Towards resource-efficient cloud systems: Avoiding over-provisioning in demand-prediction based resource provisioning

Abstract

Demand-prediction based resource provisioning schemes help assure service level objectives (SLO) in cloud systems. We notice that if a provisioning scheme does not exclude bursts from historical resource demands in normal demand prediction or always uses a large padding to correct under-prediction, it will lead to resource over-provisioning and low resource utilization. To improve the previous schemes, in this paper, we present a Resource-efficient Predictive Resource Provisioning system in clouds (RPRP) that excludes bursts in demand prediction and has algorithms to specifically handle bursts to avoid resource over-provisioning. Rather than setting padding to a possibly high value, RPRP has a load-dependent padding algorithm that adaptively determines padding based on predicted demands. To handle bursts, RPRP embodies a responsive padding algorithm that adaptively adjusts padding to recover from both under-provisioning and over-provisioning. We implemented RPRP on top of Xen and conducted both trace-driven simulation and real-world testbed experiments. The experimental results show that RPRP achieves higher resource utilization, more accurate demand predictions, and fewer SLO violations than previous schemes.