U.S. patent application number 15/095955 was filed with the patent office on 2016-12-08 for determining the chronology and causality of events.
The applicant listed for this patent is Cisco Technology, Inc.. Invention is credited to Shih-Chun Chang, Khawar Deen, Shashidhar Gandham, Anubhav Gupta, Rohit Chandra Prasad, Abhishek Ranjan Signh, Navindra Yadav.
Application Number | 20160359914 15/095955 |
Document ID | / |
Family ID | 57451053 |
Filed Date | 2016-12-08 |
United States Patent
Application |
20160359914 |
Kind Code |
A1 |
Deen; Khawar ; et
al. |
December 8, 2016 |
DETERMINING THE CHRONOLOGY AND CAUSALITY OF EVENTS
Abstract
An example method includes calculating latency bounds for
communications from two sensors to a collector (i.e., maximum and
minimum latencies). After the collector receives an event report
from the first sensor and an event report form the second sensor,
the collector can determine, using the latency bounds, whether one
event likely preceded the other.
Inventors: |
Deen; Khawar; (Sunnyvale,
CA) ; Yadav; Navindra; (Cupertino, CA) ;
Gupta; Anubhav; (Sunnyvale, CA) ; Gandham;
Shashidhar; (Fremont, CA) ; Prasad; Rohit
Chandra; (Sunnyvale, CA) ; Signh; Abhishek
Ranjan; (Pleasanton, CA) ; Chang; Shih-Chun;
(San Jose, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Cisco Technology, Inc. |
San Jose |
CA |
US |
|
|
Family ID: |
57451053 |
Appl. No.: |
15/095955 |
Filed: |
April 11, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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62171899 |
Jun 5, 2015 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
H04L 47/28 20130101;
H04L 67/12 20130101; G06F 9/45558 20130101; H04L 41/0816 20130101;
H04L 63/1433 20130101; H04L 63/1466 20130101; G06F 16/285 20190101;
G06F 2221/033 20130101; H04W 72/08 20130101; G06F 16/288 20190101;
G06T 11/206 20130101; H04J 3/0661 20130101; H04L 43/106 20130101;
H04L 47/32 20130101; G06F 16/173 20190101; G06F 2009/45587
20130101; H04L 41/22 20130101; H04L 43/0882 20130101; H04L 43/16
20130101; H04L 43/10 20130101; H04L 63/0263 20130101; H04L 63/1425
20130101; H04L 63/16 20130101; G06F 16/29 20190101; G06F 2221/2111
20130101; H04L 45/507 20130101; H04L 63/145 20130101; G06F 16/137
20190101; G06F 21/552 20130101; H04L 43/02 20130101; G06F 16/17
20190101; H04L 43/0811 20130101; G06F 21/566 20130101; H04L 1/242
20130101; H04L 61/2007 20130101; H04L 67/36 20130101; G06F 16/9535
20190101; H04L 9/3242 20130101; H04L 63/1408 20130101; H04W 84/18
20130101; G06F 16/122 20190101; G06F 2221/2101 20130101; H04L
41/0803 20130101; H04L 41/0893 20130101; H04L 47/2441 20130101;
H04L 63/0876 20130101; G06F 3/04842 20130101; H04L 9/3239 20130101;
G06F 16/2322 20190101; G06F 2221/2105 20130101; H04L 41/046
20130101; H04L 45/306 20130101; H04L 45/38 20130101; H04L 47/11
20130101; H04L 63/06 20130101; H04L 63/1441 20130101; H04L 43/045
20130101; H04L 45/66 20130101; H04L 63/20 20130101; G06F 3/04847
20130101; G06F 16/235 20190101; H04L 41/0668 20130101; H04L 47/20
20130101; H04L 67/16 20130101; H04L 67/42 20130101; G06F 16/24578
20190101; G06F 21/53 20130101; H04L 43/0876 20130101; H04L 45/74
20130101; G06F 2009/45595 20130101; G06N 99/00 20130101; H04L 69/22
20130101; G06F 16/1744 20190101; G06F 2221/2115 20130101; H04L
41/0806 20130101; G06F 16/2365 20190101; H04L 43/0829 20130101;
H04L 43/0858 20130101; H04L 43/12 20130101; G06F 3/0482 20130101;
G06F 16/162 20190101; G06F 16/174 20190101; H04L 43/08 20130101;
H04L 63/1416 20130101; H04L 67/22 20130101; H04J 3/14 20130101;
H04L 43/062 20130101; H04L 63/1458 20130101; G06F 16/248 20190101;
G06F 2009/45591 20130101; H04L 41/12 20130101; H04L 67/10 20130101;
H04L 43/0888 20130101; G06F 16/1748 20190101; H04L 47/2483
20130101; H04L 47/31 20130101; H04L 43/0805 20130101; H04L 43/0864
20130101; H04L 43/0841 20130101; G06F 2221/2145 20130101; G06N
20/00 20190101; H04L 9/0866 20130101; H04L 41/16 20130101; H04L
43/04 20130101; H04L 69/16 20130101; H04L 45/46 20130101; H04L
63/0227 20130101; G06F 2009/4557 20130101; H04L 67/1002
20130101 |
International
Class: |
H04L 29/06 20060101
H04L029/06 |
Claims
1. A computer-implemented method comprising: receiving, from a
first sensor at a first time, a description of a first event;
receiving, from a second sensor at a second time, a description of
a second event; and determining that the first event preceded the
second event.
2. The computer-implemented method of claim 1, further comprising:
determining a minimum latency associated with the first sensor;
determining a maximum latency associated with the second sensor;
and wherein the determining that the first event preceded the
second event comprises: comparing the first time minus the minimum
latency for the first sensor with the second time minus the maximum
latency for the second sensor.
3. The computer-implemented method of claim 2, further comprising:
determining a maximum latency associated with the first sensor;
determining a minimum latency associated with the second sensor;
and determining that the first event caused the second event by
determining that the first time minus the maximum latency
associated with the first sensor is within a predetermined range
with the second time minus the minimum latency associated with the
second sensor.
4. The computer-implemented method of claim 3, wherein determining
that the first event caused the second event is based on the type
of event of the first event and the type of event of the second
event.
5. The computer-implemented method of claim 4, wherein the second
event is a data center attack.
6. The computer-implemented method of claim 1, further comprising:
receiving, from the first sensor, a description of a third event;
receiving, from the second sensor, a description of a fourth event;
wherein the determining that the first event preceded the second
event comprises: determining that the first event preceded the
third event; determining that the third event preceded the fourth
event; and determining that the fourth event preceded the second
event.
7. The computer-implemented method of claim 6, wherein: determining
that the first event preceded the third event comprises comparing:
a timestamp associated with the first event with a timestamp
associated with the third event; or a sequence number associated
with the first event with a sequence number associated with the
third event; and determining that the third event preceded the
fourth event comprises comparing: an event type associated with the
third event with an event type associated with the fourth
event.
8. The computer-implemented method of claim 7, wherein the first
event is a data center attack, the method further comprising:
determining that the second event did not cause the first
event.
9. The computer-implemented method of claim 1, wherein the second
event is a process starting.
10. The computer-implemented method of claim 1, wherein the second
time is before the first time.
11. A non-transitory computer-readable medium having computer
readable instructions stored thereon that, when executed by a
processor of a computer, cause the computer to: receive, from a
first sensor at a first time, a description of a first event;
receive, from the first sensor, a description of a third event;
receive, from a second sensor at a second time, a description of a
second event; receive, from the second sensor, a description of a
fourth event; and determine that the first event preceded the
second event by: determining that the first event preceded the
third event; determining that the third event preceded the fourth
event; and determining that the fourth event preceded the second
event.
12. The non-transitory computer-readable medium of claim 11,
wherein: the instructions that cause the computer to determine that
the first event preceded the third event further cause the computer
to compare: a timestamp associated with the first event with a
timestamp associated with the third event; or a sequence number
associated with the first event with a sequence number associated
with the third event; and the instructions that cause the computer
to determine that the third event preceded the fourth event further
cause the computer to compare an event type associated with the
third event with an event type associated with the fourth
event.
13. The non-transitory computer-readable medium of claim 12,
wherein the first event is a data center attack and the
instructions further cause the computer to: determine that the
second event did not cause the first event.
14. The computer-implemented method of claim 1, wherein the second
sensor is installed on a virtual machine and the second event is a
process starting.
15. The computer-implemented method of claim 1, wherein the second
time is before the first time.
16. A system comprising: a processor; a computer-readable medium;
and non-transitory computer-readable instructions stored thereon
that, when executed by the processor, cause the system to:
determine a minimum latency associated with a first sensor;
receive, from the first sensor at a first time, a description of a
first event; determine a maximum latency associated with a second
sensor; receive, from the second sensor at a second time, a
description of a second event; and determine that the first event
preceded the second event by: comparing the first time minus the
minimum latency for the first sensor with the second time minus the
maximum latency for the second sensor.
17. The system of claim 16, wherein the instructions further cause
the system to: determine a maximum latency associated with the
first sensor; determine a minimum latency associated with the
second sensor; and determine that the first event caused the second
event by determining that the first time minus the maximum latency
associated with the first sensor is within a predetermined range
with the second time minus the minimum latency associated with the
second sensor.
18. The system of claim 17, wherein the instructions that cause the
system to determine that the first event caused the second event do
so based on the type of event of the first event and the type of
event of the second event.
19. The system of claim 18, wherein the second event is a data
center attack.
20. The system of claim 16, wherein the sensor is installed on a
virtual machine and the second event is a process starting up.
Description
RELATED APPLICATIONS
[0001] This application claims priority to U.S. Provisional
Application No. 62/171,899, entitled "SYSTEM FOR MONITORING AND
MANAGING DATACENTERS", filed 5 Jun. 2015, which is incorporated
herein by reference in its entirety.
TECHNICAL FIELD
[0002] The present technology pertains to network security and more
specifically pertains to determining the chronology of events with
the network.
BACKGROUND
[0003] Data centers typically include large numbers of entities
(e.g., servers, switches, routers, etc.), each of which has its own
internal clock used to annotate the time of events. These entities
are rarely fully synchronized and, across the data center, their
clocks often present substantial discrepancies. This is known as
the clock skew problem.
[0004] Clock skew can make it difficult to determine event
sequences (i.e., what is the proper chronological ordering of the
events) and event causality (which events triggered which events).
If all clocks in the data center were guaranteed to be in exact
synchrony then ascertaining chronological ordering of events and
event causality could be achieved by simply looking at event
timestamps. However, since the various clocks in the network are
not guaranteed to be in exact synchrony even when using clock
management mechanisms such as NTP, determining event chronology and
causality in the data center can prove difficult. This is
particularly difficult when dealing with events that occurred very
close in time.
BRIEF DESCRIPTION OF THE FIGURES
[0005] In order to describe the manner in which the above-recited
and other advantages and features of the disclosure can be
obtained, a more particular description of the principles briefly
described above will be rendered by reference to specific
embodiments that are illustrated in the appended drawings.
Understanding that these drawings depict only example embodiments
of the disclosure and are not therefore to be considered to be
limiting of its scope, the principles herein are described and
explained with additional specificity and detail through the use of
the accompanying drawings in which:
[0006] FIG. 1 shows an example network traffic monitoring system
according to some example embodiments;
[0007] FIG. 2 illustrates an example network environment 200
according to some example embodiments;
[0008] FIG. 3 shows an example method 300 for determining the
chronological ordering of two events according to various
embodiments;
[0009] FIG. 4A shows an example graphical representation of the
timing of an event according to various embodiments;
[0010] FIG. 4B depicts an example timeline according to various
embodiments;
[0011] FIGS. 5A, 5B, 5C, 5D, and 5E show example chronological
graphs according to various embodiments; and
[0012] FIGS. 6A and 6B illustrate example system embodiments.
DESCRIPTION OF EXAMPLE EMBODIMENTS
Overview
[0013] The present technology includes determining precedence and
causality of network events.
[0014] An example method includes calculating latency bounds for
communications from two sensors to a collector (i.e., maximum and
minimum latencies). After the collector receives an event report
from the first sensor and an event report form the second sensor,
the collector can determine, using the latency bounds, whether one
event likely preceded the other.
Description
[0015] Various embodiments of the disclosure are discussed in
detail below. While specific implementations are discussed, it
should be understood that this is done for illustration purposes
only. A person skilled in the relevant art will recognize that
other components and configurations may be used without parting
from the spirit and scope of the disclosure.
[0016] The disclosed technology addresses the need in the art for
determining the chronology and causality of events in a data
center.
[0017] Traditional data centers mainly contemplated attack vectors
from outside the data center. Thus, firewalls and other security
devices have traditionally been placed on the periphery of the data
center. However, as modern data centers have begun to house diverse
customers with a growing number of applications, internal security
within a datacenter is becoming more important. Sophisticated
attacks (or malfunctioning or misconfigured components) can be
difficult to identify and trace. A crucial element of successfully
identifying attacks can be to establish a trustworthy chronological
history of events. The present disclosure enables a system to
identify events with particularity across a datacenter and then
piece together an accurate record. As discussed in greater detail
below, this can be accomplished despite untrustworthy clocks on
components or variable latency throughout the data center.
[0018] FIG. 1 shows an example network traffic monitoring system
100 according to some example embodiments. Network traffic
monitoring system 100 can include configuration and image manager
102, sensors 104, external data sources 106, collectors 108,
analytics module 110, policy engine 112, and presentation module
116. These modules may be implemented as hardware and/or software
components. Although FIG. 1 illustrates an example configuration of
the various components of network traffic monitoring system 100,
those of skill in the art will understand that the components of
network traffic monitoring system 100 or any system described
herein can be configured in a number of different ways and can
include any other type and number of components. For example,
sensors 104 and collectors 108 can belong to one hardware and/or
software module or multiple separate modules. Other modules can
also be combined into fewer components and/or further divided into
more components.
[0019] Configuration and image manager 102 can provision and
maintain sensors 104. In some example embodiments, sensors 104 can
reside within virtual machine images, and configuration and image
manager 102 can be the component that also provisions virtual
machine images.
[0020] Configuration and image manager 102 can configure and manage
sensors 104. When a new virtual machine is instantiated or when an
existing one is migrated, configuration and image manager 102 can
provision and configure a new sensor on the machine. In some
example embodiments configuration and image manager 102 can monitor
the health of sensors 104. For instance, configuration and image
manager 102 may request status updates or initiate tests. In some
example embodiments, configuration and image manager 102 can also
manage and provision virtual machines.
[0021] In some example embodiments, configuration and image manager
102 can verify and validate sensors 104. For example, sensors 104
can be provisioned a unique ID that is created using a one-way hash
function of its basic input/output system (BIOS) universally unique
identifier (UUID) and a secret key stored on configuration and
image manager 102. This UUID can be a large number that is
difficult for an imposter sensor to guess. In some example
embodiments, configuration and image manager 102 can keep sensors
104 up to date by installing new versions of their software and
applying patches. Configuration and image manager 102 can obtain
these updates automatically from a local source or the
Internet.
[0022] Sensors 104 can reside on nodes of a data center network
(e.g., virtual partition, hypervisor, physical server, switch,
router, gateway, other network device, other electronic device,
etc.). In general, a virtual partition may be an instance of a
virtual machine (VM) (e.g., VM 104a), sandbox, container (e.g.,
container 104c), or any other isolated environment that can have
software operating within it. The software may include an operating
system and application software. For software running within a
virtual partition, the virtual partition may appear to be a
distinct physical server. In some example embodiments, a hypervisor
(e.g., hypervisor 104b) may be a native or "bare metal" hypervisor
that runs directly on hardware, but that may alternatively run
under host software executing on hardware. Sensors 104 can monitor
communications to and from the nodes and report on environmental
data related to the nodes (e.g., node IDs, statuses, etc.). Sensors
104 can send their records over a high-speed connection to
collectors 108 for storage. Sensors 104 can comprise a piece of
software (e.g., running on a VM, container, virtual switch,
hypervisor, physical server, or other device), an
application-specific integrated circuit (ASIC) (e.g., a component
of a switch, gateway, router, standalone packet monitor, or other
network device including a packet capture (PCAP) module or similar
technology), or an independent unit (e.g., a device connected to a
network device's monitoring port or a device connected in series
along a main trunk of a datacenter). It should be understood that
various software and hardware configurations can be used as sensors
104. Sensors 104 can be lightweight, thereby minimally impeding
normal traffic and compute resources in a datacenter. Sensors 104
can "sniff" packets being sent over its host network interface card
(NIC) or individual processes can be configured to report traffic
to sensors 104. This sensor structure allows for robust capture of
granular (i.e., specific) network traffic data from each hop of
data transmission.
[0023] As sensors 104 capture communications, they can continuously
send network traffic data to collectors 108. The network traffic
data can relate to a packet, a collection of packets, a flow, a
group of flows, etc. The network traffic data can also include
other details such as the VM BIOS ID, sensor ID, associated process
ID, associated process name, process user name, sensor private key,
geo-location of a sensor, environmental details, etc. The network
traffic data can include information describing the communication
on all layers of the Open Systems Interconnection (OSI) model. For
example, the network traffic data can include signal strength (if
applicable), source/destination media access control (MAC) address,
source/destination internet protocol (IP) address, protocol, port
number, encryption data, requesting process, a sample packet,
etc.
[0024] In some example embodiments, sensors 104 can preprocess
network traffic data before sending to collectors 108. For example,
sensors 104 can remove extraneous or duplicative data or they can
create a summary of the data (e.g., latency, packets and bytes sent
per flow, flagged abnormal activity, etc.). In some example
embodiments, sensors 104 can be configured to only capture certain
types of connection information and disregard the rest. Because it
can be overwhelming for a system to capture every packet in a
network, in some example embodiments, sensors 104 can be configured
to capture only a representative sample of packets (e.g., every
1,000th packet or other suitable sample rate).
[0025] Sensors 104 can send network traffic data to one or multiple
collectors 108. In some example embodiments, sensors 104 can be
assigned to a primary collector and a secondary collector. In other
example embodiments, sensors 104 are not assigned a collector, but
can determine an optimal collector through a discovery process.
Sensors 104 can change where they send their network traffic data
if their environments change, such as if a certain collector
experiences failure or if a sensor is migrated to a new location
and becomes closer to a different collector. In some example
embodiments, sensors 104 can send different types of network
traffic data to different collectors. For example, sensors 104 can
send network traffic data related to one type of process to one
collector and network traffic data related to another type of
process to another collector.
[0026] Collectors 108 can serve as a repository for the data
recorded by sensors 104. In some example embodiments, collectors
108 can be directly connected to a top of rack switch. In other
example embodiments, collectors 108 can be located near an end of
row switch. Collectors 108 can be located on or off premises. It
will be appreciated that the placement of collectors 108 can be
optimized according to various priorities such as network capacity,
cost, and system responsiveness. In some example embodiments, data
storage of collectors 108 is located in an in-memory database, such
as dashDB by International Business Machines. This approach
benefits from rapid random access speeds that typically are
required for analytics software. Alternatively, collectors 108 can
utilize solid state drives, disk drives, magnetic tape drives, or a
combination of the foregoing according to cost, responsiveness, and
size requirements. Collectors 108 can utilize various database
structures such as a normalized relational database or NoSQL
database.
[0027] In some example embodiments, collectors 108 may only serve
as network storage for network traffic monitoring system 100. In
other example embodiments, collectors 108 can organize, summarize,
and preprocess data. For example, collectors 108 can tabulate how
often packets of certain sizes or types are transmitted from
different nodes of a data center. Collectors 108 can also
characterize the traffic flows going to and from various nodes. In
some example embodiments, collectors 108 can match packets based on
sequence numbers, thus identifying traffic flows and connection
links. In some example embodiments, collectors 108 can flag
anomalous data. Because it would be inefficient to retain all data
indefinitely, in some example embodiments, collectors 108 can
periodically replace detailed network traffic flow data with
consolidated summaries. In this manner, collectors 108 can retain a
complete dataset describing one period (e.g., the past minute or
other suitable period of time), with a smaller dataset of another
period (e.g., the previous 2-10 minutes or other suitable period of
time), and progressively consolidate network traffic flow data of
other periods of time (e.g., day, week, month, year, etc.). By
organizing, summarizing, and preprocessing the network traffic flow
data, collectors 108 can help network traffic monitoring system 100
scale efficiently. Although collectors 108 are generally referred
to herein in the plurality, it will be appreciated that collectors
108 can be implemented using a single machine, especially for
smaller datacenters.
[0028] In some example embodiments, collectors 108 can receive data
from external data sources 106, such as security reports,
white-lists (106a), IP watchlists (106b), whois data (106c), or
out-of-band data, such as power status, temperature readings,
etc.
[0029] In some example embodiments, network traffic monitoring
system 100 can include a wide bandwidth connection between
collectors 108 and analytics module 110. Analytics module 110 can
include application dependency (ADM) module 160, reputation module
162, vulnerability module 164, malware detection module 166, etc.,
to accomplish various tasks with respect to the flow data collected
by sensors 104 and stored in collectors 108. In some example
embodiments, network traffic monitoring system 100 can
automatically determine network topology. Using network traffic
flow data captured by sensors 104, network traffic monitoring
system 100 can determine the type of devices existing in the
network (e.g., brand and model of switches, gateways, machines,
etc.), physical locations (e.g., latitude and longitude, building,
datacenter, room, row, rack, machine, etc.), interconnection type
(e.g., 10 Gb Ethernet, fiber-optic, etc.), and network
characteristics (e.g., bandwidth, latency, etc.). Automatically
determining the network topology can assist with integration of
network traffic monitoring system 100 within an already established
datacenter. Furthermore, analytics module 110 can detect changes of
network topology without the need of further configuration.
[0030] Analytics module 110 can determine dependencies of
components within the network using ADM module 160. For example, if
component A routinely sends data to component B but component B
never sends data to component A, then analytics module 110 can
determine that component B is dependent on component A, but A is
likely not dependent on component B. If, however, component B also
sends data to component A, then they are likely interdependent.
These components can be processes, virtual machines, hypervisors,
virtual local area networks (VLANs), etc. Once analytics module 110
has determined component dependencies, it can then form a component
("application") dependency map. This map can be instructive when
analytics module 110 attempts to determine a root cause of a
failure (because failure of one component can cascade and cause
failure of its dependent components). This map can also assist
analytics module 110 when attempting to predict what will happen if
a component is taken offline. Additionally, analytics module 110
can associate edges of an application dependency map with expected
latency, bandwidth, etc. for that individual edge.
[0031] Analytics module 110 can establish patterns and norms for
component behavior. For example, it can determine that certain
processes (when functioning normally) will only send a certain
amount of traffic to a certain VM using a small set of ports.
Analytics module can establish these norms by analyzing individual
components or by analyzing data coming from similar components
(e.g., VMs with similar configurations) Similarly, analytics module
110 can determine expectations for network operations. For example,
it can determine the expected latency between two components, the
expected throughput of a component, response times of a component,
typical packet sizes, traffic flow signatures, etc. In some example
embodiments, analytics module 110 can combine its dependency map
with pattern analysis to create reaction expectations. For example,
if traffic increases with one component, other components may
predictably increase traffic in response (or latency, compute time,
etc.).
[0032] In some example embodiments, analytics module 110 can use
machine learning techniques to identify security threats to a
network using malware detection module 166. For example, malware
detection module 166 can be provided with examples of network
states corresponding to an attack and network states corresponding
to normal operation. Malware detection module 166 can then analyze
network traffic flow data to recognize when the network is under
attack. In some example embodiments, the network can operate within
a trusted environment for a time so that analytics module 110 can
establish baseline normalcy. In some example embodiments, analytics
module 110 can contain a database of norms and expectations for
various components. This database can incorporate data from sources
external to the network (e.g., external sources 106). Analytics
module 110 can then create access policies for how components can
interact using policy engine 112. In some example embodiments,
policies can be established external to network traffic monitoring
system 100 and policy engine 112 can detect the policies and
incorporate them into analytics module 110. A network administrator
can manually tweak the policies. Policies can dynamically change
and be conditional on events. These policies can be enforced by the
components depending on a network control scheme implemented by a
network. Policy engine 112 can maintain these policies and receive
user input to change the policies.
[0033] Policy engine 112 can configure analytics module 110 to
establish or maintain network policies. For example, policy engine
112 may specify that certain machines should not intercommunicate
or that certain ports are restricted. A network and security policy
controller (not shown) can set the parameters of policy engine 112.
In some example embodiments, policy engine 112 can be accessible
via presentation module 116. In some example embodiments, policy
engine 112 can include policy data 112. In some example
embodiments, policy data 112 can include endpoint group (EPG) data
114, which can include the mapping of EPGs to IP addresses and/or
MAC addresses. In some example embodiments, policy data 112 can
include policies for handling data packets.
[0034] In some example embodiments, analytics module 110 can
simulate changes in the network. For example, analytics module 110
can simulate what may result if a machine is taken offline, if a
connection is severed, or if a new policy is implemented. This type
of simulation can provide a network administrator with greater
information on what policies to implement. In some example
embodiments, the simulation may serve as a feedback loop for
policies. For example, there can be a policy that if certain
policies would affect certain services (as predicted by the
simulation) those policies should not be implemented. Analytics
module 110 can use simulations to discover vulnerabilities in the
datacenter. In some example embodiments, analytics module 110 can
determine which services and components will be affected by a
change in policy. Analytics module 110 can then take necessary
actions to prepare those services and components for the change.
For example, it can send a notification to administrators of those
services and components, it can initiate a migration of the
components, it can shut the components down, etc.
[0035] In some example embodiments, analytics module 110 can
supplement its analysis by initiating synthetic traffic flows and
synthetic attacks on the datacenter. These artificial actions can
assist analytics module 110 in gathering data to enhance its model.
In some example embodiments, these synthetic flows and synthetic
attacks are used to verify the integrity of sensors 104, collectors
108, and analytics module 110. Over time, components may
occasionally exhibit anomalous behavior. Analytics module 110 can
analyze the frequency and severity of the anomalous behavior to
determine a reputation score for the component using reputation
module 162. Analytics module 110 can use the reputation score of a
component to selectively enforce policies. For example, if a
component has a high reputation score, the component may be
assigned a more permissive policy or more permissive policies;
while if the component frequently violates (or attempts to violate)
its relevant policy or policies, its reputation score may be
lowered and the component may be subject to a stricter policy or
stricter policies. Reputation module 162 can correlate observed
reputation score with characteristics of a component. For example,
a particular virtual machine with a particular configuration may be
more prone to misconfiguration and receive a lower reputation
score. When a new component is placed in the network, analytics
module 110 can assign a starting reputation score similar to the
scores of similarly configured components. The expected reputation
score for a given component configuration can be sourced outside of
the datacenter. A network administrator can be presented with
expected reputation scores for various components before
installation, thus assisting the network administrator in choosing
components and configurations that will result in high reputation
scores.
[0036] Some anomalous behavior can be indicative of a misconfigured
component or a malicious attack. Certain attacks may be easy to
detect if they originate outside of the datacenter, but can prove
difficult to detect and isolate if they originate from within the
datacenter. One such attack could be a distributed denial of
service (DDOS) where a component or group of components attempt to
overwhelm another component with spurious transmissions and
requests. Detecting an attack or other anomalous network traffic
can be accomplished by comparing the expected network conditions
with actual network conditions. For example, if a traffic flow
varies from its historical signature (packet size, transport
control protocol header options, etc.) it may be an attack.
[0037] In some cases, a traffic flow may be expected to be reported
by a sensor, but the sensor may fail to report it. This situation
could be an indication that the sensor has failed or become
compromised. By comparing the network traffic flow data from
multiple sensors 104 spread throughout the datacenter, analytics
module 110 can determine if a certain sensor is failing to report a
particular traffic flow.
[0038] Presentation module 116 can include serving layer 118,
authentication module 120, web front end 122, public alert module
124, and third party tools 126. In some example embodiments,
presentation module 116 can provide an external interface for
network monitoring system 100. Using presentation module 116, a
network administrator, external software, etc. can receive data
pertaining to network monitoring system 100 via a webpage,
application programming interface (API), audiovisual queues, etc.
In some example embodiments, presentation module 116 can preprocess
and/or summarize data for external presentation. In some example
embodiments, presentation module 116 can generate a webpage. As
analytics module 110 processes network traffic flow data and
generates analytic data, the analytic data may not be in a
human-readable form or it may be too large for an administrator to
navigate. Presentation module 116 can take the analytic data
generated by analytics module 110 and further summarize, filter,
and organize the analytic data as well as create intuitive
presentations of the analytic data.
[0039] Serving layer 118 can be the interface between presentation
module 116 and analytics module 110. As analytics module 110
generates reports, predictions, and conclusions, serving layer 118
can summarize, filter, and organize the information that comes from
analytics module 110. In some example embodiments, serving layer
118 can also request raw data from a sensor or collector.
[0040] Web frontend 122 can connect with serving layer 118 to
present the data from serving layer 118 in a webpage. For example,
web frontend 122 can present the data in bar charts, core charts,
tree maps, acyclic dependency maps, line graphs, tables, etc. Web
frontend 122 can be configured to allow a user to "drill down" on
information sets to get a filtered data representation specific to
the item the user wishes to drill down to. For example, individual
traffic flows, components, etc. Web frontend 122 can also be
configured to allow a user to filter by search. This search filter
can use natural language processing to analyze the user's input.
There can be options to view data relative to the current second,
minute, hour, day, etc. Web frontend 122 can allow a network
administrator to view traffic flows, application dependency maps,
network topology, etc.
[0041] In some example embodiments, web frontend 122 may be solely
configured to present information. In other example embodiments,
web frontend 122 can receive inputs from a network administrator to
configure network traffic monitoring system 100 or components of
the datacenter. These instructions can be passed through serving
layer 118 to be sent to configuration and image manager 102 or
policy engine 112. Authentication module 120 can verify the
identity and privileges of users. In some example embodiments,
authentication module 120 can grant network administrators
different rights from other users according to established
policies.
[0042] Public alert module 124 can identify network conditions that
satisfy specified criteria and push alerts to third party tools
126. Public alert module 124 can use analytic data generated or
accessible through analytics module 110. One example of third party
tools 126 is a security information and event management system
(SIEM). Third party tools 126 may retrieve information from serving
layer 118 through an API and present the information according to
the SIEM's user interfaces.
[0043] FIG. 2 illustrates an example network environment 200
according to some example embodiments. It should be understood
that, for the network environment 100 and any environment discussed
herein, there can be additional or fewer nodes, devices, links,
networks, or components in similar or alternative configurations.
Example embodiments with different numbers and/or types of clients,
networks, nodes, cloud components, servers, software components,
devices, virtual or physical resources, configurations, topologies,
services, appliances, deployments, or network devices are also
contemplated herein. Further, network environment 200 can include
any number or type of resources, which can be accessed and utilized
by clients or tenants. The illustrations and examples provided
herein are for clarity and simplicity.
[0044] Network environment 200 can include network fabric 212,
layer 2 (L2) network 206, layer 3 (L3) network 208, endpoints 210a,
210b, . . . , and 210d (collectively, "204"). Network fabric 212
can include spine switches 202a, 202b, . . . , 202n (collectively,
"202") connected to leaf switches 204a, 204b, 204c, . . . , 204n
(collectively, "204"). Spine switches 202 can connect to leaf
switches 204 in network fabric 212. Leaf switches 204 can include
access ports (or non-fabric ports) and fabric ports. Fabric ports
can provide uplinks to spine switches 202, while access ports can
provide connectivity for devices, hosts, endpoints, VMs, or other
electronic devices (e.g., endpoints 204), internal networks (e.g.,
L2 network 206), or external networks (e.g., L3 network 208).
[0045] Leaf switches 204 can reside at the edge of network fabric
212, and can thus represent the physical network edge. In some
cases, leaf switches 204 can be top-of-rack switches configured
according to a top-of-rack architecture. In other cases, leaf
switches 204 can be aggregation switches in any particular
topology, such as end-of-row or middle-of-row topologies. Leaf
switches 204 can also represent aggregation switches, for
example.
[0046] Network connectivity in network fabric 212 can flow through
leaf switches 204. Here, leaf switches 204 can provide servers,
resources, VMs, or other electronic devices (e.g., endpoints 210),
internal networks (e.g., L2 network 206), or external networks
(e.g., L3 network 208), access to network fabric 212, and can
connect leaf switches 204 to each other. In some example
embodiments, leaf switches 204 can connect endpoint groups (EPGs)
to network fabric 212, internal networks (e.g., L2 network 206),
and/or any external networks (e.g., L3 network 208). EPGs can be
used in network environment 200 for mapping applications to the
network. In particular, EPGs can use a grouping of application
endpoints in the network to apply connectivity and policy to the
group of applications. EPGs can act as a container for buckets or
collections of applications, or application components, and tiers
for implementing forwarding and policy logic. EPGs also allow
separation of network policy, security, and forwarding from
addressing by instead using logical application boundaries. For
example, each EPG can connect to network fabric 212 via leaf
switches 204.
[0047] Endpoints 210 can connect to network fabric 212 via leaf
switches 204. For example, endpoints 210a and 210b can connect
directly to leaf switch 204a, which can connect endpoints 210a and
210b to network fabric 212 and/or any other one of leaf switches
204. Endpoints 210c and 210d can connect to leaf switch 204b via L2
network 206. Endpoints 210c and 210d and L2 network 206 are
examples of LANs. LANs can connect nodes over dedicated private
communications links located in the same general physical location,
such as a building or campus.
[0048] Wide area network (WAN) 212 can connect to leaf switches
204c or 204d via L3 network 208. WANs can connect geographically
dispersed nodes over long-distance communications links, such as
common carrier telephone lines, optical lightpaths, synchronous
optical networks (SONET), or synchronous digital hierarchy (SDH)
links. LANs and WANs can include layer 2 (L2) and/or layer 3 (L3)
networks and endpoints.
[0049] The Internet is an example of a WAN that connects disparate
networks throughout the world, providing global communication
between nodes on various networks. 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
can refer to 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. Endpoints 210 can include any
communication device or component, such as a computer, server,
hypervisor, virtual machine, container, process (e.g., running on a
virtual machine), switch, router, gateway, host, device, external
network, etc. In some example embodiments, endpoints 210 can
include a server, hypervisor, process, or switch configured with
virtual tunnel endpoint (VTEP) functionality which connects an
overlay network with network fabric 212. The overlay network may
allow virtual networks to be created and layered over a physical
network infrastructure. Overlay network protocols, such as Virtual
Extensible LAN (VXLAN), Network Virtualization using Generic
Routing Encapsulation (NVGRE), Network Virtualization Overlays
(NVO3), and Stateless Transport Tunneling (STT), can provide a
traffic encapsulation scheme which allows network traffic to be
carried across L2 and L3 networks over a logical tunnel. Such
logical tunnels can be originated and terminated through VTEPs. The
overlay network can host physical devices, such as servers,
applications, endpoint groups, virtual segments, virtual workloads,
etc. In addition, endpoints 210 can host virtual workload(s),
clusters, and applications or services, which can connect with
network fabric 212 or any other device or network, including an
internal or external network. For example, endpoints 210 can host,
or connect to, a cluster of load balancers or an EPG of various
applications.
[0050] Network environment 200 can also integrate a network traffic
monitoring system, such as the one shown in FIG. 1. For example, as
shown in FIG. 2, the network traffic monitoring system can include
sensors 104a, 104b, . . . , 104n (collectively, "104"), collectors
108a, 108b, . . . 108n (collectively, "108"), and analytics module
110. In some example embodiments, spine switches 202 do not have
sensors 104. Analytics module 110 can receive and process network
traffic data collected by collectors 108 and detected by sensors
104 placed on nodes located throughout network environment 200. In
some example embodiments, analytics module 110 can be implemented
in an active-standby model to ensure high availability, with a
first analytics module functioning in a primary role and a second
analytics module functioning in a secondary role. If the first
analytics module fails, the second analytics module can take over
control. Although analytics module 110 is shown to be a standalone
network appliance in FIG. 2, it will be appreciated that analytics
module 110 can also be implemented as a VM image that can be
distributed onto a VM, a cluster of VMs, a software as a service
(SaaS), or other suitable distribution model in various other
example embodiments. In some example embodiments, sensors 104 can
run on endpoints 210, leaf switches 204, spine switches 202,
in-between network elements (e.g., sensor 104h), etc. In some
example embodiments, leaf switches 204 can each have an associated
collector 108. For example, if leaf switch 204 is a top of rack
switch then each rack can contain an assigned collector 108.
[0051] Although network fabric 212 is illustrated and described
herein as an example leaf-spine architecture, one of ordinary skill
in the art will readily recognize that the subject technology can
be implemented based on any network topology, including any data
center or cloud network fabric. Indeed, other architectures,
designs, infrastructures, and variations are contemplated herein.
For example, the principles disclosed herein are applicable to
topologies including three-tier (including core, aggregation, and
access levels), fat tree, mesh, bus, hub and spoke, etc. It should
be understood that sensors and collectors can be placed throughout
the network as appropriate according to various architectures.
[0052] FIG. 3 shows an example method 300 for determining the
chronological ordering of two events according to various
embodiments. Example method 300 begins with determining a minimum
and maximum latency associated with a first sensor (step 302). The
minimum latency and the maximum latency between collector 108 and
sensor 104 can be calculated by one of sensor 104 or collector 108
sending a message to the other device (collector 108 or sensor 104)
and calculating the time it takes for a response. The latency of
the connection can be half the difference between the time the
message was sent and the time the message was received. In some
embodiments, over the course of normal operation collector 108
sends messages to sensor 104 and can calculate the minimum latency
and maximum latency based on the latency of those messages and
responses. Alternatively or additionally, collector 108 can send
special messages such as a "ping" (i.e., an Internet Control
Message Protocol echo request) specifically to calculate a minimum
latency and maximum latency. In some embodiments, the minimum
latency is assumed to be 0. In some embodiments, network traffic
monitoring system 100 receives an input specifying a minimum
latency for a connection to a sensor 104 or a default minimum
latency for all connections. In some embodiments, the maximum and
minimum latency are provided by sensor 104. In some embodiments,
the latencies incorporate allowances for sensor 104 processing
(i.e., if sensor 104 must process packets before sending a report
to collector 108).
[0053] In some embodiments, the minimum latency and/or the maximum
latency are estimated using or informed by a network graph which
contains known or estimated latencies between network components.
For example, without experimentally determining the minimum latency
or the maximum latency, network traffic monitoring system 100 can
estimate those latencies by estimating a path that a flow would
take between collector 108 and sensor 104 and can, using previously
determined the latencies of individual links within the flow,
determine estimated minimum latency and maximum latency of the
flow.
[0054] In some embodiments, the maximum latency is informed by
varying timestamp to collector 108 received time differences. For
example, if an event report with a timestamp of 30 (arbitrary time
units) is first received at collector 108 time of 31 and then a
later event report with a timestamp of 50 is received at collector
108 time of 61, then the maximum latency can be at least 10
([61-50]-[31-30]). This maximum latency value can be useful even if
the associated timestamp values are not trusted for global
accuracy.
[0055] It should be understood that "latency" as used herein
includes network latency as well as processing delays and other
delays caused by physical or arbitrary limitations.
[0056] Using similar techniques as explained above in step 302,
example method 300 can continue by determining a minimum and
maximum latency associated with a second sensor (step 304).
[0057] Example method 300 can continue by receiving, at a first
time and from a first sensor, a first event report describing a
first event (step 306). An event can be a flow event such as a flow
being initiated, received, rejected, ended, etc. An event can also
include other data center, endpoint, or application, events (such
as an process starting or freezing, an endpoint opening a port, a
power outage, a bandwidth spike, an attack, etc.). An event can be
a summary of other events. For example, in a DDoS situation,
numerous attacking endpoints might attempt to communicate with a
target endpoint; a sensor on each of the attacking endpoints can
send an event report describing the various DDoS communications; a
summary event can then be created describing that a DDoS attack
occurred in the network.
[0058] An event report can include a summary of the event or a
detailed report of an event. A summary of an event can be
preprocessed by a sensor 104 to remove extraneous information or
include extra (e.g., summary) information. In some embodiments, the
event report includes a timestamp of the event, according to
associated sensor's 104 clock. In some embodiments, sensor 104
sends reports to collector 108 immediately after the event occurs;
alternatively, sensor 104 sends reports to collector 108
periodically and the reports can describe multiple events that
occurred over the period.
[0059] Similar to step 306, Example method 300 can continue by
receiving, at a second time and from a second sensor, a second
event report describing a second event (step 308).
[0060] Example method 300 can continue by determining whether the
first event caused the second event (step 310). For example, if the
first event is a packet being sent from an endpoint associated with
the first sensor and the second event is that same packet being
received from an endpoint associated with the second sensor, then
the first event caused the second event. The causal relation
between the two events can be determined using other means as well;
for example, analytics module 110 can determine that the first
event was a trigger for the second event as might occur when the
second event is a DDoS attack and the first event is the command
and control message initiating the attack. If step 310 is "no",
example method 300 can continue at step 312 and, similar to step
310, determine whether the second event caused the first event.
[0061] Step 314 follows a negative result at step 312 by
determining if the first time minus the minimum latency for the
first sensor is earlier than the second time minus the maximum
latency for the second sensor. In some embodiments, this includes
converting the times and latencies to integer values and
determining which value is greater (i.e., earlier). Step 320 can
follow a "no" result at step 314 and can include, similar to step
314, determining whether the second time minus the minimum latency
for the second sensor is earlier than the first time minus the
maximum latency for the first sensor.
[0062] In some embodiments, the system can attempt to find that one
event (e.g., the first event) likely caused the another event
(e.g., the second event) by determining that the times of the
events are sufficiently close (e.g., within calculated or
experimentally determined sensor-to-sensor latency bounds). The
system can further determine that one event likely caused the other
if the two events are complimentary in type; for example, if the
first event is a flow being initialized (or a packet, frame,
stream, etc. being sent) and the second event is a flow being
received (or a packet, frame, stream, etc. being received). This
can be useful if a packet or flow is translated (or otherwise
obfuscated) as it traverses the network.
[0063] If a first event precedes a second event, in some
embodiments, this means that the second event did not cause the
first event. For example, if the first event is an attack, and the
system is attempting to find the initial cause of the attack (e.g.,
a command and control signal), then if the second event is
subsequent to the attack, then it likely did not cause the
attack.
[0064] Following a negative result at step 320, network traffic
monitoring system 100 can determine that the chronology between the
first event and the second event is ambiguous (step 322). In other
words, it cannot be determined based on the data available that
either event preceded the other or that the events were
simultaneous. In some embodiments, instead of having maximum and
minimum latencies, the system can determine probability density
functions for the latencies associated with the first and second
sensors. Network traffic monitoring system 100 can then determine,
using the two probability density functions, the likelihood that
one event preceded the other.
[0065] If either step 310 or step 314 result in "yes," then example
method 300 can continue by determining that the first event
preceded the second event (step 316); similarly, if either step 312
or step 320 result in "yes," then example method 300 can continue
by determining that the second event preceded the first event (step
318). After either step 316 or step 318, example method 300 can
then end.
[0066] In some embodiments, after determining that the first event
preceded the second event; the system can then determine whether
the first event likely caused the second event. For example, the
system can determine if the first time minus the maximum latency of
the first sensor is close to the second time minus the minimum
latency of the second sensor. In other words, the system can
determine if the maximum possible difference between the actual
times that the two events occurred is within a predetermined
threshold. Other statistical models can be utilized to make a
determination that the two events likely occurred within a
predefined time window. In some embodiments, "causation" between
events requires a different window based on the type of events. For
example, in some attacks, the causal event should be within a
narrow window (e.g., a few milliseconds) whereas in other events,
the causal event might be within a broader window (e.g., a few
hours).
[0067] In some embodiments, the system can also determine whether
the one event caused another event by comparing the types of events
of the two events. For example, if one event is sending a message
and the other event is receiving the message (or a message that
fits a description of the first message, although a direct match
might be uncertain). Other examples of corresponding types of
events can include a command and control signal being sent and an
attack commencing, a flow being started and the flow being
rejected, etc.
[0068] In some embodiments, the first event and the second event
can be simultaneous. For example, the events can be receiving a
timing signal from a universal clock (e.g., over a dedicated timing
interface) received by the first and second sensors.
[0069] Although example method 300 concerns events from two
different sensors 104; various techniques can be used to determine
the chronology of two events from one sensor. For example, event
reports can include a sequence number such that an event report
with a later sequence number describes a later event in comparison
to an event described in an event report with a lower sequence
number. Another example includes where event reports include a
timestamp of the events. Although the event timestamps might not
necessarily be trusted, they can be used to determine the relative
chronology for two events detected by the same sensor 104.
Similarly, the time of receipt of the report by the collector 108
can inform the chronological ordering of two events from the same
sensor, even if such ordering can be unreliable as one report may
be delayed in transit. In some embodiments, a combination of
techniques can be combined to determine a likely chronological
ordering of events from a single senor 104 or a combination of
sensors 108.
[0070] In some embodiments, a collector 108 can determine a
sensor's 104 estimated clock skew based on a timestamp on a
received communication, the collector's 108 clock when the
communication is received, and the maximum and minimum latencies
between the sensor 104 and collector 108. Example equation for this
can be:
Skew.sub.upperBound=-Time.sub.collector+Time.sub.sensor+Latency.sub.max
and
Skew.sub.lowerBound=-Time.sub.collector+Time.sub.sensor+Latency.sub.m-
in. Applying these equations to an example, if the collector 108
receives a communication at time 76 (using arbitrary units of time
for simplicity of explanation) bearing a timestamp of 51 and the
latency between the collector 108 and sensor 104 varies from a
maximum of 15 to a minimum of 3, then the clock skew is likely from
-10 to -22 relative to the collector's 108 clock. Thus, a later
communication from the same sensor 104 bearing a timestamp of 48
(sensor 104 time) can be translated to 58-70 (collector 108 time).
This approach can be useful when receiving delayed event reports
from sensors 104; for example, if a sensor 104 gathers event data
over a period and then sends a report describing the events that
happened during that period.
[0071] FIG. 4A shows an example graphical representation of the
timing of an event 401 according to various embodiments and used in
later figures. In some embodiments, network traffic monitoring
system 100 can determine event window 402 based on the report
received time 408 (i.e., at collector 108), minimum latency 406,
and maximum latency 407. Event window 302 can be the estimated time
(e.g., according to collector's 108 clock) calculated based on a
minimum latency 306 and a maximum latency 407. For example, the
earliest event time 403 can be calculated by subtracting the
maximum latency 407 from report received time 408 while the latest
event time 404 can be calculated by subtracting the minimum latency
406 from the report received time 408. Line 410 indicates the
relative chronology of occurrences, occurrences to the right are
more recent than occurrences to the left.
[0072] FIG. 4B depicts an example timeline 420 according to various
embodiments. Various events 401 can correspond to sensors reporting
from various endpoints. For example, events 401.sub.a1, 401.sub.a2,
and 401.sub.a3 can correspond to Endpoint A; events 401.sub.b1,
401.sub.b2, and 401.sub.b3 can correspond to Endpoint B; and events
401.sub.c1, 401.sub.c2, and 401.sub.c3 can correspond to Endpoint
C.
[0073] Referring to the event windows of event 401.sub.a1 and event
401.sub.a2, the latest event time of event 401.sub.a1 precedes the
earliest event time of event 401.sub.a2. Thus, network monitoring
system 100 can determine that event 401.sub.a1 preceded event
401.sub.a2 (see steps 314 and 316). In contrast, the latest time of
event 401.sub.b1 is after the earliest time of event 401.sub.b2
while the latest time of event 401.sub.b2 is after the earliest
time of event 401.sub.b1 (in other words, their event windows 402
overlap); thus network monitoring system 100 can determine that the
chronology of the two events is ambiguous (see steps 314, 320, and
322). Applying the principles in example method 300, network
monitoring system 100 can determine that event 401.sub.a1 precedes
event 401.sub.a2 which both precede event 401.sub.a3.
[0074] FIGS. 5A-5E show example chronological graphs 501-505
according to various embodiments. Nodes A1-A3, B1-B3, and C1-C3 can
correspond to respective events 401.sub.a1-401.sub.a3,
401.sub.b1-401.sub.b3, and 401.sub.c1-401.sub.c3 of FIG. 4B while
nodes D1-D3 and E1-E3 correspond to events 401 not otherwise
depicted. In FIGS. 5A-5E, an arrow can denote chorological
precedence; e.g., that node A1 precedes node A2 in graph 501.
Chorological precedence can be determined by applying example
method 300 on various events 401 or through other means. A double
arrow such as exists between nodes A2 and B2 in graph 502 can
represent simultaneity (denoted herein with "="). Simultaneity can
occur, for example, if two events 401 are in fact duplicate entries
for one event (e.g., if received on two different collectors 108 or
described in different contexts) or by using a dedicated timing
interface.
[0075] Graphs 501-504 can represent various graph segments (e.g.,
from various collectors 108, methods, time periods, etc.). Graphs
501-505 can be directed acyclic graphs, meaning they represent
strict precedence. For example in graph 501, because A1 precedes A2
(denoted using ">" herein) and A2>B1>B2>B3, then B3
cannot precede A1. Alternatively, the chronological precedence of
events 408 can be probabilistic, which can result in a probability
that one event 408 might precede another 408. For example, B3 might
precede A1 in graph 501, according to the combined probabilities
that each association is incorrect in the chain of
A1>A2>B1>B2>B3, or some other statistical
calculation.
[0076] Graphs can be combined; for example, graphs 501-504 can be
combined to form graph 505. In some embodiments, the chronological
precedence of two graphs may disagree. For example, graph 501
represents that A2>B1>B2 while graph 502 represents that
A2=B2; this situation creates a "cycle" (A2>B1>B2=A2>B1 .
. . ). Various conflict-resolution techniques can be employed to
overcome such inconsistencies. For example, network traffic
monitoring system 100 can determine probabilities that
relationships are accurate and compare the probabilities; for
example, A2>B1 in graph 501 might have a 99% confidence level
while A2=B2 in graph 502 might have a 20% confidence level, the
system can then disregard the A2=B2 association. In some
embodiments, every relationship of the cycle can be disregarded
(e.g., each of A2>B1, B1>B2, and A2=B2).
[0077] FIG. 6A and FIG. 6B illustrate example system embodiments.
The more appropriate embodiment will be apparent to those of
ordinary skill in the art when practicing the present technology.
Persons of ordinary skill in the art will also readily appreciate
that other system embodiments are possible.
[0078] FIG. 6A illustrates a conventional system bus computing
system architecture 600 wherein the components of the system are in
electrical communication with each other using a bus 605. Example
system 600 includes a processing unit (CPU or processor) 610 and a
system bus 605 that couples various system components including the
system memory 615, such as read only memory (ROM) 670 and random
access memory (RAM) 675, to the processor 610. The system 600 can
include a cache of high-speed memory connected directly with, in
close proximity to, or integrated as part of the processor 610. The
system 600 can copy data from the memory 615 and/or the storage
device 630 to the cache 612 for quick access by the processor 610.
In this way, the cache can provide a performance boost that avoids
processor 610 delays while waiting for data. These and other
modules can control or be configured to control the processor 610
to perform various actions. Other system memory 615 may be
available for use as well. The memory 615 can include multiple
different types of memory with different performance
characteristics. The processor 610 can include any general purpose
processor and a hardware module or software module, such as module
1 637, module 7 634, and module 3 636 stored in storage device 630,
configured to control the processor 910 as well as a
special-purpose processor where software instructions are
incorporated into the actual processor design. The processor 610
may essentially be a completely self-contained computing system,
containing multiple cores or processors, a bus, memory controller,
cache, etc. A multi-core processor may be symmetric or
asymmetric.
[0079] To enable user interaction with the computing device 600, an
input device 645 can represent any number of input mechanisms, such
as a microphone for speech, a touch-sensitive screen for gesture or
graphical input, keyboard, mouse, motion input, speech and so
forth. An output device 635 can also be one or more of a number of
output mechanisms known to those of skill in the art. In some
instances, multimodal systems can enable a user to provide multiple
types of input to communicate with the computing device 600. The
communications interface 640 can generally govern and manage the
user input and system output. There is no restriction on operating
on any particular hardware arrangement and therefore the basic
features here may easily be substituted for improved hardware or
firmware arrangements as they are developed.
[0080] Storage device 630 is a non-volatile memory and can be a
hard disk or other types of computer readable media which can store
data that are accessible by a computer, such as magnetic cassettes,
flash memory cards, solid state memory devices, digital versatile
disks, cartridges, random access memories (RAMs) 675, read only
memory (ROM) 670, and hybrids thereof.
[0081] The storage device 630 can include software modules 637,
634, 636 for controlling the processor 610. Other hardware or
software modules are contemplated. The storage device 630 can be
connected to the system bus 605. In one aspect, a hardware module
that performs a particular function can include the software
component stored in a computer-readable medium in connection with
the necessary hardware components, such as the processor 610, bus
605, display 635, and so forth, to carry out the function.
[0082] FIG. 6B illustrates an example computer system 650 having a
chipset architecture that can be used in executing the described
method and generating and displaying a graphical user interface
(GUI). Computer system 650 is an example of computer hardware,
software, and firmware that can be used to implement the disclosed
technology. System 650 can include a processor 655, representative
of any number of physically and/or logically distinct resources
capable of executing software, firmware, and hardware configured to
perform identified computations. Processor 655 can communicate with
a chipset 660 that can control input to and output from processor
655. In this example, chipset 660 outputs information to output
665, such as a display, and can read and write information to
storage device 670, which can include magnetic media, and solid
state media, for example. Chipset 660 can also read data from and
write data to RAM 675. A bridge 680 for interfacing with a variety
of user interface components 685 can be provided for interfacing
with chipset 660. Such user interface components 685 can include a
keyboard, a microphone, touch detection and processing circuitry, a
pointing device, such as a mouse, and so on. In general, inputs to
system 650 can come from any of a variety of sources, machine
generated and/or human generated.
[0083] Chipset 660 can also interface with one or more
communication interfaces 690 that can have different physical
interfaces. Such communication interfaces can include interfaces
for wired and wireless local area networks, for broadband wireless
networks, as well as personal area networks. Some applications of
the methods for generating, displaying, and using the GUI disclosed
herein can include receiving ordered datasets over the physical
interface or be generated by the machine itself by processor 655
analyzing data stored in storage 670 or 675. Further, the machine
can receive inputs from a user via user interface components 685
and execute appropriate functions, such as browsing functions by
interpreting these inputs using processor 655.
[0084] It can be appreciated that example systems 600 and 650 can
have more than one processor 610 or be part of a group or cluster
of computing devices networked together to provide greater
processing capability.
[0085] For clarity of explanation, in some instances the present
technology may be presented as including individual functional
blocks including functional blocks comprising devices, device
components, steps or routines in a method embodied in software, or
combinations of hardware and software.
[0086] In some embodiments the computer-readable storage devices,
mediums, and memories can include a cable or wireless signal
containing a bit stream and the like. However, when mentioned,
non-transitory computer-readable storage media expressly exclude
media such as energy, carrier signals, electromagnetic waves, and
signals per se.
[0087] Methods according to the above-described examples can be
implemented using computer-executable instructions that are stored
or otherwise available from computer readable media. Such
instructions can comprise, for example, instructions and data which
cause or otherwise configure a general purpose computer, special
purpose computer, or special purpose processing device to perform a
certain function or group of functions. Portions of computer
resources used can be accessible over a network. The computer
executable instructions may be, for example, binaries, intermediate
format instructions such as assembly language, firmware, or source
code. Examples of computer-readable media that may be used to store
instructions, information used, and/or information created during
methods according to described examples include magnetic or optical
disks, flash memory, USB devices provided with non-volatile memory,
networked storage devices, and so on.
[0088] Devices implementing methods according to these disclosures
can comprise hardware, firmware and/or software, and can take any
of a variety of form factors. Typical examples of such form factors
include laptops, smart phones, small form factor personal
computers, personal digital assistants, rackmount devices,
standalone devices, and so on. Functionality described herein also
can be embodied in peripherals or add-in cards. Such functionality
can also be implemented on a circuit board among different chips or
different processes executing in a single device, by way of further
example.
[0089] The instructions, media for conveying such instructions,
computing resources for executing them, and other structures for
supporting such computing resources are means for providing the
functions described in these disclosures.
[0090] Although a variety of examples and other information was
used to explain aspects within the scope of the appended claims, no
limitation of the claims should be implied based on particular
features or arrangements in such examples, as one of ordinary skill
would be able to use these examples to derive a wide variety of
implementations. Further and although some subject matter may have
been described in language specific to examples of structural
features and/or method steps, it is to be understood that the
subject matter defined in the appended claims is not necessarily
limited to these described features or acts. For example, such
functionality can be distributed differently or performed in
components other than those identified herein. Rather, the
described features and steps are disclosed as examples of
components of systems and methods within the scope of the appended
claims. Moreover, claim language reciting "at least one of" a set
indicates that one member of the set or multiple members of the set
satisfy the claim.
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