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SLA based profit optimization in autonomic computing systems
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Source International Conference On Service Oriented Computing archive
Proceedings of the 2nd international conference on Service oriented computing table of contents
New York, NY, USA
SESSION: Service delivery table of contents
Pages: 173 - 182  
Year of Publication: 2004
ISBN:1-58113-871-7
Authors
Li Zhang  IBM T.J. Watson Research Center, Yorktown Heights, NY
Danilo Ardagna  Politecnico di Milano, Milano, Italy
Sponsors
ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
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ABSTRACT

With the development of the Service Oriented Architecture (SOA), organizations are able to compose complex applications from distributed services supported by third party providers. Under this scenario, large data centers provide services to many customers by sharing available IT resources. This leads to the efficient use of resources and the reduction of operating costs. Service providers and their customers often negotiate utility based Service Level Agreements (SLAs) to determine costs and penalties based on the achieved performance levels. Data centers often employ an autonomic computing infrastructure and use a centralized dispatch and control component (a dispatcher) to distribute the user requests to backend servers, and to set the scheduling policies at each server. This dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a set of dispatching and control policies for the dispatcher in such service oriented environments. The objective is to maximize the provider's profits associated with multiple class of SLAs. We show that the overall problem is NP-hard, and develop meta-heuristic solutions based on the tabu-search algorithm. Experimental results are presented to show the benefits of our approach.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Li Zhang: colleagues
Danilo Ardagna: colleagues