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.

An Adaptive Performance Aware Threshold Policy Based on $$Q_n$$ Estimator in Cloud Data Centers

“…Gaussian efficiency of this algorithm is 58% which over weighs MAD [24]. Limitation of S n algorithm is discontinuities in its influence function [25]. Our work implements Q n estimator to fix the threshold values.…”

An adaptive threshold policy for host overload detection in cloud data centre

“… Performance evaluation of the proposed scheme against various baseline DVMC schemes using PlanetLab workload.  Performance evaluation of the proposed scheme against SnBODA [8] using PlanetLab workload.…”

“…In an attempt to provide a dynamic threshold value, Bala and Padha [8] have modified MAD and suggested a location-free estimator that computes the median of absolute deviations. However, the scheme doesn't perform well in terms of QoS and leads to higher SLAVs.…”

Resource‐efficient load‐balancing framework for cloud data center networks

“…Another approach based on the ant‐colony system was presented in 39 wherein the VM assignment problem was formulated as a constrained combinatorial optimization problem, and the information of the VMs and servers was used to minimize the power consumption. Several other intelligent approaches have been presented for optimizing the power consumption 40–48. However, in most of these approaches, the VM assignment problem is converted into a single‐objective problem by using the weighted‐sum approach.…”

“…The framework uses power consumption modeling to measure the power consumption based on the resource utilization 53. This model has been widely used in the design of energy‐aware VM placement approaches 40,46–48.…”