Self-Adaptive and Self-Congured CPU Resource Provisioning for Virtualized Servers Using Kalman Filters

Data center virtualization allows cost-eective server consol-idation which can increase system throughput and reduce power consumption. Resource management of virtualized servers is an important and challenging task, especially when dealing with uctuating workloads and complex multi-tier server applications. Recent results in control theory-based resource management have shown the potential benets of adjusting allocations to match changing workloads. This paper presents a new resource management scheme that integrates the Kalman lter into feedback controllers to dynamically allocate CPU resources to virtual machines hosting server applications. The novelty of our approach is the use of the Kalman lter|the optimal ltering technique for state estimation in the sum of squares sense|to track the CPU utilizations and update the allocations accordingly. Our basic controllers continuously detect and self-adapt to unforeseen workload intensity changes. Our more advanced controller self-congures itself to any workload condition without any a priori information. In-dicatively, it results in within 4.8 % of the performance of workload-aware controllers under high intensity workload changes, and performs equally well under medium inten-sity trac. In addition, our controllers are enhanced to deal with multi-tier server applications: by using the pair-wise re-source coupling between application components, they pro-vide a 3 % on average server performance improvement when facing large unexpected workload increases when compared to controllers with no such resource-coupling mechanism. We evaluate our techniques by controlling a 3-tier Rubis benchmark web site deployed on a prototype Xen-virtualized cluster