An Adaptive Overload Detection Policy Based on the Estimator Sn in Cloud Environment

Abstract

Efficient use of cloud resources and providing QoS to its clients is quite challenging for cloud service providers. On one hand, deployment of excessive active resources leads to increase in operational cost and on the other hand, shortage of resources may affect the QoS and SLA violations. In order to optimize the resource utilization of datacenter keeping SLA intact, the issues like over-loaded and under-loaded servers in a cloud datacenter are very important to deal with. Virtual machine migration technique is quite effective in handling such issues. The present work focuses on the adaptive threshold based overload detection policy which uses the robust estimator Sn for statistically analyzing the historical CPU usage of hosts, periodically and accordingly adjusts the upper CPU utilization threshold. The results obtained from proposed policy are compared with Median Absolute Deviation policy for overload detection and it has been found that energy performance efficiency of proposed policy is better than the median absolute deviation policy.