Autonomous return on investment analysis of additional processing resources
by Jonathan Wildstrom, Peter Stone, Emmett Witchel
International Journal of Autonomic Computing (IJAC), Vol. 1, No. 3, 2010

Abstract: As the use of virtualisation and partitioning grows, it becomes possible to deploy a multi-tier web-based application with a variable amount of computing power. This introduces the possibility of provisioning only for a minimum workload, with the intention of renting more resources as necessary, but it also creates the problem of quickly and accurately identifying when more resources are needed or unneeded resources are being paid for. This paper presents a machine learning based approach to handling this problem. An autonomous adaptive agent learns to predict the gain (or loss) that would result from more (or less) resources; this agent uses only low-level system statistics, rather than relying on custom instrumentation of the operating system or middleware. Our agent is fully implemented and evaluated on a publicly available multi-machine, multi-process distributed system (the online transaction processing benchmark TPC-W). We show that our adaptive agent is competitive with any static choice of computing resources over a variety of test workloads. We also show that the agent outperforms each static choice in at least one case, implying that it is well suited for a situation where the workload is unknown.

Online publication date: Thu, 06-May-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Autonomic Computing (IJAC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com