Predictive and Dynamic Resource Allocation for Enterprise Applications

Dynamic resource allocation has the potential to provide significant increases in total revenue in enterprise systems through the reallocation of available resources as the demands on hosted applications change over time. This paper investigates the combination of workload prediction algorithms and switching policies: the former aim to forecast the workload associated with Internet services, the latter switch resources between applications according to certain system criteria. An evaluation of two well known switching policies - the proportional switching policy (PSP) and the bottleneck aware switching policy (BSP) - is conducted in the context of seven workload prediction algorithms. This study uses real-world workload traces consisting of approximately 3.5M requests, and models a multi-tiered, cluster-based, multi-server solution. The results show that a combination of the bottleneck aware switching policy and workload predictions based on an autoregressive, integrated, moving-average model can improve system revenue by as much as 43%.