Resource Allocation in Contending Virtualized Environments through Stochastic Virtual Machine Performance Modeling and Feedback
Congfeng Jiang, Jian Ping Wan, Xianghua Xu, Zhang Ji-lin, Xindong You · 2013
With active deployment of virtualization in large scale data centers and cloud com-puting environments, allocation and scheduling of virtual and physical resources raise more challenges and may have negative impacts on system performance due to: (1) the isolation between the guest Virtual Machines and the Virtual Machines Monitor, and (2) the independent and even conflicting operations between multiple Virtual Machines. In this paper a stochastic model of resources in virtualized environments is proposed and three resource allocation and scheduling algorithms are proposed to provide performance guarantees and service differentiation in contending conditions. In the proposed algo-rithms user behavior and workloads are characterized through the historical and real time performance profiling and estimation from hosted agents within individual Virtual Ma-chines. The resources are allocated according to the demand as well as the performance of the targeted Virtual Machines based on the Sufferage aggregation and performance feedback. Experiments on a real Xen based virtualization environment with 20 Virtual Machines are conducted and evaluated for accuracy, efficiency, sensitivity, and over-head. The results show that the performance feedback based allocation can achieve a higher SLA satisfaction rate as 97.1%, a low load imbalance index as 18.7%. The per-formance feedback based allocator uses 14.06 % less CPU time for CPU-intensive appli-cations and reduces 45.59 % io wait time in disk contention environments. The results also show that the feedback based algorithm is valid, effective and scalable for imple-mentation in real virtualized environments.