Dynamic baseline determination for distributed business transaction

Patent metadata

Field	Value
Publication number	US-10230611-B2
Application number	US-201514929271-A
Country	US
Kind code	B2
Filing date	Oct 31, 2015
Priority date	Sep 10, 2009
Publication date	Mar 12, 2019
Grant date	Mar 12, 2019

Abstract

The present technology may determine an anomaly in a portion of a distributed business application. Data can automatically be captured and analyzed for the portion of the application associated with the anomaly. By automatically capturing data for just the portion associated with the anomaly, the present technology reduces the resource and time requirements associated with other code-based solutions for monitoring transactions. A method for performing a diagnostic session for a request may begin with initiating collection of diagnostic data associated with a request. An application thread on each of two or more servers may be sampled. The application threads may be associated with the same business transaction and the business transaction may be associated with the request. The diagnostic data may be stored.

First claim

What is claimed is:

1. A method for monitoring an application, the method comprising:
monitoring, by an agent on a machine, a distributed business transaction associated with a plurality of distributed applications executing across a computer network, wherein monitoring includes collecting runtime data associated with the distributed business transaction;
based on the runtime data, determining, by the agent, a performance baseline for handling an application request associated with the distributed business transaction;
comparing, by the agent, the runtime data outliers to the performance baseline to identify an anomaly in the distributed business transaction, wherein a number of outliers occurring for a business transaction within a particular time window is compared to a baseline of outlier occurrence for the distributed business transaction, wherein a behavior of the distributed business transaction is learned based on the comparison over time;
upon identifying the anomaly, automatically triggering a diagnostic session at the machine to collect one or more diagnostic parameters about the distributed business transaction based on the learned behavior of the distributed business transaction;
continually updating the performance baseline based on subsequent monitoring of requests associated with the application; and
based on the collected diagnostic parameters, causing, by the agent, a model of a distributed business transaction flow to be generated, wherein a flow map of the one or more distributed business transactions is generated that includes a map of applications or virtual machines that make up the one or more distributed business transactions associated with the diagnostic session triggered by the anomaly.

2. The method of claim 1 , wherein determining the performance baseline includes determining a rate of outliers.

3. The method of claim 1 , further comprising:
receiving business transaction name information; and
receiving call chain information.

4. The method of claim 1 , further comprising, displaying with the model, a status of the anomaly, a duration of the anomaly, and a name of the distributed business transaction.

5. The method of claim 1 , further comprising, displaying with the model, a relationship between the applications or virtual machines that make up the distributed business transaction.

6. The method of claim 1 , wherein causing the model to be generated includes generating a call graph.

7. The method of claim 6 , further comprising, displaying with the call graph, a name of a corresponding application called and a time at which the called application executed.

8. The method of claim 1 , further comprising, dynamically reporting an anomaly rate through an interface based on the identified anomalies and continually updated baseline.

9. A non-transitory computer readable storage medium having embodied thereon a program, the program being executable by a processor to perform a method for monitoring a garbage collection process, the method comprising:
monitoring, by an agent on a machine, a distributed business transaction associated with a plurality of distributed applications executing across a computer network, wherein monitoring includes collecting runtime data associated with the distributed business transaction;
based on the runtime data, determining, by the agent, a performance baseline for handling an application request associated with the distributed business transaction;
comparing, by the agent, the runtime data outliers to the performance baseline to identify an anomaly in the distributed business transaction, wherein a number of outliers occurring for a business transaction within a particular time window is compared to a baseline of outlier occurrence for the distributed business transaction, wherein a behavior of the distributed business transaction is learned based on the comparison over time;
upon identifying the anomaly, automatically triggering a diagnostic session at the machine to collect one or more diagnostic parameters about the distributed business transaction based on the learned behavior of the distributed business transaction;
continually updating the performance baseline based on subsequent monitoring of requests associated with the application; and
based on the collected diagnostic parameters, causing, by the agent, a model of the distributed business transaction flow to be generated, wherein a flow map of the one or more distributed business transactions is generated that includes a map of applications or virtual machines that make up the one or more distributed business transactions associated with the diagnostic session triggered by the anomaly.

10. The non-transitory computer readable storage medium of claim 9 , wherein determining the performance baseline includes determining a rate of outliers.

11. The non-transitory computer readable storage medium of claim 9 , the method further comprising:
receiving business transaction name information; and
receiving call chain information.

12. The non-transitory computer readable storage medium of claim 9 , the method further comprising, displaying with the model, a status of the anomaly, a duration of the anomaly, and a name of the distributed business transaction.

13. The non-transitory computer readable storage medium of claim 9 , the method further comprising, displaying with the model, a relationship between the applications or virtual machines that make up the distributed business transaction.

14. The non-transitory computer readable storage medium of claim 9 , wherein causing the model to be generated includes generating a call graph.

15. The non-transitory computer readable storage medium of claim 14 , the method further comprising, displaying with the call graph, a name of a corresponding application called and a time at which the called application executed.

16. The non-transitory computer readable storage medium of claim 9 , the method further comprising, dynamically reporting an anomaly rate through an interface based on the identified anomalies and continually updated baseline.

17. A system for monitoring a distributed application, comprising:
a processor configured to execute a process; and
a memory configured to store program instructions which contain the process executable by the processor, the process configured to:
monitor, by an agent executing on the apparatus, a distributed business transaction associated with a plurality of distributed applications executing across a computer network, wherein monitoring includes collecting runtime data associated with the distributed business transaction;
based on the runtime data, determine a performance baseline for handling an application request associated with the distributed business transaction;
compare the runtime data outliers to the performance baseline to identify an anomaly in the distributed business transaction, wherein a number of outliers occurring for a business transaction within a particular time window is compared to a baseline of outlier occurrence for the distributed business transaction, wherein a behavior of the distributed business transaction is learned based on the comparison over time;
upon identifying the anomaly, automatically trigger a diagnostic session at the machine to collect one or more diagnostic parameters about the distributed business transaction based on the learned behavior of the distributed business transaction;
continually update the performance baseline based on subsequent monitoring of requests associated with the application; and
based on the collected diagnost

Assignees

• Appdynamics Llc
• Cisco Tech Inc

Inventors

• Bansal Jyoti
• Sunkara Bhaskar

Classifications

• H04L43/16

  Threshold monitoring · CPC title

• H04L43/0817

  by checking functioning · CPC title

• H04L41/142

  using statistical or mathematical methods · CPC title

• H04L43/045

  for graphical visualisation of monitoring data · CPC title

• H04L43/022

  by sampling · CPC title