Automated service level management system

• US 9,311,611 B2
• Filed: 06/16/2006
• Issued: 04/12/2016
• Est. Priority Date: 06/16/2006
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett Packard Enterprise Development LP (Hewlett-Packard Enterprise Company)
• Inventors: Suer, Myles, Newton, Barbara, Baron, David, Lazarus, Michael
• Primary Examiner(s): Ouellette, Jonathan
• Application Number:
  US11/454,733
• Publication Number:
  US 20070294406A1
• Time in Patent Office: 3,588 Days
• Field of Search: 705/1, 705/1.1
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: Y02P 90/80 Management or planning

Abstract

A method for service level management comprises identifying connected enterprise application components and, under control of an automated system, relating historical performance for the connected enterprise application components and electronically creating a service level agreement based on the historical performance relation.

Claims

1. A service level management method comprising:

identifying connected enterprise application components;

under control of an automated system;

relating historical performance for the connected enterprise application components; and

electronically creating a service level agreement based on the historical performance relation obtained from analysis of the connected enterprise application components that are identified, wherein the relating and the electronically creating are performed by one or more computers.

2. The method according to claim 1 further comprising:

creating a service catalog designating enterprise application components.

3. The method according to claim 1 further comprising:

storing historical performance level data in a storage;

interrelating business service object components with the stored historical performance level data; and

electronically deriving business service component performance from the interrelated business service object components and the historical performance level data.

4. The method according to claim 3 further comprising:

integrating the derived business service component performance into a historically-based service baseline; and

combining the historically-based service baseline into the historical performance level data.

5. The method according to claim 4 further comprising:

collecting data from business customers regarding service impact and opportunity cost for a changed component performance condition;

analyzing a series of historically-based service baselines and opportunity cost data; and

deriving an optimum service level agreement baseline from the analysis.

6. The method according to claim 4 further comprising:

collecting data from business customers regarding historical component outage and changed component performance condition; and

relating historical component outage and system performance variation to component performance.

7. The method according to claim 1 further comprising:

storing historical performance level data in a storage;

interrelating business service object components with the stored historical performance level data;

assigning business impact to services;

associating the business impact with a plurality of different elements of an information technology (IT) environment including business services, departments, operators, and work groups;

creating a unified metric for cost that is directly translatable to revenue from the assigned business impact;

specifying a unified cost function as a business impact that describes value of delivery of a portfolio of services by information technology to a business;

electronically balancing business impact aspects including service level, resources, service maintenance schedules, cost, and quality of service; and

creating virtual service level agreements based on the balancing.

8. The method according to claim 1 further comprising:

collecting information on information technology (IT) organizations that supply services to business services;

collecting information on the business services that receive services from the IT organizations;

establishing relationships between the IT organizations and business services; and

establishing business impact rules at the IT organization level for downtime and/or degraded performance.

9. The method according to claim 8 further comprising:

analyzing the established IT organization-business services relationships and the established business impact rules in combination with the historical performance; and

determining a historical performance impact based on the analysis.

10. The method according to claim 8 further comprising:

establishing an information technology (IT) environment model whereby entities including departments, business services, configuration items, work groups, and/or operators respond to a system input signal that can be used to simulate collective IT behavior;

activating a simulation based on an event selected from among a plurality of predetermined incidents and/or predetermined system modifications;

simulating performance to an incident and/or system modification affecting a business service according to the established business impact rules producing a response according to the established IT environmental model;

simulating changes to the IT environmental model to validate expected changes to performance to the changes in an actual IT environment;

displaying rules that trigger the simulated incident; and

auditing the rules.

11. The method according to claim 10 further comprising:

establishing a profile of a preselected operator that estimates the operator'"'"'s capability to process events in terms of success, quality, and time in context of the established IT environment model; and

reassigning the operator based on the profiled capabilities.

12. The method according to claim 10 further comprising:

predicting changes to intensity and type of incident to determine expected incident response time and proactively modify the IT environment in response to the changes.

13. The method according to claim 10 further comprising:

classifying incidents according to skills-based resolutions behavior; and

determining behavior according to the classification for expected paths-to-resolution and expected processing time.

14. The method according to claim 10 further comprising;

estimating cost and business impact for adding a new business service to the established IT environment; and

selecting business services according to similarity and scaling the selected business services to estimate impact of adding the new business service.

15. The method according to claim 1 further comprising:

collecting performance information for a service portfolio;

modeling historical performance of the service portfolio based on the collected information; and

simulating a selected outage incident and/or a selected modification in service level decisions for performance impact across business services.

16. The method according to claim 1 further comprising:

identifying a collection of operators as a work group;

assigning incidents to work groups based on work group traffic and work group efficacy for dynamically accommodating changes in loading;

modeling behavior of individual operators in the work group and behavior of the collective work group, the modeled behavior further comprising modeling of processing elasticity of the individual operators and/or work group with increasing demand of event processing;

estimating effective work time of resources for a work group whereby staff loading can be expressed as a full-time equivalent;

estimating the staff loading using historic data; and

processing events assigned to the work group according to the modeling.

17. The method according to claim 1 further comprising:

identifying impact of downtime and/or degraded performance at a work around level; and

creating an audible impact trail based on the identified impact.

18. The method according to claim 1 further comprising:

assigning business impact to services;

specifying a unified cost function as a business impact that describes value of delivery of a portfolio of services by information technology to a business;

optimizing configuration of information technology (IT) components using the unified cost function whereby business impact within defined constraints is minimized;

determining assignment of operators to work groups according to the optimization;

determining staffing level across the IT components according to the optimization;

specifying service level objectives for association with business services according to the optimization;

selecting IT infrastructure according to the optimization; and

determining relative merit of modification to staffing, service level objectives, and/or infrastructure.

19. The method according to claim 1 further comprising:

simulating performance to incidents and/or system modifications affecting a business service according to established business impact rules;

developing a priority model that specifies relative priority of an incident relative to other processed incidents according to the simulation;

integrating service level objectives and business impact information into the priority model;

deploying the priority model into a service management environment; and

deploying service level objectives and staffing changes into the service management environment.

20. The method according to claim 1 further comprising:

generating an incident for a business service based on expected failure, expected performance deterioration, actual failure, or actual performance deterioration;

classifying incidents;

estimating optimal routing of incidents to assignment groups according to the incident classification; and

reassigning incident trajectories in simulation based on best routing estimates to determine business impact of optimal incident assignment.

21. A service level management method comprising:

analyzing a service level organization comprising;

executing a retrospective analysis of historic performance that deconstructs the historic performance according to historic actual functioning of information technology (IT) business services staff and service level organization deployment;

executing an active analysis of IT business service current performance; and

executing a proactive analysis of simulation IT business services performance including proactive entry of events and/or change conditions for staff and service level organization deployment simulation, wherein the analyzing of the service level organization, the executing of the retrospective analysis, the executing of the active analysis, and the executing of the proactive analysis are performed by one or more computers.

22. The method according to claim 21 comprising:

executing the retrospective analysis further comprising;

executing a comprehensive restructuring of captured data for analysis of historic performance; and

executing at least one On-Line Analytical Processing (OLAP) and transactional analytics for historic performance characterization.

23. The method according to claim 21 comprising:

executing the active analysis further comprising;

executing at least one On-Line Analytical Processing (OLAP) and transactional analytics for current performance characterization;

defining relationships between information technology (IT) business services and customers in an IT-centric perspective that enables determination of impact of unavailable and/or non-performed business services.

24. The method according to claim 21 comprising:

executing the proactive analysis further comprising guided searching to determine optimal efficiency level.

25. The method according to claim 21 comprising:

executing the proactive analysis further comprising;

executing a dynamically-configurable information technology (IT) and business organization model that selectively simulates historic, current, and/or prospective performance, enabling experimentation to determine potential performance before invoking organizational changes; and

executing a guided search and optimization for recursive simulation, enabling optimal configuration by iterative simulation changes, determination of change vectors, and deploying simulation results into a new simulation model.