Resource Allocation in Contending Virtualized Environments through VM Performance Modeling and Feedback

With active deployment of virtualization in large scale data centers and cloud computing 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 (VMs) and the Virtual Machines Monitor (VMM), and (2) the independent and even conflicting operations between multiple VMs. In this paper a stochastic model of resources in virtualized environments is proposed and resource allocation and scheduling algorithm are proposed to provide performance guarantees and service differentiation in contending conditions. In the proposed algorithm user behavior and workloads are characterized through the historical and real time performance profiling and estimation from hosted agents within individual VMs. The resources are allocated according to the demand as well as the performance of the targeted VMs based on the Suffer age aggregation and performance feedback. Experiments on a real Xen based virtualization environment with 3 VMs are conducted and evaluated for accuracy, efficiency, sensitivity, and overhead. The results show that the performance feedback based allocation can achieve a higher SLA satisfaction rate as 97.5%, a lower load imbalance index as 17.6%. The results also show that this algorithm is valid, effective and scalable for implementation in real virtualized environments.