Service level management

• US 8,073,721 B1
• Filed: 05/23/2000
• Issued: 12/06/2011
• Est. Priority Date: 05/24/1999
• Status: Expired due to Term

• Current Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Original Assignee: Computer Associates Think Inc. (Broadcom, Inc.)
• Inventors: Lewis, Lundy
• Primary Examiner(s): Nguyen, Dean T
• Application Number:
  US09/577,225
• Time in Patent Office: 4,214 Days
• Field of Search: 705/1.1, 705/7.12, 705/7.13, 705/7.22, 705/7.36, 705/1, 705/7, 705/8, 705/9, 705/10, 705/26, 709/223, 709/224, 709/200, 713/1, 713/100, 201/202, 370/252
• US Class Current: 705/7.12
• 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 for providing 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 service parameter that represents the performance of the service;

monitoring, on a computing device, a plurality of component parameters for the plurality of network components that support the service, wherein the plurality of component parameters measure the performances of the plurality of network components that support the service;

mapping, on the computing device, the plurality of component parameters monitored for the plurality of network components to the service parameter that represents the performance of the service, wherein the service parameter mapped to the plurality of component parameters has a value that indicates whether the service conforms to an agreed upon service level identified in a service level agreement;

executing, on the computing device, one or more data mining algorithms to discover a subset of the plurality of component parameters that have a most influence on the service parameter, wherein the discovered subset includes more than one of the plurality of component parameters having the most influence on the service parameter;

identifying, on the computing device, a function that takes one or more input arguments corresponding to the subset of the plurality of component parameters having the most influence on the service parameter and infers the value for the service parameter from the one or more input arguments to provide a many-to-one mapping between the subset of the plurality of component parameters and the value for the service parameter;

monitoring, on the computing device, the subset of the plurality of component parameters that have the most influence on the service parameter; and

determining, on the computing device, whether the service conforms to the agreed upon service level identified in the service level agreement from the monitored subset of the plurality of component parameters, wherein determining whether the service conforms to the agreed upon service level includes;

providing, on the computing device, values for the monitored subset of the plurality of component parameters as the one or more input arguments to the function that provides the many-to-one mapping between the subset of the plurality of component parameters and the value for the service parameter;

inferring, on the computing device, the value for the service parameter from the monitored values for the subset of the plurality of component parameters provided as the one or more input arguments to the function; and

determining, on the computing device, whether the inferred value for the service parameter indicates that the service conforms to the agreed upon service level identified in the service level agreement.

2. The method of claim 1, wherein determining whether the service conforms to the agreed upon service level further includes:

determining, on the computing device, that the service conforms to the agreed upon service level agreement in response to the inferred value for the service parameter meeting or exceeding a threshold value identified in the service level agreement; and

determining, on the computing device, that the service does not conform to the agreed upon service level in response to the inferred value for the service parameter failing to meet or exceed the threshold value identified in the service level agreement.

3. The method of claim 2, wherein determining whether the service conforms to the agreed upon service level further includes generating, on the computing device, a report indicating whether the service conformed to the agreed upon service level during a predetermined time period.

4. The method of claim 3, wherein the report includes the inferred value for the service parameter during the predetermined time period.

5. 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 influences that the plurality of component parameters have on the service parameter; and

analyzing the influences represented for the plurality of component parameters in the decision tree to identify the subset of the plurality of component parameters that includes the more than one component parameters having the most influence on the service parameter.

6. The method of claim 5, wherein the decision tree includes numeric percentages that represent the influences that the plurality of component parameters have on the service parameter.

7. The method of claim 5, wherein the decision tree includes binary values that represent the influences that the plurality of component parameters have on the service parameter.

8. The method of claim 5, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the influences that the plurality of component parameters have on the service parameter.

9. The method of claim 1, wherein the identified function includes a fuzzy logic algorithm that comprises:

translating the monitored values for the subset of the plurality of component parameters into fuzzy concepts;

determining numeric grades of membership that the monitored values have in the fuzzy concepts, wherein the one or more input arguments associated with the function include the numeric grades of membership that the monitored values have in the fuzzy concepts; and

inferring the value for the service parameter from the numeric grades of membership that the monitored values have in the fuzzy concepts.

10. The method of claim 1, wherein the subset of the plurality of component parameters that have the most influence on the service parameter are representative of the plurality of component parameters that measure the performances of the plurality of network components that support the service.

11. The method of claim 10, wherein the subset of the plurality of component parameters measure the performances of a subset of the plurality of network components that support the service, and wherein the subset of the plurality of network components includes more than one of the plurality of network components, whereby the performance of the service depends upon performances of the subset of the plurality of network components.

12. The method of claim 11, wherein determining whether the service conforms to the agreed upon service level further includes:

determining, on the computing device, that the service does not conform to the agreed upon service level in response to the inferred value for the service parameter failing to meet or exceed a threshold value identified in the service level agreement; and

executing one or more instructions on the subset of the plurality of network components until the value for the service parameter meets or exceeds the threshold value identified in the service level agreement in response to determining that the service does not conform to the agreed upon service level.

13. The method of claim 12, wherein the one or more instructions control the values for the subset of the plurality of component parameters.

14. The method of claim 11, wherein the one or more input arguments associated with the function further define scheduled unavailability for at least one of the more than one network components in the subset of the plurality of network components.

15. The method of claim 1, wherein the network includes multiple domains, and wherein the plurality of network components support the service across the multiple domains.

16. The method of claim 15, wherein mapping the plurality of component parameters to the service parameter includes:

receiving multiple intra-domain alarms at an alarm correlation agent, wherein one or more monitoring agents generate the multiple intra-domain alarms across the multiple domains from the plurality of component parameters that measure the performances of the plurality of network components that support the service across the multiple domains; and

correlating the multiple intra-domain alarms generated across the multiple domains, wherein the alarm correlation agent correlates the multiple intra-domain alarms to generate one or more inter-domain alarms across the multiple domains and map the one or more inter-domain alarms to the service parameter.

17. The method of claim 15, wherein the more than one component parameters having the most influence on the service parameter measure the performances of more than one of the plurality of network components that support the service across the multiple domains, and wherein the more than one of the plurality of network components are located in different ones of the multiple domains.

18. A system for providing service level management, comprising:

a network having a plurality of network devices that support a service provided over the network, wherein performance of the service depends upon performances of the plurality of network devices that support the service, and wherein the service has a service parameter that represents the performance of the service;

one or more electronic devices coupled to the network, wherein the one or more electronic devices are configured to;

monitor plurality of component parameters for the plurality of network devices that support the service, wherein the plurality of component parameters measure the performances of the plurality of network devices that support the service;

map the plurality of component parameters monitored for the plurality of network devices to the service parameter that represents the performance of the service, wherein the service parameter mapped to the plurality of component parameters has a value that indicates whether the service conforms to an agreed upon service level identified in a service level agreement;

execute one or more data mining algorithms to discover a subset of the plurality of component parameters that have a most influence on the service parameter, wherein the discovered subset includes more than one of the plurality of component parameters having the most influence on the service parameter; and

identify a function that takes one or more input arguments corresponding to the subset of the plurality of component parameters having the most influence on the service parameter and infers the value for the service parameter from the one or more input arguments to provide a many-to-one mapping between the subset of the plurality of component parameters and the value for the service parameter;

at least one monitoring agent coupled to the network, wherein the at least one monitoring agent is configured to monitor the subset of the plurality of component parameters that have the most influence on the service parameter; and

a service analysis system coupled to the network, wherein the service analysis system includes one or more processors that determine whether the service conforms to the agreed upon service level identified in the service level agreement from the monitored subset of the plurality of component parameters, wherein to determine whether the service conforms to the agreed upon service level, the one or more processors are configured to;

provide values for the monitored subset of the plurality of component parameters as the one or more input arguments to the function that provides the many-to-one mapping between the subset of the plurality of component parameters and the value for the service parameter;

infer the value for the service parameter from the monitored values for the subset of the plurality of component parameters provided as the one or more input arguments to the function; and

determine whether the inferred value for the service parameter indicates that the service conforms to the agreed upon service level identified in the service level agreement.

19. The system of claim 18, wherein to determine whether the service conforms to the agreed upon service level, the one or more processors are further configured to:

determine that the service conforms to the agreed upon service level agreement in response to the inferred value for the service parameter meeting or exceeding a threshold value identified in the service level agreement; and

determine that the service does not conform to the agreed upon service level in response to the inferred value for the service parameter failing to meet or exceed the threshold value identified in the service level agreement.

20. The system of claim 19, wherein to determine whether the service conforms to the agreed upon service level, the one or more processors are further configured to generate a report indicating whether the service conformed to the agreed upon service level during a predetermined time period.

21. The system of claim 20, wherein the report includes the inferred value for the service parameter during the predetermined time period.

22. The system of claim 18, wherein the one or more data mining algorithms include a decision tree algorithm that comprises:

producing a decision tree that represents influences that the plurality of component parameters have on the service parameter; and

analyzing the influences represented for the plurality of component parameters in the decision tree to identify the subset of the plurality of component parameters that includes the more than one component parameters having the most influence on the service parameter.

23. The system of claim 22, wherein the decision tree includes numeric percentages that represent the influences that the plurality of component parameters have on the service parameter.

24. The system of claim 22, wherein the decision tree includes binary values that represent the influences that the plurality of component parameters have on the service parameter.

25. The system of claim 22, wherein the decision tree includes a root node that represents the service parameter, a plurality of leaf nodes that represent the plurality of component parameters, and a plurality of dependencies between the root node and the plurality of leaf nodes that represent the influences that the plurality of component parameters have on the service parameter.

26. The system of claim 18, wherein the identified function includes a fuzzy logic algorithm that comprises:

translating the monitored values for the subset of the plurality of component parameters into fuzzy concepts;

determining numeric grades of membership that the monitored values have in the fuzzy concepts, wherein the one or more input arguments associated with the function include the numeric grades of membership that the monitored values have in the fuzzy concepts; and

inferring the value for the service parameter from the numeric grades of membership that the monitored values have in the fuzzy concepts.

27. The system of claim 18, wherein the subset of the plurality of component parameters that have the most influence on the service parameter are representative of the plurality of component parameters that measure the performances of the plurality of network devices that support the service.

28. The system of claim 27, wherein the subset of the plurality of component parameters measure the performances of a subset of the plurality of network devices that support the service, and wherein the subset of the plurality of network devices includes more than one of the plurality of network devices, whereby the performance of the service depends upon performances of the more than one network devices in the subset of the plurality of network devices.

29. The system of claim 28, wherein to determine whether the service conforms to the agreed upon service level, the one or more processors are further configured to:

determine that the service does not conform to the agreed upon service level in response to the inferred value for the service parameter failing to meet or exceed a threshold value identified in the service level agreement; and

execute executing one or more instructions on the subset of the plurality of network devices until the value for the service parameter meets or exceeds the threshold value identified in the service level agreement in response to determining that the service does not conform to the agreed upon service level.

30. The system of claim 29, wherein the one or more instructions control the values for the subset of the plurality of component parameters.

31. The system of claim 28, wherein the one or more input arguments associated with the function further define scheduled unavailability for at least one of the more than one network devices in the subset of the plurality of network devices.

32. The system of claim 18, wherein the network includes multiple domains, and wherein the plurality of network devices support the service across the multiple domains.

33. The system of claim 32, further comprising an alarm correlation agent that executes on the one or more electronic devices to map the plurality of component parameters to the service parameter, the alarm correlation agent configured to:

receive multiple intra-domain alarms from one or more monitoring agents, wherein the one or more monitoring agents generate the multiple intra-domain alarms across the multiple domains from the plurality of component parameters that measure the performances of the plurality of network devices that support the service across the multiple domains; and

correlate the multiple intra-domain alarms generated across the multiple domains to generate one or more inter-domain alarms across the multiple domains, wherein the alarm correlation agent maps the one or more inter-domain alarms to the service parameter.

34. The system of claim 32, wherein the more than one component parameters having the most influence on the service parameter measure the performances of more than one of the plurality of network devices that support the service across the multiple domains, and wherein the more than one of the plurality of network devices are located in different ones of the multiple domains.