Problem Determination and Diagnosis in Shared Dynamic Clouds

• US 20130305092A1
• Filed: 05/14/2012
• Published: 11/14/2013
• Est. Priority Date: 05/14/2012
• Status: Active Grant

• Current Assignee: SINOEAST CONCEPT LIMITED (Tencent Holdings Limited)
• Original Assignee: International Business Machines Corporation
• Inventors: Jayachandran, Praveen, Sharma, Bikash, Verma, Akshat
• Granted Patent: US 8,862,727 B2
• Time in Patent Office: Days
• US Class Current: 714/37
• CPC Class Codes: G06F 11/0709 in a distributed system consisting of a plurality of standalone computer nodes, e.g. clusters, client-server systems
• CPC Class Codes: G06F 11/0754 by exceeding limits
• CPC Class Codes: G06F 11/079 Root cause analysis, i.e. error or fault diagnosis in a hardware test environment G06F11/22; in a software test environment G06F11/36
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3495 for systems
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/815 Virtual
• CPC Class Codes: H04L 41/0613 based on the type or category of the network elements
• CPC Class Codes: H04L 41/0631 using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
• CPC Class Codes: H04L 41/5096 wherein the managed service relates to distributed or central networked applications

Abstract

A method, an apparatus and an article of manufacture for problem determination and diagnosis in a shared dynamic cloud environment. The method includes monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric, identifying a symptom of a problem and generating an event based on said monitoring, analyzing the event to determine a deviation from normal behavior, and classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

Claims

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14. An article of manufacture comprising a computer readable storage medium having computer readable instructions tangibly embodied thereon which, when implemented, cause a computer to carry out a plurality of method steps comprising:

monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric;

identifying a symptom of a problem and generating an event based on said monitoring;

analyzing the event to determine a deviation from normal behavior; and

classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

15. The article of manufacture of claim 14, wherein said monitoring comprises outputting a stream of data-points, each data-point being derived from a monitoring data time series, corresponding to each system and application metric in the shared dynamic cloud environment.

16. The article of manufacture of claim 14, wherein the method steps comprise:

building a model of normal application behavior by leveraging a machine learning technique; and

using the model to detect a deviation in the monitored data from normal behavior.

17. The article of manufacture of claim 14, wherein the existing knowledge comprises at least one fault signature, wherein a fault signature captures a set of deviations from normal behavior that are characteristic of an event.

18. The article of manufacture of claim 17, wherein the method steps comprise matching the deviation from normal behavior with a fault signature when an event is generated.

23. The article of manufacture of claim 14, wherein the at least one metric comprises a metric pertaining to at least one of central processing unit, memory, cache, network resources and disk resources.

24. The article of manufacture of claim 14, wherein said identifying comprises identifying a trend from time series data.

25. The article of manufacture of claim 14, wherein said analyzing comprises using statistical correlation across virtual machines and resources to pinpoint the location of the deviation to an affected resource and virtual machine.

19. A system for problem determination and diagnosis in a shared dynamic cloud environment, comprising:

at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium;

a memory; and

at least one processor coupled to the memory and operative for;

monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric;

identifying a symptom of a problem and generating an event based on said monitoring;

analyzing the event to determine a deviation from normal behavior; and

classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

26. The system of claim 19, wherein the at least one metric comprises a metric pertaining to at least one of central processing unit, memory, cache, network resources and disk resources.

27. The system of claim 19, wherein said identifying comprises identifying a trend from time series data.

28. The system of claim 19, wherein said analyzing comprises using statistical correlation across virtual machines and resources to pinpoint the location of the deviation to an affected resource and virtual machine.

20. A system for problem determination and diagnosis in a shared dynamic cloud environment, comprising:

a memory;

at least one processor coupled to the memory; and

at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium, the at least one distinct software module comprising;

a monitoring engine module, executing on the processor, for monitoring each virtual machine and physical server in the shared dynamic cloud environment for at least one metric and outputting a monitoring data time-series corresponding to each metric;

an event generation engine module, executing on the processor, for identifying a symptom of a problem and generating an event based on said monitoring;

a problem determination engine module, executing on the processor, for analyzing the event to determine and locate a deviation from normal behavior; and

a diagnosis engine module, executing on the processor, for classifying the event as a cloud-based anomaly or an application fault based on existing knowledge.

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