OP-MLB: An Online VM Prediction based Multi-objective Load Balancing Framework for Resource Management at Cloud Datacenter

Abstract

The elasticity of cloud resources allow cloud clients to expand and shrink their demand of resources dynamically over time. However, fluctuations in the resource demands and pre-defined size of virtual machines (VMs) lead to lack of resource utilization, load imbalance and excessive power consumption. To address these issues and to improve the performance of datacenter, an efficient resource management framework is proposed, which anticipates resource utilization of the servers and balances the load accordingly. It facilitates power saving, by minimizing the number of active servers, VM migrations and maximizing the resource utilization. An online resource prediction system, is developed and installed at each VM, to minimize the risk of Service Level Agreement (SLA) violations and performance degradation due to under/overloaded servers. In addition, multi-objective VM placement and migration algorithms are proposed to reduce the network traffic and power consumption within datacenter. The proposed framework is evaluated by executing experiments on three real world workload datasets namely, Google Cluster Dataset, Planet Lab and Bitsbrain traces. The comparison of proposed framework with the state-of-art approaches, reveals its superiority in terms of different performance metrics. The improvement in power saving achieved by OP-MLB framework is upto 85.3% over the Best-Fit approach.