Server virtualization by user behaviour model using a data mining technique &amp;#x2014; A preliminary study

Prangchumpol, Dulyawit; Sanguansintukul, Siripun; Tantasanawong, Panjai

doi:10.1109/icitst.2009.5402611

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Similarly, the performance of the predictive model for the web server is measured the same way as the proxy server. The results demonstrate that the accuracy prediction of the level of user access for the web server is 87.18% [9].…”

Section: Prediction Of User Behaviormentioning

confidence: 94%

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Improving Heterogeneous Workload Performance in Server Virtualization through User Behavior Schedules

Prangchumpol

Sanguansintukul

Tantasanawong

2010

2010 8th Annual Communication Networks and Services Research Conference

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One of the most attractive topics today is server virtualization. Virtualization is a method of running multiple independent virtual operating systems on a single physical computer. It is a way of maximizing physical resources to maximize the investment in hardware. This concept can help to consolidate servers, improve hardware utilization and reduce the consumption of power and physical space in the data center. However, in an environment where workloads share the underlying platforms, the problem of configuration controls on sharing dynamic resource allocation becomes a challenge. In this paper, we present the improved performance on heterogeneous workload in server virtualization using user behavior schedules. We show a new concept for managing workloads by involving user behavior in each type of service workload. The result shows that user behaviors are different in each type of service workload and time. This preliminary study may be an approach to improve management of data centers running heterogeneous workloads using server virtualization.

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Section: Prediction Of User Behaviormentioning

confidence: 94%

“…We employed an association rule, a data mining technique, to predict user accesses to the proxy and web servers [9].…”

Section: Prediction Of User Behaviormentioning

confidence: 99%