U.S. patent application number 14/815211 was filed with the patent office on 2017-02-02 for application centric network experience monitoring.
The applicant listed for this patent is AppDynamics, Inc.. Invention is credited to Jyoti Bansal, Ajay Chandel, Adam Leftik, Harish Nataraj, Bhaskar Sunkara.
Application Number | 20170034019 14/815211 |
Document ID | / |
Family ID | 57886653 |
Filed Date | 2017-02-02 |
United States Patent
Application |
20170034019 |
Kind Code |
A1 |
Nataraj; Harish ; et
al. |
February 2, 2017 |
APPLICATION CENTRIC NETWORK EXPERIENCE MONITORING
Abstract
A system determines the performance of a network within the
context of an application using that network. Network data is
collected and correlated with an application that uses the network
as well as a distributed transaction implemented by the
application. The collected network data is culled, and the
remaining data is rolled up into one or more metrics. The metrics,
selected network data, and other data are reported in the context
of the application that implements part of the distributed
transaction. In this manner, specific network performance and
architecture data is reported along with application context
information.
Inventors: |
Nataraj; Harish; (Berkeley,
CA) ; Leftik; Adam; (San Francisco, CA) ;
Chandel; Ajay; (Fremont, CA) ; Bansal; Jyoti;
(San Francisco, CA) ; Sunkara; Bhaskar; (San
Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
AppDynamics, Inc. |
San Francisco |
CA |
US |
|
|
Family ID: |
57886653 |
Appl. No.: |
14/815211 |
Filed: |
July 31, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 11/3495 20130101;
H04W 24/10 20130101; H04L 41/5058 20130101; G06F 11/3419 20130101;
H04L 41/142 20130101; H04L 41/5035 20130101; G06F 2201/865
20130101; H04L 43/062 20130101; G06F 11/3006 20130101; H04W 36/30
20130101; H04L 67/42 20130101; H04L 43/16 20130101; H04L 41/5009
20130101; G06F 11/3072 20130101 |
International
Class: |
H04L 12/26 20060101
H04L012/26 |
Claims
1. A method for correlating application performance data and
network performance data for a distributed transaction, comprising:
collecting application data by a first module installed on a first
machine, the application data collected during execution of an
application, the application one of a plurality of applications on
one or more machines that implement a distributed transaction over
a network; receiving, by a second module installed on the first
machine, the collected application data; collecting network data
for the network by the second module installed on the first
machine; identifying, by the second module, a subset of the
collected network data including network flow data corresponding to
the application data and collected during execution of the
application while implementing a portion of the distributed
transaction over the network; correlating the application data and
the identified subset of the network data using distributed
transaction information that includes call chain data, the
correlated application data and the identified subset of the
network data indicating performance of the network within a context
of the application during execution; and reporting the correlated
application data and the network indicating the performance of the
network within the context of the application during execution from
a remote server.
2. The method of claim 1, the network data including network
infrastructure data.
3. The method of claim 1, wherein the first module is a first agent
installed on the first machine and the second module is a second
agent installed on the first machine.
4. The method of claim 1, wherein the first module is a first agent
installed on the first machine and the second module is a plug-in
installed in the first agent on the first machine.
5. The method of claim 1, including: collecting, by the first
module, the distributed transaction information from the
application being monitored; and providing, by the first module,
the distributed transaction information to the second module.
6. The method of claim 4, wherein the call chain data includes a
sequence of one or more nodes that have previously processed the
distributed business transaction.
7. The method of claim 1, including: collecting, by the first
module, a network flow tuple for the application; and providing, by
the first module, the distributed transaction information and the
network flow tuple to the second module.
8. The method of claim 6, including: receiving, by the second
module, the network flow tuple and distributed transaction
information; generating, by the second module, metrics for network
flow group data that matches the received network flow tuple; and
reporting the network flow group metrics and the distributed
transaction information to a remote server.
9. The method of claim 1, wherein correlating includes: receiving
application performance metrics generated from the application data
collected by the first module; receiving network performance
metrics generated from the network data collected by the second
module; and correlating the application performance metrics and
network performance metrics using the call chain data that indicate
a sequence of machines that have previously processed the
distributed transaction are associated with each of the application
performance metrics and network performance metrics.
10. A non-transitory computer readable storage medium having
embodied thereon a program, the program being executable by a
processor to perform a method for correlating application
performance data and network performance data for a distributed
transaction, the method comprising: collecting application data by
a first module installed on a first machine, the application data
collected during execution of an application, the application one
of a plurality of applications on one or more machines that
implement a distributed transaction over a network; receiving, by a
second module installed on the first machine, the collected
application data; collecting network data for the network by the
second module installed on the first machine; identifying, by the
second module, a subset of the collected network data including
network flow data corresponding to the application data and
collected during execution of the application while implementing a
portion of the distributed transaction over the network;
correlating the application data and the identified subset of the
network data using distributed transaction information that
includes call chain data, the correlated application data and the
identified subset of the network data indicating performance of the
network within a context of the application during execution; and
reporting the correlated application data and the network
indicating the performance of the network within the context of the
application during execution from a remote server.
11. The non-transitory computer readable storage medium of claim
10, the network data including network infrastructure data.
12. The non-transitory computer readable storage medium of claim
10, wherein the first module is a first agent installed on the
first machine and the second module is a second agent installed on
the first machine.
13. The non-transitory computer readable storage medium of claim
10, wherein the first module is a first agent installed on the
first machine and the second module is a plug-in installed in the
first agent on the first machine.
14. The non-transitory computer readable storage medium of claim
10, wherein the first module collecting the distributed transaction
information from the application being monitored by the first
module, the first module providing the second module with the
distributed transaction information.
15. The non-transitory computer readable storage medium of claim
14, wherein the call chain data includes a sequence of one or more
nodes that have previously processed the distributed business
transaction.
16. The non-transitory computer readable storage medium of claim
10, wherein the first module collecting a network flow tuple for
the application, the first module providing the distributed
transaction information and the network flow tuple to the second
module.
17. The non-transitory computer readable storage medium of claim
16, the second module receiving the network flow tuple and
distributed transaction information, the second module generating
metrics for network flow group data that matches the received
network flow tuple and reporting the network flow group metrics and
distributed transaction information to a remote server.
18. The non-transitory computer readable storage medium of claim
10, wherein correlating includes: receiving application performance
metrics generated from the application data collected by the first
module; receiving network performance metrics generated from the
network data collected by the second module; and correlating the
application performance metrics and network performance metrics
using the call chain data that indicate a sequence of machines that
have previously processed the distributed transaction are
associated with each of the application performance metrics and
network performance metrics.
19. A system for correlating application performance data and
network performance data for a distributed transaction, comprising:
a server including a memory and a processor; and one or more
modules stored in the memory and executed by the processor to
perform operations including: collect application data by a first
module installed on a first machine, the application data collected
during execution of an application, the application one of a
plurality of applications on one or more machines that implement a
distributed transaction over a network; receive, by a second module
installed on the first machine, the collected application data;
collect network data for the network by the second module installed
on the first machine; identify, by the second module, a subset of
the collected network data including network flow data
corresponding to the application data and collected during
execution of the application while implementing a portion of the
distributed transaction over the network; correlate the application
data and the identified subset of the network data using
distributed transaction information that includes call chain data,
the correlated application data and the identified subset of the
network data indicating performance of the network within a context
of the application during execution; and report the correlated
application data and the identified subset of the network data
indicating the performance of the network within the context of the
application from a remote server.
20. The system of claim 19, wherein the call chain data includes a
sequence of nodes that have previously processed the distributed
business transaction.
Description
BACKGROUND
[0001] The World Wide Web has expanded to provide numerous web
services to consumers. The web services may be provided by a web
application which uses multiple services and applications to handle
a transaction. The applications may be distributed over several
machines, making the topology of the machines that provide the
service more difficult to track and monitor.
[0002] Monitoring a web application helps to provide insight
regarding bottle necks in communication, communication failures and
other information regarding performance of the services that
provide the web application. Most application monitoring tools
provide a standard report regarding application performance. Though
the typical report may be helpful for most users, it may not
provide the particular information that an administrator wants to
know.
[0003] In particular, application performance management (APM)
systems typically only monitor the performance of an application.
The APM systems usually do not provide performance details of a
particular network over which an application executes. If network
information is provided, it is typically only the time that the
transaction spends on the network--there is no context or other
data regarding the network. What is needed is an APM system that
provides application-specific network performance details.
SUMMARY
[0004] The present technology determines the performance of a
network within the context of an application using that network.
Network data is collected and correlated with an application that
uses the network as well as a distributed transaction implemented
by the application. The collected network data is culled, and the
remaining data is rolled up into one or more metrics. The metrics,
selected network data, and other data are reported in the context
of the application that implements part of the distributed
transaction. In this manner, specific network performance and
architecture data is reported along with application context
information.
[0005] An embodiment may include a method for correlating
application performance data and network performance data for a
distributed transaction. Application data may be collected by a
first module installed on a first machine, such that the
application data is collected during execution of an application.
The application may be one of a plurality of applications on one or
more machines that implement a distributed transaction. Network
data may be collected for a network by a second module installed on
the first machine, such that the network data is collected during
execution of the application while implementing a portion of the
distributed transaction over the network. The application data and
the network data may be correlated using distributed transaction
information. The correlated application data and the network data
may be reported from a remote server.
[0006] An embodiment may include a system for reporting data. The
system may include a processor, memory, and one or more modules
stored in memory and executable by the processor. When executed,
the modules may collect application data by a first module
installed on a first machine, the application data collected during
execution of an application, the application one of a plurality of
applications on one or more machines that implement a distributed
transaction, collect network data for a network by a second module
installed on the first machine, the network data collected during
execution of the application while implementing a portion of the
distributed transaction over the network, correlate the application
data and the network data using distributed transaction
information, and report the correlated application data and the
network data from a remote server.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a block diagram of a system for correlating an
application and network performance data.
[0008] FIG. 2 is a block diagram of a host which implements a
standalone network agent.
[0009] FIG. 3 is a block diagram of a host that implements a
plug-in network agent.
[0010] FIG. 4 is a block diagram of an open system interconnection
model.
[0011] FIG. 5 is a block diagram of data flow from an application
and network performance monitoring system.
[0012] FIG. 6 is a method for providing a language agent in a
monitoring system.
[0013] FIG. 7 is a method for providing a network agent in a
monitoring system.
[0014] FIG. 8 is a method for providing a controller and a
monitoring system.
[0015] FIG. 9 is a method for reporting correlated application data
and network data.
[0016] FIG. 10 is an example of reporting application data and
correlated network data.
[0017] FIG. 11 is a block diagram of a computing environment for
implementing the present technology
DETAILED DESCRIPTION
[0018] The present technology determines the performance of a
network within the context of an application using that network.
Network data is collected and correlated with an application that
uses the network as well as a distributed transaction implemented
by the application. The collected network data is culled, and the
remaining data is rolled up into one or more metrics. The metrics,
selected network data, and other data are reported in the context
of the application that implements part of the distributed
transaction. In this manner, specific network performance and
architecture data is reported along with application context
information.
[0019] To provide application context to the network data, business
transaction informant is provided to a module, such as an agent,
that collects the network data. The network agent receives the
distributed transaction information along with an identification of
what network data to associate it with. The network agent collects
network data, such as network flow group data, and identifies the
network group data associated with the distributed transaction
information. The network agent then generates metrics from the
identified network group data, and transmits the metrics, the
associated distributed transaction information, and optionally
other data, such as the network group flow data, to a remote
controller. The remote controller receives the data from the
network agent, receives application metric data from other agents,
and correlates the network flow group metrics and application
metric data using the distributed transaction information. The
controller may report the performance of the application along with
network performance data and architecture information to the user
for a particular application.
[0020] Correlating and reporting application and network
performance data together brings relevant network level
infrastructure visibility that directly correlates to application
performance. The present monitoring is performed from a consumer
point of view on a consumer machine, rather than from the point of
view of some point on the network, which would not provide an
entirely accurate picture of what is occurring from the point of
view of the consumer. In some instances, the network monitoring
system may be implemented on servers providing the application that
executes over the network, and monitors network degradation to
determine if the network degradation affects an application.
[0021] FIG. 1 is a block diagram of a system for correlating an
application and network performance data. System 100 of FIG. 1
includes client device 105 and 192, mobile device 115, network 120,
network server 125, application servers 130, 140, 150 and 160,
asynchronous network machine 170, data stores 180 and 185,
controller 190, and data collection server 195.
[0022] Client device 105 may include network browser 110 and be
implemented as a computing device, such as for example a laptop,
desktop, workstation, or some other computing device. Network
browser 110 may be a client application for viewing content
provided by an application server, such as application server 130
via network server 125 over network 120.
[0023] Network browser 110 may include agent 112. Agent 112 may be
installed on network browser 110 and/or client 105 as a network
browser add-on, downloading the application to the server, or in
some other manner. Agent 112 may be executed to monitor network
browser 110, the operation system of client 105, and any other
application, API, or other component of client 105. Agent 112 may
determine network browser navigation timing metrics, access browser
cookies, monitor code, and transmit data to data collection 160,
controller 190, or another device. Agent 112 may perform other
operations related to monitoring a request or a network at client
105 as discussed herein.
[0024] Mobile device 115 is connected to network 120 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 105 and mobile device 115 may include hardware and/or
software configured to access a web service provided by network
server 125.
[0025] Mobile device 115 may include network browser 117 and an
agent 119. Agent 119 may reside in and/or communicate with network
browser 117, as well as communicate with other applications, an
operating system, APIs and other hardware and software on mobile
device 115. Agent 119 may have similar functionality as that
described herein for agent 112 on client 105, and may repot data to
data collection server 160 and/or controller 190.
[0026] Network 120 may facilitate communication of data between
different servers, devices and machines of system 100 (some
connections shown with lines to network 120, 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 120 may include one or more machines such as load balance
machines and other machines.
[0027] Network server 125 is connected to network 120 and may
receive and process requests received over network 120. Network
server 125 may be implemented as one or more servers implementing a
network service, and may be implemented on the same machine as
application server 130. When network 120 is the Internet, network
server 125 may be implemented as a web server. Network server 125
and application server 130 may be implemented on separate or the
same server or machine.
[0028] Application server 130 communicates with network server 125,
application servers 140 and 150, and controller 190. Application
server 130 may also communicate with other machines and devices
(not illustrated in FIG. 1). Application server 130 may host an
application or portions of a distributed application. The host
application 132 may be in one of many platforms, such as including
a Java, PHP, .NET, Node.JS, be implemented as a Java virtual
machine, or include some other host type. Application server 130
may also include one or more agents 134 (i.e. "modules"), including
a language agent, machine agent, and network agent, and other
software modules. Application server 130 may be implemented as one
server or multiple servers as illustrated in FIG. 1.
[0029] Application 132 and other software on application server 130
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 132, calls sent by
application 132, and communicate with agent 134 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.
[0030] In some embodiments, server 130 may include applications
and/or code other than a virtual machine. For example, server 130
may include Java code, .NET code, PHP code, Ruby code, C code or
other code to implement applications and process requests received
from a remote source.
[0031] Agents 134 on application server 130 may be installed,
downloaded, embedded, or otherwise provided on application server
130. For example, agents 134 may be provided in server 130 by
instrumentation of object code, downloading the agents to the
server, or in some other manner. Agents 134 may be executed to
monitor application server 130, monitor code running in a or a
virtual machine 132 (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 130
and one or more applications on application server 130.
[0032] Each of agents 134, 144, 154 and 164 may include one or more
agents, such as a 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. Agents are discussed in more detail
below with respect to FIG. 2.
[0033] Agent 134 may detect operations such as receiving calls and
sending requests by application server 130, resource usage, and
incoming packets. Agent 134 may receive data, process the data, for
example by aggregating data into metrics, and transmit the data
and/or metrics to controller 190. Agent 134 may perform other
operations related to monitoring applications and application
server 130 as discussed herein. For example, agent 134 may identify
other applications, share business transaction data, aggregate
detected runtime data, and other operations.
[0034] An agent may operate to monitor a node, tier or nodes or
other entity. A node may be a software program or a hardware
component (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.
[0035] Agent 134 may create a request identifier for a request
received by server 130 (for example, a request received by a client
105 or 115 associated with a user or another source). The request
identifier may be sent to client 105 or mobile device 115,
whichever device sent the request. In embodiments, the request
identifier may be created when a data is collected and analyzed for
a particular business transaction. Additional information regarding
collecting data for analysis is discussed in U.S. patent
application no. U.S. patent application Ser. No. 12/878,919, titled
"Monitoring Distributed Web Application Transactions," filed on
Sep. 9, 2010, U.S. Pat. No. 8,938,533, titled "Automatic Capture of
Diagnostic Data Based on Transaction Behavior Learning," filed on
Jul. 22, 2011, and U.S. patent application Ser. No. 13/365,171,
titled "Automatic Capture of Detailed Analysis Information for Web
Application Outliers with Very Low Overhead," filed on Feb. 2,
2012, the disclosures of which are incorporated herein by
reference.
[0036] Each of application servers 140, 150 and 160 may include an
application and agents. Each application may run on the
corresponding application server. Each of applications 142, 152 and
162 on application servers 140-160 may operate similarly to
application 132 and perform at least a portion of a distributed
business transaction. Agents 144, 154 and 164 may monitor
applications 142-162, collect and process data at runtime, and
communicate with controller 190. The applications 132, 142, 152 and
162 may communicate with each other as part of performing a
distributed transaction. In particular each application may call
any application or method of another virtual machine.
[0037] Asynchronous network machine 170 may engage in asynchronous
communications with one or more application servers, such as
application server 150 and 160. For example, application server 150
may transmit several calls or messages to an asynchronous network
machine. Rather than communicate back to application server 150,
the asynchronous network machine may process the messages and
eventually provide a response, such as a processed message, to
application server 160. Because there is no return message from the
asynchronous network machine to application server 150, the
communications between them are asynchronous.
[0038] Data stores 180 and 185 may each be accessed by application
servers such as application server 150. Data store 185 may also be
accessed by application server 150. Each of data stores 180 and 185
may store data, process data, and return queries received from an
application server. Each of data stores 180 and 185 may or may not
include an agent.
[0039] Controller 190 may control and manage monitoring of business
transactions distributed over application servers 130-160. In some
embodiments, controller 190 may receive application data, including
data associated with monitoring client requests at client 105 and
mobile device 115, from data collection server 160. In some
embodiments, controller 190 may receive application monitoring data
and network data from each of agents 112, 119, 134, 144 and 154.
Controller 190 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 192, which may be a mobile device, client device, or any
other platform for viewing an interface provided by controller 190.
In some embodiments, a client device 192 may directly communicate
with controller 190 to view an interface for monitoring data.
[0040] Client device 192 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 192 may
communicate with controller 190 to create and view a custom
interface. In some embodiments, controller 190 provides an
interface for creating and viewing the custom interface as content
page, e.g. a web page, which may be provided to and rendered
through a network browser application on client device 192.
[0041] Applications 132, 142, 152 and 162 may be any of several
types of applications. Examples of applications that may implement
applications 132-162 include a Java, PHP, .Net, Node.JS, and other
applications.
[0042] FIG. 2 is a block diagram of a host which implements a
standalone network agent. Host 250 may be implemented as a virtual
machine, or an application of some type, such as a PHP application,
or any other node capable of being monitored by an agent. Host 250
includes language agent 220, network agent 230, and machine agent
240. Language agent 220 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 agent
220 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 which
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 network agent 230.
[0043] Network agent 230 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 230 is installed. The network
agent 230 may collect data by intercepting and performing packet
capture on packets coming in from a one or more sockets. 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 reports the metrics, flow group data, and call chain data
to a controller. The network agent 230 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.
[0044] A machine agent 240 may reside on the host 250 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.
[0045] Each of the language agent 220, network agent 230, and
machine agent 240 may report data to the controller 210. Controller
210 may be implemented as a remote server that communicates with
agents 220-240. The controller 210 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.
[0046] FIG. 3 is a block diagram of a host that implements a
plug-in network agent. Host 350 includes language agent 320,
network agent 330, and machine agent 340. Network agent 330 may be
implemented as a plug-in module that is installed onto language
agent 320. The network agent 330 operates similarly to network
agent 230, but is installed in an agent rather than directly on the
host.
[0047] FIG. 4 is a block diagram of an open system interconnection
module. The open systems interconnection model (OSI model) is a
conceptual model that characterizes and standardizes the
communication functions of a computing system. The seven layers of
the model include a physical layer, data link layer, network layer,
transport layer, session layer, presentation layer, and application
layer. Language agents may collect data by monitoring an
application layer. Network agents may collect data by monitoring a
network layer, for example by monitoring a socket to collect the
network layer data as it comes in over the socket. The different
layers are monitored from a consumer device rather than the network
in order to obtain the most accurate information from the point of
view of the consumer.
[0048] FIG. 5 is a block diagram of a data flow for an application
and network performance monitoring system. The block diagram of
FIG. 5 includes language agent 510, network agent 520, and
controller 530. Language agent 510 provides data to controller 530
and network agent 520. Network agent 520 provides data to
controller 530. Language agent 510 receives application data from
an application being monitored, application flow data from messages
received by the application, and call chain data. Language agent
510 creates metrics from the application data and reports the
application flow data and call chain data to the network agent 520.
Language agent 510 also reports the application data, application
data metrics, and call chain data to the controller 530. The
application data and metrics are associated with a particular
distributed transaction through the call chain data which specifies
a particular sequence of machines that process a distributed
transaction.
[0049] Network agent 520 receives network flow group data through
packet capture performed while monitoring a socket. The network
agent then generates metrics from the flow group data for flows
that correspond to the application flow data received from the
language agent 510. Network agent 520 then reports the metrics as
well as the flow group data associated with a particular
application to the controller along with the call chain data. The
controller receives the data from the language agent and network
agent and correlates it together as a distributed transaction based
on the call chain data associated with the distributed
transaction.
[0050] FIG. 6 is a method for providing a language agent in a
monitoring system. First, application data and call chain data may
be collected for an application that processes business
transactions by a language agent at step 610. The call chain data
may include a series of machines and services that have previously
processed an application transaction.
[0051] Network flow data is collected for selected applications by
a language agent at step 620. The network flow data may include a
tuple of source IP, source port, destination IP, and destination
port data. This data is collected as a time series of tuples by
monitoring the deepest levels of an application by the language
agent.
[0052] Network flow data and call chain data are provided to a
network agent at step 630. The network flow data and call chain
data may be provided periodically, upon request of the network
agent, or based on another event. The collected application data is
an aggregated by the language agent at step 640. The data may be
aggregated into a series of metrics, such as response time, average
time, and other data. Next, the aggregated application data and
call chain data may be reported to a controller by the language
agent at step 650. The reported data is associated with a call
chain, and is used to correlate with other reported data, such as
network flow data and architecture data, at a controller.
[0053] FIG. 7 is a method for providing a network agent in a
monitoring system. Network flow group and network infrastructure
data is collected by a network agent at step 710. The data may be
collected at a socket and includes network layer data such as
source IP, destination port, destination IP, and protocol data.
Application flow data and call chain data are received from a
language agent by the network agent at step 770. The call chain
data and application flow data may be used by the network agent to
identify flow group data for processing and reporting b to a
controller y the network agent.
[0054] A subset of the network data collected by the network agent
is identified at step 730. The subset of flow group data that is
collected corresponds to application flow data received by the
network agent from the language agent. Hence, the network agent
identifies flow group data received over a socket that matches flow
data received from the language agent. Next, the identified network
flow group data is aggregated into metrics by the network agent.
Flow group data not matching the flow data is discarded, while
matching flow group data is kept and rolled into one or more
metrics by the network agent. The metrics may include TCP
throughput, TCP packet loss, latency, bandwidth, and other metrics.
After aggregating the metrics, the identified network flow group
data and network infrastructure data, metrics, and call chain data
may be reported to the controller by the network agent. The data
may be reported periodically, in response to a request by a
controller, or based on some other event.
[0055] FIG. 8 is a method for providing a controller and a
monitoring system. First, application data metrics and call chain
data are received from a language agent by a controller at step
810. Next, flow group data, flow group metrics and call chain data
may be received from a network agent by the controller at step 820.
The application metrics and flow group metrics may then be
correlated using the call chain data by the controller at step 830.
The correlated application data and network data may then be
reported to a user by a controller at step 840. Reporting the
correlated application data is discussed in more detail below with
respect to FIG. 9.
[0056] FIG. 9 is a method for reporting correlated application data
and network data. Application data and application infrastructure
data is reported to a user at step 910. The application data and
infrastructure information may include an identification of the
nodes, the application ID, and other information regarding an
application. Network data and network infrastructure information
may be reported at step 920. The network infrastructure may include
the nodes from which a message is sent and received, as well as any
intermediary machines, such as a load balancer. Network metrics
correlated with the application metrics may then be reported at
step 930. The metrics may include the performance of an application
within the distributed transaction as well as the performance of
the network that carried out the distributed transaction. An
example of reporting network metrics correlated with application
metrics is provided in the interface of FIG. 10.
[0057] FIG. 10 includes a first graphical interface 1010 which
shows application data and metric information. Interface 1010
illustrates tier 1 in communication with tier 2 and tier 3. The
connection between tier 1 and tier 2 has metrics of 100 calls per
minute and an average response time of 200 MS. The application
metrics between tier 1 and tier 3 include an average of 25 calls
per minute with an average response time of 400 MS for the
application called between tier 1 and tier 3.
[0058] Interface 1020 illustrates network infrastructure and metric
information. Between tier 1 and tier 2, the infrastructure of the
network includes load balancer one. Between tier 1 and load
balancer one, the metrics displayed are a 0% loss, a 20 MS FRTT,
and a 20 MS RRTT. The network metrics between load balancer one and
tier 2 also include a 0% loss, a 20 MS FRTT, and a 20 MS RRTT. The
network metrics between tier 1 and load balancer two, which is
determined to exist between tier 1 and tier 3, include a 0% loss, a
20 MS FRTT, and a 20 MS RRTT. The network metrics between load
balancer two and tier 3 include a 1% loss, a 20 MS FRTT, and a 20
MS RRTT. Because the percentage loss of data or packets between
load balancer two and tier 3 is greater than an acceptable amount,
the line representing the network path between load balancer two
and tier 3 is highlighted as a thick FIGURE line than the other
network paths. If a user were to select the particular network path
associated with the 1% loss metric, the interface could provide the
user with additional data associated with the particular flow from
which the metrics were derived.
[0059] The application-based graphic 1010 and network-based graphic
1020 provide performance data for a particular distributed
transaction, and are thereby correlated to each other. In
particular, the application data and network data is provided for a
portion of a distributed transaction that occurs between nodes 1
and nodes 2 and 3.
[0060] FIG. 11 is a block diagram of a system for implementing the
present technology. System 1100 of FIG. 11 may be implemented in
the contexts of the likes of client computer 105 and 192, servers
125, 130, 140, 150, and 160, machine 170, data stores 180 and 190,
and controller 190. The computing system 1100 of FIG. 11 includes
one or more processors 1110 and memory 1120. Main memory 1120
stores, in part, instructions and data for execution by processor
1110. Main memory 1120 can store the executable code when in
operation. The system 1100 of FIG. 11 further includes a mass
storage device 1130, portable storage medium drive(s) 1140, output
devices 1150, user input devices 1160, a graphics display 1170, and
peripheral devices 1180.
[0061] The components shown in FIG. 11 are depicted as being
connected via a single bus 1190. However, the components may be
connected through one or more data transport means. For example,
processor unit 1110 and main memory 1120 may be connected via a
local microprocessor bus, and the mass storage device 1130,
peripheral device(s) 1180, portable storage device 1140, and
display system 1170 may be connected via one or more input/output
(I/O) buses.
[0062] Mass storage device 1130, which may be implemented with a
magnetic disk drive, an optical disk drive, a flash drive, or other
device, is a non-volatile storage device for storing data and
instructions for use by processor unit 1110. Mass storage device
1130 can store the system software for implementing embodiments of
the present invention for purposes of loading that software into
main memory 1120.
[0063] Portable storage device 1140 operates in conjunction with a
portable non-volatile storage medium, such as a floppy disk,
compact disk or Digital video disc, USB drive, memory card or
stick, or other portable or removable memory, to input and output
data and code to and from the computer system 1100 of FIG. 11. The
system software for implementing embodiments of the present
invention may be stored on such a portable medium and input to the
computer system 1100 via the portable storage device 1140.
[0064] Input devices 1160 provide a portion of a user interface.
Input devices 1160 may include an alpha-numeric keypad, such as a
keyboard, for inputting alpha-numeric and other information, a
pointing device such as a mouse, a trackball, stylus, cursor
direction keys, microphone, touch-screen, accelerometer, and other
input devices Additionally, the system 1100 as shown in FIG. 11
includes output devices 1150. Examples of suitable output devices
include speakers, printers, network interfaces, and monitors.
[0065] Display system 1170 may include a liquid crystal display
(LCD) or other suitable display device. Display system 1170
receives textual and graphical information, and processes the
information for output to the display device. Display system 1170
may also receive input as a touch-screen.
[0066] Peripherals 1180 may include any type of computer support
device to add additional functionality to the computer system. For
example, peripheral device(s) 1180 may include a modem or a router,
printer, and other device.
[0067] The system of 1100 may also include, in some
implementations, antennas, radio transmitters and radio receivers
1190. The antennas and radios may be implemented in devices such as
smart phones, tablets, and other devices that may communicate
wirelessly. The one or more antennas may operate at one or more
radio frequencies suitable to send and receive data over cellular
networks, Wi-Fi networks, commercial device networks such as a
Bluetooth devices, and other radio frequency networks. The devices
may include one or more radio transmitters and receivers for
processing signals sent and received using the antennas.
[0068] The components contained in the computer system 1100 of FIG.
11 are those typically found in computer systems that may be
suitable for use with embodiments of the present invention and are
intended to represent a broad category of such computer components
that are well known in the art. Thus, the computer system 1100 of
FIG. 11 can be a personal computer, hand held computing device,
smart phone, 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,
Macintosh OS, Android, C, C++, Node.JS, and other suitable
operating systems.
[0069] The foregoing detailed description of the technology herein
has been presented for purposes of illustration and description. It
is not intended to be exhaustive or to limit the technology to the
precise form disclosed. Many modifications and variations are
possible in light of the above teaching. The described embodiments
were chosen in order to best explain the principles of the
technology and its practical application to thereby enable others
skilled in the art to best utilize the technology in various
embodiments and with various modifications as are suited to the
particular use contemplated. It is intended that the scope of the
technology be defined by the claims appended hereto.
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