Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres


Cloud data centres improve CPU utilization of their servers (physical machines or PMs) through Virtualization (virtual machines or VMs). Over virtualised and under virtualized PMs suffer performance degradation and power dissipation respectively. This work presents a stochastic modular scheme for allocating VM requests to a PM by avoiding overloading of PM and keeping the global load characteristics under specified QoS goal. The proposed approach categorizes PMs into three groups (Under Load, Normal Load, Over Load) in a way that minimizes number of PMs in Under Load and Over Load groups and maximizes number of PMs in Normal Load group. We compute VM request rejection probability, response time, service time and number of PMs that are overload or under loaded for evaluating the performance of our model. The results show that these parameters do not degrade with increasing arrival rate. Thus the proposed model is simple yet efficient approach for VM placement problem.

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@inproceedings{Kumar2013OverloadAM, title={Overload Avoidance Model using Optimal Placement of Virtual Machines in Cloud Data Cetres}, author={Narander Kumar and Shalini Agarwal and Vipin Saxena and Anton Beloglazov and Rajkumar Buyya and Olivier Beaumont and Lionel Eyraud-Dubois and Christopher Thraves Caro and Hejer Rejeb and Yongmin Tan and Kyle Chard and Kris Bubendorfer and Hamzeh Khazaei and Saeed Rashwand and Sheng Di and Cho-Li Wang}, year={2013} }