious to set up and, in rapidly
changing application environments, error-prone, are no longer
needed.
[0055] The disclosed application intelligence platform can use
configurable thresholds to maintain service level agreements (SLAs)
and ensure optimum performance levels for system by detecting slow,
very slow, and stalled transactions. Configurable thresholds
provide a flexible way to associate the right business context with
a slow request to isolate the root cause.
[0056] In addition, health rules can be set up with conditions that
use the dynamically generated baselines to trigger alerts or
initiate other types of remedial actions when performance problems
are occurring or may be about to occur.
[0057] For example, dynamic baselines can be used to automatically
establish what is considered normal behavior for a particular
application. Policies and health rules can be used against
baselines or other health indicators for a particular application
to detect and troubleshoot problems before users are affected.
Health rules can be used to define metric conditions to monitor,
such as when the "average response time is four times slower than
the baseline". The health rules can be created and modified based
on the monitored application environment.
[0058] Examples of health rules for testing business transaction
performance can include business transaction response time and
business transaction error rate. For example, health rule that
tests whether the business transaction response time is much higher
than normal can define a critical condition as the combination of
an average response time greater than the default baseline by 3
standard deviations and a load greater than 50 calls per minute. In
some implementations, this health rule can define a warning
condition as the combination of an average response time greater
than the default baseline by 2 standard deviations and a load
greater than 100 calls per minute. In some implementations, the
health rule that tests whether the business transaction error rate
is much higher than normal can define a critical condition as the
combination of an error rate greater than the default baseline by 3
standard deviations and an error rate greater than 10 errors per
minute and a load greater than 50 calls per minute. In some
implementations, this health rule can define a warning condition as
the combination of an error rate greater than the default baseline
by 2 standard deviations and an error rate greater than 5 errors
per minute and a load greater than 50 calls per minute. These are
non-exhaustive and non-limiting examples of health rules and other
health rules can be defined as desired by the user.
[0059] Policies can be configured to trigger actions when a health
rule is violated or when any event occurs. Triggered actions can
include notifications, diagnostic actions, auto-scaling capacity,
running remediation scripts.
[0060] Most of the metrics relate to the overall performance of the
application or business transaction (e.g., load, average response
time, error rate, etc.) or of the application server infrastructure
(e.g., percentage CPU busy, percentage of memory used, etc.). The
Metric Browser in the controller UI can be used to view all of the
metrics that the agents report to the controller.
[0061] In addition, special metrics called information points can
be created to report on how a given business (as opposed to a given
application) is performing. For example, the performance of the
total revenue for a certain product or set of products can be
monitored. Also, information points can be used to report on how a
given code is performing, for example how many times a specific
method is called and how long it is taking to execute. Moreover,
extensions that use the machine agent can be created to report user
defined custom metrics. These custom metrics are base-lined and
reported in the controller, just like the built-in metrics.
[0062] All metrics can be accessed programmatically using a
Representational State Transfer (REST) API that returns either the
JavaScript Object Notation (JSON) or the eXtensible Markup Language
(XML) format. Also, the REST API can be used to query and
manipulate the application environment.
[0063] Snapshots provide a detailed picture of a given application
at a certain point in time. Snapshots usually include call graphs
that allow that enables drilling down to the line of code that may
be causing performance problems. The most common snapshots are
transaction snapshots.
[0064] FIG. 4 illustrates an example application intelligence
platform (system) 400 for performing one or more aspects of the
techniques herein. The system 400 in FIG. 4 includes client device
405 and 492, mobile device 415, network 420, network server 425,
application servers 430, 440, 450, and 460, asynchronous network
machine 470, data stores 480 and 485, controller 490, and data
collection server 495. The controller 490 can include visualization
system 496 for providing displaying of the report generated for
performing the field name recommendations for field extraction as
disclosed in the present disclosure. In some implementations, the
visualization system 496 can be implemented in a separate machine
(e.g., a server) different from the one hosting the controller
490.
[0065] Client device 405 may include network browser 410 and be
implemented as a computing device, such as for example a laptop,
desktop, workstation, or some other computing device. Network
browser 410 may be a client application for viewing content
provided by an application server, such as application server 430
via network server 425 over network 420.
[0066] Network browser 410 may include agent 412. Agent 412 may be
installed on network browser 410 and/or client 405 as a network
browser add-on, downloading the application to the server, or in
some other manner. Agent 412 may be executed to monitor network
browser 410, the operating system of client 405, and any other
application, API, or another component of client 405. Agent 412 may
determine network browser navigation timing metrics, access browser
cookies, monitor code, and transmit data to data collection 460,
controller 490, or another device. Agent 412 may perform other
operations related to monitoring a request or a network at client
405 as discussed herein including report generating.
[0067] Mobile device 415 is connected to network 420 and may be
implemented as a portable device suitable for sending and receiving
content over a network, such as for example a mobile phone, smart
phone, tablet computer, or other portable device. Both client
device 405 and mobile device 415 may include hardware and/or
software configured to access a web service provided by network
server 425.
[0068] Mobile device 415 may include network browser 417 and an
agent 419. Mobile device may also include client applications and
other code that may be monitored by agent 419. Agent 419 may reside
in and/or communicate with network browser 417, as well as
communicate with other applications, an operating system, APIs and
other hardware and software on mobile device 415. Agent 419 may
have similar functionality as that described herein for agent 412
on client 405, and may report data to data collection server 460
and/or controller 490.
[0069] Network 420 may facilitate communication of data among
different servers, devices and machines of system 400 (some
connections shown with lines to network 420, some not shown). The
network may be implemented as a private network, public network,
intranet, the Internet, a cellular network, Wi-Fi network, VoIP
network, or a combination of one or more of these networks. The
network 420 may include one or more machines such as load balance
machines and other machines.
[0070] Network server 425 is connected to network 420 and may
receive and process requests received over network 420. Network
server 425 may be implemented as one or more servers implementing a
network service, and may be implemented on the same machine as
application server 430 or one or more separate machines. When
network 420 is the Internet, network server 425 may be implemented
as a web server.
[0071] Application server 430 communicates with network server 425,
application servers 440 and 450, and controller 490. Application
server 450 may also communicate with other machines and devices
(not illustrated in FIG. 3). Application server 430 may host an
application or portions of a distributed application. The host
application 432 may be in one of many platforms, such as including
a Java, PHP, .Net, and Node.JS, be implemented as a Java virtual
machine, or include some other host type. Application server 430
may also include one or more agents 434 (i.e., "modules"),
including a language agent, machine agent, and network agent, and
other software modules. Application server 430 may be implemented
as one server or multiple servers as illustrated in FIG. 4.
[0072] Application 432 and other software on application server 430
may be instrumented using byte code insertion, or byte code
instrumentation (BCI), to modify the object code of the application
or other software. The instrumented object code may include code
used to detect calls received by application 432, calls sent by
application 432, and communicate with agent 434 during execution of
the application. BCI may also be used to monitor one or more
sockets of the application and/or application server in order to
monitor the socket and capture packets coming over the socket.
[0073] In some embodiments, server 430 may include applications
and/or code other than a virtual machine. For example, servers 430,
440, 450, and 460 may each include Java code, .Net code, PHP code,
Ruby code, C code, C++ or other binary code to implement
applications and process requests received from a remote source.
References to a virtual machine with respect to an application
server are intended to be for exemplary purposes only.
[0074] Agents 434 on application server 430 may be installed,
downloaded, embedded, or otherwise provided on application server
430. For example, agents 434 may be provided in server 430 by
instrumentation of object code, downloading the agents to the
server, or in some other manner. Agent 434 may be executed to
monitor application server 430, monitor code running in a virtual
machine 432 (or other program language, such as a PHP, .Net, or C
program), machine resources, network layer data, and communicate
with byte instrumented code on application server 430 and one or
more applications on application server 430.
[0075] Each of agents 434, 444, 454, and 464 may include one or
more agents, such as language agents, machine agents, and network
agents. A language agent may be a type of agent that is suitable to
run on a particular host. Examples of language agents include a
Java agent, .Net agent, PHP agent, and other agents. The machine
agent may collect data from a particular machine on which it is
installed. A network agent may capture network information, such as
data collected from a socket.
[0076] Agent 434 may detect operations such as receiving calls and
sending requests by application server 430, resource usage, and
incoming packets. Agent 434 may receive data, process the data, for
example by aggregating data into metrics, and transmit the data
and/or metrics to controller 490. Agent 434 may perform other
operations related to monitoring applications and application
server 430 as discussed herein. For example, agent 434 may identify
other applications, share business transaction data, aggregate
detected runtime data, and other operations.
[0077] An agent may operate to monitor a node, tier of nodes, or
other entity. A node may be a software program or a hardware
component (e.g., memory, processor, and so on). A tier of nodes may
include a plurality of nodes which may process a similar business
transaction, may be located on the same server, may be associated
with each other in some other way, or may not be associated with
each other.
[0078] A language agent may be an agent suitable to instrument or
modify, collect data from, and reside on a host. The host may be a
Java, PHP, .Net, Node.JS, or other type of platform. Language
agents may collect flow data as well as data associated with the
execution of a particular application. The language agent may
instrument the lowest level of the application to gather the flow
data. The flow data may indicate which tier is communicating with
which tier and on which port. In some instances, the flow data
collected from the language agent includes a source IP, a source
port, a destination IP, and a destination port. The language agent
may report the application data and call chain data to a
controller. The language agent may report the collected flow data
associated with a particular application to a network agent.
[0079] A network agent may be a standalone agent that resides on
the host and collects network flow group data. The network flow
group data may include a source IP, destination port, destination
IP, and protocol information for network flow received by an
application on which network agent is installed. The network agent
may collect data by intercepting and performing packet capture on
packets coming in from one or more network interfaces (e.g., so
that data generated/received by all the applications using sockets
can be intercepted). The network agent may receive flow data from a
language agent that is associated with applications to be
monitored. For flows in the flow group data that match flow data
provided by the language agent, the network agent rolls up the flow
data to determine metrics such as TCP throughput, TCP loss,
latency, and bandwidth. The network agent may then report the
metrics, flow group data, and call chain data to a controller. The
network agent may also make system calls at an application server
to determine system information, such as for example a host status
check, a network status check, socket status, and other
information.
[0080] A machine agent, which may be referred to as an
infrastructure agent, may reside on the host and collect
information regarding the machine which implements the host. A
machine agent may collect and generate metrics from information
such as processor usage, memory usage, and other hardware
information.
[0081] Each of the language agent, network agent, and machine agent
may report data to the controller. Controller 490 may be
implemented as a remote server that communicates with agents
located on one or more servers or machines. The controller may
receive metrics, call chain data and other data, correlate the
received data as part of a distributed transaction, and report the
correlated data in the context of a distributed application
implemented by one or more monitored applications and occurring
over one or more monitored networks. The controller may provide
reports, one or more user interfaces, and other information for a
user.
[0082] Agent 434 may create a request identifier for a request
received by server 430 (for example, a request received by a client
405 or 415 associated with a user or another source). The request
identifier may be sent to client 405 or mobile device 415,
whichever device sent the request. In embodiments, the request
identifier may be created when data is collected and analyzed for a
particular business transaction.
[0083] Each of application servers 440, 450, and 460 may include an
application and agents. Each application may run on the
corresponding application server. Each of applications 442, 452,
and 462 on application servers 440-460 may operate similarly to
application 432 and perform at least a portion of a distributed
business transaction. Agents 444, 454, and 464 may monitor
applications 442-462, collect and process data at runtime, and
communicate with controller 490. The applications 432, 442, 452,
and 462 may communicate with each other as part of performing a
distributed transaction. Each application may call any application
or method of another virtual machine.
[0084] Asynchronous network machine 470 may engage in asynchronous
communications with one or more application servers, such as
application server 450 and 460. For example, application server 450
may transmit several calls or messages to an asynchronous network
machine. Rather than communicate back to application server 450,
the asynchronous network machine may process the messages and
eventually provide a response, such as a processed message, to
application server 460. Because there is no return message from the
asynchronous network machine to application server 450, the
communications among them are asynchronous.
[0085] Data stores 480 and 485 may each be accessed by application
servers such as application server 450. Data store 485 may also be
accessed by application server 450. Each of data stores 480 and 485
may store data, process data, and return queries received from an
application server. Each of data stores 480 and 485 may or may not
include an agent.
[0086] Controller 490 may control and manage monitoring of business
transactions distributed over application servers 430-460. In some
embodiments, controller 490 may receive application data, including
data associated with monitoring client requests at client 405 and
mobile device 415, from data collection server 460. In some
embodiments, controller 490 may receive application monitoring data
and network data from each of agents 412, 419, 434, 444, and 454
(also referred to herein as "application monitoring agents").
Controller 490 may associate portions of business transaction data,
communicate with agents to configure collection of data, and
provide performance data and reporting through an interface. The
interface may be viewed as a web-based interface viewable by client
device 492, which may be a mobile device, client device, or any
other platform for viewing an interface provided by controller 490.
In some embodiments, a client device 492 may directly communicate
with controller 490 to view an interface for monitoring data.
[0087] Client device 492 may include any computing device,
including a mobile device or a client computer such as a desktop,
work station or other computing device. Client computer 492 may
communicate with controller 390 to create and view a custom
interface. In some embodiments, controller 490 provides an
interface for creating and viewing the custom interface as a
content page, e.g., a web page, which may be provided to and
rendered through a network browser application on client device
492.
[0088] Applications 432, 442, 452, and 462 may be any of several
types of applications. Examples of applications that may implement
applications 432-462 include a Java, PHP, .Net, Node.JS, and other
applications.
[0089] FIG. 5 is a block diagram of a computer system 500 for
implementing the present technology, which is a specific
implementation of device 200 of FIG. 2 above. System 500 of FIG. 5
may be implemented in the contexts of the likes of clients 405,
492, network server 425, servers 430, 440, 450, 460, a synchronous
network machine 470, and controller 490 of FIG. 4. (Note that the
specifically configured system 500 of FIG. 5 and the customized
device 200 of FIG. 2 are not meant to be mutually exclusive, and
the techniques herein may be performed by any suitably configured
computing device.)
[0090] The computing system 500 of FIG. 5 includes one or more
processors 510 and memory 520. Main memory 520 stores, in part,
instructions and data for execution by processor 510. Main memory
510 can store the executable code when in operation. The system 500
of FIG. 5 further includes a mass storage device 530, portable
storage medium drive(s) 540, output devices 550, user input devices
560, a graphics display 570, and peripheral devices 580.
[0091] The components shown in FIG. 5 are depicted as being
connected via a single bus 590. However, the components may be
connected through one or more data transport means. For example,
processor unit 510 and main memory 520 may be connected via a local
microprocessor bus, and the mass storage device 530, peripheral
device(s) 580, portable or remote storage device 540, and display
system 570 may be connected via one or more input/output (I/O)
buses.
[0092] Mass storage device 530, which may be implemented with a
magnetic disk drive or an optical disk drive, is a non-volatile
storage device for storing data and instructions for use by
processor unit 510. Mass storage device 530 can store the system
software for implementing embodiments of the present invention for
purposes of loading that software into main memory 520.
[0093] Portable storage device 540 operates in conjunction with a
portable non-volatile storage medium, such as a compact disk,
digital video disk, magnetic disk, flash storage, etc. to input and
output data and code to and from the computer system 500 of FIG. 5.
The system software for implementing embodiments of the present
invention may be stored on such a portable medium and input to the
computer system 500 via the portable storage device 540.
[0094] Input devices 560 provide a portion of a user interface.
Input devices 560 may include an alpha-numeric keypad, such as a
keyboard, for inputting alpha-numeric and other information, or a
pointing device, such as a mouse, a trackball, stylus, or cursor
direction keys. Additionally, the system 500 as shown in FIG. 5
includes output devices 550. Examples of suitable output devices
include speakers, printers, network interfaces, and monitors.
[0095] Display system 570 may include a liquid crystal display
(LCD) or other suitable display device. Display system 570 receives
textual and graphical information, and processes the information
for output to the display device.
[0096] Peripherals 580 may include any type of computer support
device to add additional functionality to the computer system. For
example, peripheral device(s) 580 may include a modem or a
router.
[0097] The components contained in the computer system 500 of FIG.
5 can include a personal computer, hand held computing device,
telephone, mobile computing device, workstation, server,
minicomputer, mainframe computer, or any other computing device.
The computer can also include different bus configurations,
networked platforms, multi-processor platforms, etc. Various
operating systems can be used including Unix, Linux, Windows, Apple
OS, and other suitable operating systems, including mobile
versions.
[0098] When implementing a mobile device such as smart phone or
tablet computer, the computer system 500 of FIG. 5 may include one
or more antennas, radios, and other circuitry for communicating
over wireless signals, such as for example communication using
Wi-Fi, cellular, or other wireless signals.
[0099] ----Breaking Down Load Time of Web Pages into Coherent
Components ----
[0100] As mentioned above, the performance of a webpage is
influenced by multiple architectural components, often owned by
different teams within a customer organization. Identifying which
component is the bottleneck is a key step in performance
optimization/troubleshooting, because it narrows the investigation
greatly and allows customers to route problems to the right team. A
traditional approach is to divide the system into "frontend",
"backend", and "network" components, but these categories have no
discernable meaning, nor do they currently have straightforward
manners to be measured.
[0101] To understand the problem better, FIG. 6 illustrates the
anatomy of a page load 600, namely the primary components involved
in loading a typical, modern, complex webpage loaded by an end user
610, using various third-parties 620, for a website as controlled
by the site owner 630, as will be understood by those skilled in
the art. Notably, FIG. 6 includes an indication as to whether the
specific components are controlled by the site owner 630, or where
the site owner has strong, weak, or no influence over the
components. In particular, from an end user perspective 610,
there's the client machine and operating system (OS) 611, the
browser 612 (e.g., Google Chrome, Mozilla Firefox, Microsoft Edge,
etc.), and the base HTML 613. The HTML 613 generally has control
over loading static resources (first-party or "1p") 614, dynamic
resources (1p) 615, static resources (CDN) 616, third-party ("3p")
content 617, and javascript (JS), style sheets (CSS), HTML, and
other executables 618.
[0102] From the end user's local network (IP infrastructure) 641
and into the core network infrastructure 642, various third-parties
620 may be utilized, such as DNS resolvers 621, name servers 622
(storing domains 623 owned by the site owner), as well as
certificate authorities 625 (storing certificates 625 owned by the
site owner). In addition, various third-party content providers 626
may operate based on content/configuration 627 provided by the site
owner, and CDNs 628 also operate based on content/configuration 629
provided by the site owner.
[0103] The core IP infrastructure then egresses (643) into the site
owner's network 644, where name server 631 may exist (to
communicate with DNS resolvers 621), as well as various middleware
632 (e.g., reverse proxies, load balancers, TLS termination, etc.).
The ultimate website page may then be managed by the site owner
according to app code 633 on app servers 644, on web servers 645,
which operate on various server machines/operating systems.
[0104] Some helpful insights that can be drawn from FIG. 6 above
may include the following: [0105] The components are highly
interconnected, so it is far from obvious how one should attribute
page load time to categories like "frontend", "backend", and
"network", even if there was an ability to measure everything from
everywhere. For example, loading a dynamic resource (like an AJAX
request or "XMLHttpRequest" (XHR)) is initiated by the frontend,
mediated by the network, and served by a backend server. To which
category should it be attributed? [0106] There are several parts
that could be called "Network": the TCP/IP stacks on client and
server, and the IP, DNS, and PKI systems, which are largely public.
The customer has varying degrees of influence over these. [0107]
There are quite a few parts of the system that the site owner has
very limited influence over. In certain embodiments, therefore,
these components could be left out of performance analysis for
certain site owners, or else these components could be categorized
into a bucket of components that the site owner is unable to
influence.
[0108] For at least these reasons, it is very difficult to identify
if one or more components in the anatomy of a page load are
performance bottlenecks. In particular, though many real-user
monitoring (RUM) products provide some kind of breakdown, such as
based on time segments (e.g., navigation timing), or else based on
the "Frontend", "Backend", "Network", but these methods are
questionable, and do not result in actionable categories that can
be routed to teams.
[0109] The techniques herein, therefore, isolate actionable parts
of the architectural tech stack (such as Core Frontend, Core
Backend, Core Network, Third Party and Content Deliver Network
(CDN)) as bottlenecks behind a modern complex web page load.
[0110] Specifically, according to one or more embodiments of the
disclosure as described in detail below, a monitoring process
(e.g., 248) monitors timing of navigations and resources of a web
page load, and attributes timing of the navigations and resources
into corresponding timing components. In particular, the monitoring
process may attribute timing of static first-party resources and
timing of hypertext transfer protocol (HTTP) redirects to a core
frontend timing component, and may attribute timing of dynamic
first-party resources to a core backend timing component. The
monitoring process may also attribute timing of content delivery
network (CDN) resources to a CDN timing component, and may
attribute timing of non-CDN third-party resources to a third-party
timing component. Lastly, the monitoring process may further
attribute timing of network infrastructure connections to a core
connections timing component.
[0111] Accordingly, the monitoring process may then perform one or
more actions based on the attributing steps (i.e., the attributions
of timing to respective timing components).
[0112] Notably, many challenges are presented in identifying
performance bottlenecks within the page load system of FIG. 6,
since the components are highly interconnected, and degree of
control/influence varies among the components. When considering
those components that the site owner 630 can actually influence,
however, there are several, as follows: [0113] Frontend and backend
code and resources can be changed, and are typically owned by
development teams; [0114] Server hardware, OS, web server, and app
server, as well as middleware components like reverse proxies, can
be tuned or configured and are typically owned by app operations
teams; [0115] Site-owner-side network infrastructure like IP and
name servers can be changed, and are typically owned by network
operations teams; [0116] Third Party Content, such as ads,
trackers, and social widgets, are very common--site owners
typically have a business relationship with these vendors and can
influence them (e.g., by reconfiguring them or complaining about
service level agreement (SLA) violations); and [0117] Content
Delivery Networks (CDNs) are a very common special case of a third
party, where the content itself is provided by the customer but
hosted by the CDN (again, site owners can reconfigure the CDN and
complain about SLA violations).
[0118] These observations form the basis for many aspects of the
techniques herein, and how the page load time can be broken down.
For instance, some of the page load time is spent executing the
HTML, CSS, and JS code, which should be considered part of
"frontend" category. CDN and third-party resources should each have
their own categories, and for these resources, there is no need to
categorize the time into either network or backend categories
because the vendor is responsible for both. First-party resources
are triggered by frontend code but processed by backend servers, so
it's not completely clear how to attribute them. Also, although
"the network" is highly ambiguous, there are some cases where the
site owner can and should act, such as, for instance, if the cause
of slowness is in network infrastructure they own, or in the way
they've configured third-party infrastructure like the DNS. These
types of network resources or navigations should also have their
own category, optionally extricated from the non-influenceable
parts of the network.
[0119] FIG. 7 illustrates a single resource load timeline 700,
where a web browser 710 starts and establishes a connection through
network 720 to some server 730. After sending a request, the server
can process and respond to the request with the resource. As can be
seen in an "ideal classification" 740, there are a few points where
only one component {frontend (FE), backend (BE), or network (N)} is
active, so a monitoring process could unambiguously attribute the
time to that component. However, there are large blocks ("1" and
"2") where multiple components are active, so it remains ambiguous.
To further complicate the issue, in practice one can only use
measurements from the browser, which are obtained from Navigation
Timing and Resource Timing APIs. This expands the region of
ambiguity in actual classification 750 to include almost the entire
request (block "3").
[0120] In practice, only the initial connect time can be attributed
to the "network" (and optionally broken down into DNS, TCP, SSL).
For the remainder of the request, the network processing becomes
generally entangled with frontend and backend processing.
[0121] To properly classify first-party resources, since there's
currently no way to untangle the timing, the techniques herein take
a pragmatic approach, attributing time to the category where the
root-cause of slowness is most likely to lie. For instance, for
static resources like CSS files, JS files, images, etc., the most
common optimizations come from changes to the frontend code and the
resources themselves: e.g., minify, combine, compress, or eliminate
the resources. In particular, it is relatively rare in the art to
fix a slow CSS download by optimizing the site owner's servers. As
such, it would be pragmatic to count static resources as frontend
time.
[0122] Conversely, dynamic resources, such as AJAX requests or
XHRs, often do trigger some expensive backend code such as database
(DB) queries, and the most common optimizations take place there.
Moreover, an acute slowdown for a dynamic resource is almost
certainly due to a problem in the backend. Accordingly, it is
pragmatic to count the dynamic resources as backend time.
[0123] Notably, for both types of first-party resources (static and
dynamic), they may be preceded by some HTTP redirects. Redirects
may be optimized by using the right URL to begin with, which is
generally a frontend fix, and as such, it is pragmatic to count
HTTP redirects as frontend time.
[0124] In order to determine which resources are first-party, CDN,
or third-party, the techniques herein may be based on certain
manual configuration overrides, but generally acceptable behavior
can be achieved by using IP address blocks, documented by the CDNs
themselves, for CDNs, and DNS lookups to identify first-party
resources. Anything that is neither CDN nor first-party can then be
classified as third-party (e.g., a catch-all). Other techniques may
be used, such as using ad block and social media registries to
identify third-party resources. (Note also that it is better to use
IP addresses than domains to detect CDNs, as many companies use
vanity domains for their CDN content.)
[0125] The result of the analysis above is that the following
breakdown of Timing Components are defined, into which the various
resources may be attributed: [0126] Core Frontend; [0127] Core
Backend; [0128] Core Connections; [0129] CDN; and [0130]
Third-party.
[0131] Now that the Time Components have been broken down into
their most useful classifications, the techniques herein may
provide further information to fully allow for grokking the
concepts of concurrency. In particular, the techniques herein can
now attribute portions of the page load time to the above Timing
Components. If resource loads were serial, this would be easy: just
add up all the time spent in each category. However, as shown in
FIG. 8, an example of a typical waterfall (time chart) 800 of a
page load shows that many resources 810 are being loaded at any
given time (overlapping load windows 820).
[0132] One approach herein is to declare that when multiple Timing
Components are active at the same time (i.e., are concurrent), then
the techniques herein may prorate that time-slice across the Timing
Components. FIG. 9 illustrates an example depiction 900 of this
concept and proposed algorithm. Namely, the techniques herein may
divide the page load time into discrete spans 910 (910a-g) that
start at the beginning of each resource load (start time 915 for
resources 920, e.g., "1" through "4"), and end at the completion
(end time 925) of each resource load. For each span, the techniques
herein may illustratively: [0133] i) Count the number of resources
in each Timing Component 930 active during that span; [0134] ii)
Distribute the time of the span to each Timing Component, using the
number of resources in that Timing Component as the weighting
factor (e.g., as illustrated generally by the thicknesses of the
associated connecting lines 940); and [0135] iii) If there is a
span where nothing is happening (e.g., 910f), then attribute it to
the Core Frontend code processing Timing Component. This algorithm
results in values for each Timing Component that add up to the
total load time, and also "penalizes" categories that stand out as
long poles, while benefiting categories that do their work
concurrently, which is desirable because concurrency does indeed
reduce load times in general.
[0136] FIGS. 10A-10B illustrate an example chart 1000 (that refers
to the Navigation Timing and Resource Timing APIs), delineating the
time components (names) 1010 and their definition 1020, as well as
their general actionable root causes 1030 and any
limitations/conflating factors 1040. As shown, for example, core
connections 1011 may be defined to start at dnsStart and ends at
connectEnd for all first-party resources. If there are any issues
here, then actionable root causes may be that the DNS is
misconfigured, that SSL is misconfigured, and so on. As also noted,
latency or limited bandwidth may conflate the issue, as would
client machine saturation, a public DNS problem, a PKI problem, and
so on. Similar example correlations are further demonstrated in the
chart 1000 for each of the time components (core backend 1012, core
frontend 1013, CDN 1014, and third-party 1015), the contents of
which being incorporated herein.
[0137] FIG. 11 illustrates an example simplified procedure for
breaking down the load time of a web page into coherent components
in accordance with one or more embodiments described herein. For
example, a non-generic, specifically configured device (e.g.,
device 200) may perform procedure 1100 by executing stored
instructions (e.g., process 248). The procedure 1100 may start at
step 1105, and continues to step 1110, where, as described in
greater detail above, a monitoring process monitors timing of
navigations and resources of a web page load. As described above,
the monitoring process may identify the resources and navigations
in a number of ways, such as, for example: [0138] determining CDN
resources based on a corresponding IP address being located within
IP address blocks for one or more known CDNs; [0139] determining
first-party resources based on a DNS lookup; [0140] determining
third-party resources based on resources being neither CDN
resources nor first-party resources; and [0141] determining network
infrastructure connections as one or more of a DNS connection, a
TCP connection, and an SSL connection (notably, network
infrastructure connections may be limited to only connections under
at least partial first-party control, as mentioned above).
[0142] In the following steps (1115 through 1135), the monitoring
process attributes navigations and resources to corresponding
timing components as follows: [0143] Step 1115: attributing timing
of static first-party resources and timing of HTTP redirects to a
core frontend timing component (e.g., where static first-party
resources comprise one or more of stylesheets, javascript files, or
images, etc.); [0144] Step 1120: attributing timing of dynamic
first-party resources to a core backend timing component (e.g.,
where dynamic first-party resources comprise one or more of Ajax
requests or XHRs); [0145] Step 1125: attributing timing of CDN
resources to a CDN timing component; [0146] Step 1130: attributing
timing of non-CDN third-party resources to a third-party timing
component; and [0147] Step 1135: attributing timing of network
infrastructure connections to a core connections timing component
(notably optionally distinguishing DNS, TCP, and SSL connections as
sub-components of the core connections timing component, as
mentioned above).
[0148] In one embodiment herein, in step 1140 the monitoring
process may divide the timing of the web page load into discrete
spans (e.g., as described above in FIG. 9), with each span
beginning at a corresponding start of either a navigation or a
resource. In response to a plurality of particular timing
components being concurrently active during any particular span,
the monitoring process may prorate that particular span across the
plurality of particular timing components (e.g., distributing a
timing of that particular span to each of the plurality of
particular timing components based on a number of resources and
navigations in each respective timing component of the plurality of
particular timing components, as detailed above). Note that as also
described above, the monitoring process may further attribute any
idle time spans to the core frontend timing component.
[0149] In step 1145, the monitoring process may perform one or more
actions based on the attributing steps above (i.e., the
attributions of timing to respective timing components). For
example, the monitoring process may display or otherwise report the
attributions of timing to respective timing components, or else one
or more alarms may be triggered based on the attributions of timing
to respective timing components (e.g., certain timing components
being over a certain length of time or a certain percentage of
overall time, etc.). Other actions, such as detecting and
mitigating anomalies based on the attributions of timing to
respective timing components, may also be performed herein, such as
comparing current values to baseline values or other running
averages in order to detect unexpected outliers.
[0150] The simplified procedure 1100 may then end in step 1150,
notably with the ability to continue monitoring new page loads
(initial page loads or virtual page loads, such as for single page
applications (SPAs), etc.).
[0151] Notably, with regard to virtual page loads (e.g., single
page app (SPA) virtual pages), by correctly determining which
resources are triggered by a particular virtual page load, then the
techniques herein as described above also apply to virtual pages
(except that they do not have base pages, as may be appreciated by
those skilled in the art).
[0152] It should be noted that while certain steps within procedure
1100 may be optional as described above, the steps shown in FIG. 11
are merely examples for illustration, and certain other steps may
be included or excluded as desired. Further, while a particular
order of the steps is shown, this ordering is merely illustrative,
and any suitable arrangement of the steps may be utilized without
departing from the scope of the embodiments herein.
[0153] The techniques described herein, therefore, break down the
load time of a web page into coherent components. In particular, as
described above, the techniques herein help isolate actionable
parts of the architectural tech stack (e.g., Core Frontend, Core
Backend, Core Network, Third Party and CDN) as bottlenecks behind a
modern complex web page load. Previous attempts to break down the
page load time have been focused on providing detailed numbers, but
the techniques herein provide further information to interpret the
numbers. That is, the techniques herein propose both a
categorization approach to make the data actionable, as well as a
way to calculate the breakdown between categories. Notably, while
the techniques herein can be based on existing metrics provided by
the browser, they solve the concurrent loading problem that has
generally been an obstacle when one attempts to break down the
provided metrics.
[0154] In addition, the techniques herein use both navigation
timing and resource timing to break down page load time into the
five categories that are the most useful breakdown. For instance,
separating CDN and third-party resources is useful in routing
problems to the right team (or, in this case, 3rd-party
organization). Also, since Core Connection Time is computed by
measuring only the time taken to establish network connections to
first-party HTTP servers, this is better than including third-party
servers because it will be correlated with problems in the site
owner's network, which indeed are the actionable ones, so as to not
cause false alarms when there is a problem with a third-party's
network. Further, Core Backend Time may be computed by measuring
the time taken to load resources that are dynamic and therefore
likely to be bottlenecked at the server, which is better than just
measuring the time-to-first-byte, because that omits dynamic
resources like dynamic requests (e.g., AJAX) which are increasingly
common and having an increasing impact on web page performance.
Still further, Core Frontend Time may be computed by measuring the
time taken to load resources that are static and therefore likely
to be optimized by changing frontend code, which is better than
just measuring the time from first-byte to page-load, because that
includes AJAX requests which are more likely to be backend-bound.
Lastly, since many of these resources load concurrently, assigning
page-wide timing numbers would be ambiguous (i.e., what if two
resources were loading at the same time, from different
categories?), and the techniques herein, therefore, define an
algorithm to perform attribution during concurrency as well (e.g.,
as shown in FIG. 9).
[0155] In still further embodiments of the techniques herein, a
business impact of the load time of a web page can also be
quantified. That is, because of issues related to specific
applications/processes (e.g., lost traffic, slower servers,
overloaded network links, etc.), various corresponding business
transactions may have been correspondingly affected for those
applications/processes (e.g., online purchases were delayed, page
visits were halted before fully loading, user satisfaction or dwell
time decreased, etc.), while other processes (e.g., on other
network segments or at other times) remain unaffected. The
techniques herein, therefore, can correlate the load time of a web
page with various business transactions in order to better
understand the affect the load times may have had on the business
transactions, accordingly.
[0156] Illustratively, the techniques described herein may be
performed by hardware, software, and/or firmware, such as in
accordance with the illustrative Page Load Monitoring process 248,
which may include computer executable instructions executed by the
processor 220 to perform functions relating to the techniques
described herein, e.g., in conjunction with corresponding processes
of other devices in the computer network as described herein (e.g.,
on network agents, controllers, computing devices, servers,
etc.).
[0157] While there have been shown and described illustrative
embodiments above, it is to be understood that various other
adaptations and modifications may be made within the scope of the
embodiments herein. For example, while certain embodiments are
described herein with respect to certain types of networks in
particular, the techniques are not limited as such and may be used
with any computer network, generally, in other embodiments.
Moreover, while specific technologies, protocols, and associated
devices have been shown, such as Java, TCP, IP, and so on, other
suitable technologies, protocols, and associated devices may be
used in accordance with the techniques described above. In
addition, while certain devices are shown, and with certain
functionality being performed on certain devices, other suitable
devices and process locations may be used, accordingly. That is,
the embodiments have been shown and described herein with relation
to specific network configurations (orientations, topologies,
protocols, terminology, processing locations, etc.). However, the
embodiments in their broader sense are not as limited, and may, in
fact, be used with other types of networks, protocols, and
configurations.
[0158] Moreover, while the present disclosure contains many other
specifics, these should not be construed as limitations on the
scope of any invention or of what may be claimed, but rather as
descriptions of features that may be specific to particular
embodiments of particular inventions. Certain features that are
described in this document in the context of separate embodiments
can also be implemented in combination in a single embodiment.
Conversely, various features that are described in the context of a
single embodiment can also be implemented in multiple embodiments
separately or in any suitable sub-combination. Further, although
features may be described above as acting in certain combinations
and even initially claimed as such, one or more features from a
claimed combination can in some cases be excised from the
combination, and the claimed combination may be directed to a
sub-combination or variation of a sub-combination.
[0159] For instance, while certain aspects of the present
disclosure are described in terms of being performed "by a server"
or "by a controller", those skilled in the art will appreciate that
agents of the application intelligence platform (e.g., application
agents, network agents, language agents, etc.) may be considered to
be extensions of the server (or controller) operation, and as such,
any process step performed "by a server" need not be limited to
local processing on a specific server device, unless otherwise
specifically noted as such. Furthermore, while certain aspects are
described as being performed "by an agent" or by particular types
of agents (e.g., application agents, network agents, etc.), the
techniques may be generally applied to any suitable
software/hardware configuration (libraries, modules, etc.) as part
of an apparatus or otherwise.
[0160] Similarly, while operations are depicted in the drawings in
a particular order, this should not be understood as requiring that
such operations be performed in the particular order shown or in
sequential order, or that all illustrated operations be performed,
to achieve desirable results. Moreover, the separation of various
system components in the embodiments described in the present
disclosure should not be understood as requiring such separation in
all embodiments.
[0161] The foregoing description has been directed to specific
embodiments. It will be apparent, however, that other variations
and modifications may be made to the described embodiments, with
the attainment of some or all of their advantages. For instance, it
is expressly contemplated that the components and/or elements
described herein can be implemented as software being stored on a
tangible (non-transitory) computer-readable medium (e.g.,
disks/CDs/RAM/EEPROM/etc.) having program instructions executing on
a computer, hardware, firmware, or a combination thereof.
Accordingly, this description is to be taken only by way of example
and not to otherwise limit the scope of the embodiments herein.
Therefore, it is the object of the appended claims to cover all
such variations and modifications as come within the true intent
and scope of the embodiments herein.
* * * * *