A min-max framework for CPU resource provisioning in virtualized servers using ℋ<inf>∞</inf> Filters

Dynamic resource provisioning for virtualized server applications is integral to achieve efficient cloud and green computing. In server applications unpredicted workload changes occur frequently. Resource adaptation of the virtual hosts should dynamically scale to the updated demands (cloud computing) as well as co-locate applications to save on energy consumption (green computing). Most importantly, resource transitions during workload surges should occur while minimizing the expected loss due to mismatches of the resource predictions and actual workload demands. Our approach is to minimize the maximum expected loss using the same techniques as in two-person zero-sum games. We develop an H ∞ filter that minimizes the worst-case estimation and allocate resources fast. Through simulations our H ∞ filter demonstrates its effectiveness and good performance when compared against Kalman-based controllers.