Method and apparatus for service analysis in service level management (SLM)

• US 7,725,571 B1
• Filed: 05/23/2000
• Issued: 05/25/2010
• Est. Priority Date: 05/24/1999
• Status: Expired due to Fees

• Current Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Original Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Inventors: Lewis, Lundy
• Primary Examiner(s): England; David E
• Application Number:
  US09/577,232
• Time in Patent Office: 3,654 Days
• Field of Search: 709/200, 709230-235, 709/223, 709/224, 706 15- 21, 455421-424
• US Class Current: 709/224
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0631 Resource planning, allocation, distributing or scheduling for enterprises or organisations
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 10/06312 Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis
• CPC Class Codes: G06Q 10/0637 Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/04 Billing or invoicing
• CPC Class Codes: G06Q 40/12 Accounting
• CPC Class Codes: H04L 41/024 using relational databases for representation of network management data, e.g. managing via structured query language [SQL]
• CPC Class Codes: H04L 41/042 comprising distributed management centres cooperatively managing the network
• 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 41/16 using machine learning or artificial intelligence
• CPC Class Codes: H04L 41/5003 Managing SLA; Interaction between SLA and QoS
• CPC Class Codes: H04L 41/5009 Determining service level performance parameters or violations of service level contracts, e.g. violations of agreed response time or mean time between failures [MTBF]
• CPC Class Codes: H04L 41/5032 Generating service level reports
• CPC Class Codes: H04L 43/00 Arrangements for monitoring or testing data switching networks
• CPC Class Codes: H04L 43/0817 by checking functioning
• CPC Class Codes: H04L 43/0847 Transmission error
• CPC Class Codes: H04L 43/0852 Delays
• CPC Class Codes: H04L 43/0882 : Utilisation of link capacity
• CPC Class Codes: H04L 43/0888 : Throughput
• CPC Class Codes: H04L 43/0894 : Packet rate
• CPC Class Codes: H04L 43/12 : Network monitoring probes

Abstract

Method and apparatus for service level management, wherein business processes are composed of services. A state of the service is defined by one or more service parameters, and the service parameters depend upon performance of network components that support the service, e.g., component parameters. The state of the service may depend, for example, on a collection of service parameter values for availability, reliability, security, integrity and response time. A service level agreement is a contract between a supplier and a customer that identifies services supported by a network, service parameters for the services, and service levels (e.g., acceptable levels) for each service parameter.

Claims

1. A method, performed by a computer, of providing service analysis in service level management, comprising:

providing a service over a network having a plurality of network components that support the service, wherein performance of the service depends upon performances of the plurality of network components that support the service, and wherein the service has a state that represents the performance of the service;

measuring a plurality of component parameters for the plurality of network components that support the service across a plurality of domains of the network, wherein the plurality of component parameters measure performances of the plurality of network components that support the service;

determining the state of the service from the plurality of component parameters measured across the plurality of domains of the network, wherein determining the state of the service includes mapping the plurality of component parameters measured across the plurality of domains of the network to a service parameter that represents the state of the service, and wherein the state of the service indicates whether the service conforms to an agreed upon service level identified in a service level agreement;

executing one or more data mining algorithms to discover a respective influence that a first one of the measured component parameters in a first domain of the plurality of domains of the network has on the service parameter;

executing the one or more data mining algorithms to discover a respective influence that a second one of the measured component parameters in a second domain of the plurality of domains of the network has on the service parameter;

identifying one of the first component parameter or the second component parameter that has a greatest influence on the service parameter based on the respective influences that the first component parameter and the second component parameter have on the service parameter; and

designating the identified component parameter to be the service parameter.

2. The method of claim 1, wherein the service parameter represents one or more of a response time for one or more of the plurality of network components, traffic congestion in a selected portion of the network, availability for one or more of the plurality of network components, reliability for one or more of the plurality of network components, security for one or more of the plurality of network components, performance for one or more of the plurality of network components, or a configuration for one or more of the plurality of network components.

3. The method of claim 1, wherein a plurality of monitoring agents are configured to measure the plurality of component parameters for the plurality of network components that support the service.

4. The method of claim 3, wherein each of the plurality of monitoring agents are configured to measure a subset of the plurality of component parameters in a respective domain of the plurality of domains of the network.

5. The method of claim 4, wherein the one or more data mining algorithms include an inductive logic algorithm that comprises:

incorporating knowledge relating to the plurality of domains of the network and knowledge relating to the plurality of measured component parameters within a rule base;

inferring the respective influences that each of the plurality of measured component parameters have on the service parameter using the knowledge incorporated within the rule base; and

producing one or more of propositional statements or quantified statements that express the respective influences that one or more of the plurality of component parameters have on the service parameter.

6. The method of claim 1, further comprising integrating the plurality of domains into a service level management domain that comprises a hierarchical structure for the plurality of domains.

7. The method of claim 1, wherein designating the identified component parameter to be the service parameter includes

designating the first component parameter to be the service parameter

in response to determining that the respective influence that the first component parameter has on the service parameter is greater than

the respective influence that the second component parameter has on the service parameter.

8. The method of claim 1, wherein designating the identified component parameter to be the service parameter includes designating the second component parameter to be the service parameter in response to determining that the respective influence that the second component parameter has on the service parameter is greater than the respective influence that the first component parameter has on the service parameter.

9. The method of claim 1, wherein the one or more data mining algorithms are executed while the plurality of network components are operating to support the service.

10. The method of claim 1, wherein the one or more data mining algorithms include a neural network algorithm that comprises:

identifying a case library that includes a plurality of cases representing episodes of problem solving;

applying a plurality of relevance rules to identify one or more of the cases in the case library that are relevant to discovering the respective influences for the plurality of measured component parameters; and

adapting one or more solutions variables associated with the identified cases using parameterized adaption logic to discover the respective influences that each of the plurality of measured component parameters have on the service parameter.

11. The method of claim 1, wherein the one or more data mining algorithms include a decision tree algorithm that comprises producing a decision tree that represents the respective influences that each of the plurality of measured component parameters have on the service parameter.

12. The method of claim 11, wherein the decision tree includes one or more of numeric values or binary values that represent the respective influences that each of the plurality of measured component parameters have on the service parameter.

13. The method of claim 11, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that respectively represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the respective influences that each of the plurality of component parameters have on the service parameter.

14. The method of claim 1, wherein the one or more data mining algorithms include a top N algorithm that comprises:

identifying a predetermined number of the plurality of measured component parameters that have a greatest influence on the service parameter; and

producing a list that includes the identified component parameters having the greatest influence on the service parameter, wherein the identified component parameters are listed in a decreasing order of the respective influences that the identified component parameters have on the service parameter.

15. The method of claim 1, wherein the one or more data mining algorithms include a rule induction algorithm that comprises producing one or more rules that represent the respective influences that one or more of the plurality of measured component parameters have on the service parameter.

16. The method of claim 15, wherein the one or more rules include one or more of propositional statements or quantified statements that represent the respective influences that the one or more component parameters have on the service parameter.

17. The method of claim 1, wherein the one or more data mining algorithms include a fuzzy logic algorithm that comprises:

translating the plurality of measured component parameters into a plurality of respective fuzzy concepts;

determining grades of membership that the measured component parameters have in the respective fuzzy concepts, wherein the grades of membership quantify transitions between a plurality of states in a state transition graph; and

inferring the respective influences that each of the plurality of measured component parameters have on the service parameter from the grades of membership that the measured component parameters have in the respective fuzzy concepts.

18. A device readable medium storing device executable instructions for executing a method of providing service analysis in service level management, the method comprising:

providing a service over a network having a plurality of network components that support the service, wherein performance of the service depends upon performances of the plurality of network components that support the service, and wherein the service has a state that represents the performance of the service;

measuring a plurality of component parameters for the plurality of network components that support the service across a plurality of domains of the network, wherein the plurality of component parameters measure performances of the plurality of network components that support the service;

determining the state of the service from the plurality of component parameters measured across the plurality of domains of the network, wherein determining the state of the service includes mapping the plurality of component parameters measured across the plurality of domains of the network to a service parameter that represents the state of the service, and wherein the state of the service indicates whether the service conforms to an agreed upon service level identified in a service level agreement;

executing one or more data mining algorithms to discover a respective influence that a first one of the measured component parameters in a first domain of the plurality of domains of the network has on the service parameter;

executing the one or more data mining algorithms to discover a respective influence that a second one of the measured component parameters in a second domain of the plurality of domains of the network has on the service parameter;

identifying one of the first component parameter or the second component parameter that has a greatest influence on the service parameter based on the respective influences that the first component parameter and the second component parameter have on the service parameter; and

designating the identified component parameter to be the service parameter.

19. The medium of claim 18, wherein the service parameter represents one or more of a response time for one or more of the plurality of network components, traffic congestion in a selected portion of the network, availability for one or more of the plurality of network components, reliability for one or more of the plurality of network components, security for one or more of the plurality of network components, performance for one or more of the plurality of network components, or a configuration for one or more of the plurality of network components.

20. The medium of claim 18, wherein a plurality of monitoring agents are configured to measure the plurality of component parameters for the plurality of network components that support the service.

21. The medium of claim 20, wherein the method further comprises the plurality of monitoring agents are configured to measure a subset of the plurality of component parameters in a respective domain of the plurality of domains of the network.

22. The medium of claim 21, wherein the one or more data mining algorithms include an inductive logic algorithm that comprises:

incorporating knowledge relating to the plurality of domains of the network and knowledge relating to the plurality of measured component parameters within a rule base;

inferring the respective influences that each of the plurality of measured component parameters have on the service parameter using the knowledge incorporated within the rule base; and

producing one or more of propositional statements or quantified statements that express the respective influences that one or more of the plurality of component parameters have on the service parameter.

23. The medium of claim 18, wherein the method further comprises integrating the plurality of domains into a service level management domain that comprises a hierarchical structure for the plurality of domains.

24. The medium of claim 18, wherein designating the identified component parameter to be the service parameter includes

designating the first component parameter to be the service parameter

in response to determining that the respective influence that the first component parameter has on the service parameter is greater than

the respective influence that the second component parameter has on the service parameter.

25. The medium of claim 18, wherein designating the identified component parameter to be the service parameter includes designating the second component parameter to be the service parameter in response to determining that the respective influence that the second component parameter has on the service parameter is greater than the respective influence that the first component parameter has on the service parameter.

26. The medium of claim 18, wherein the one or more data mining algorithms include a neural network algorithm that comprises:

identifying a case library that includes a plurality of cases representing episodes of problem solving;

applying a plurality of relevance rules to identify one or more of the cases in the case library that are relevant to discovering the respective influences for the plurality of measured component parameters; and

adapting one or more solutions variables associated with the identified, cases using parameterized adaption logic to discover the respective influences that each of the plurality of measured component parameters have on the service parameter.

27. The medium of claim 18, wherein the one or more data mining algorithms include a decision tree algorithm that comprises producing a decision tree that represents the respective influences that each of the plurality of measured component parameters have on the service parameter.

28. The medium of claim 27, wherein the decision tree includes one or more of numeric values or binary values that represent the respective influences that each of the plurality of measured component parameters have on the service parameter.

29. The medium of claim 27, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that respectively represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the respective influences that each of the plurality of component parameters have on the service parameter.

30. The medium of claim 18, wherein the one or more data mining algorithms include a top N algorithm that comprises:

identifying a predetermined number of the plurality of measured component parameters that have a greatest influence on the service parameter; and

producing a list that includes the identified component parameters having the greatest influence on the service parameter, wherein the identified component parameters are listed in a decreasing order of the respective influences that the identified component parameters have on the service parameter.

31. The medium of claim 18, wherein the one or more data mining algorithms include a rule induction algorithm that comprises producing one or more rules that represent the respective influences that one or more of the plurality of measured component parameters have on the service parameter.

32. The medium of claim 31, wherein the one or more rules include one or more of propositional statements or quantified statements that represent the respective influences that the one or more component parameters have on the service parameter.

33. The medium of claim 18, wherein the one or more data mining algorithms include a fuzzy logic algorithm that comprises:

translating the plurality of measured component parameters into a plurality of respective fuzzy concepts;

determining grades of membership that the measured component parameters have in the respective fuzzy concepts, wherein the grades of membership quantify transitions between a plurality of states in a state transition graph; and

inferring the respective influences that each of the plurality of measured component parameters have on the service parameter from the grades of membership that the measured component parameters have in the respective fuzzy concepts.