Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers

It is challenging to reduce resource over-provisioning for enterprise applications while maintaining set-vice level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study, the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, us well as it multi-pulse (MP) model. We compare the properties of the predictive controllers with tin adaptive integral (1) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers tire evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in tin enterprise data center. Since these traces were collected in tin open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where it Web server wits hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and rind that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not, In addition, till the three predictive schemes performed significantly better when the prediction error was accounted For using it feedback mechanism. The NIP-hosed method also demonstrated an interesting self-learning behavior.