Dynamic Acceleration of Parallel Applications in Cloud Platforms by Adaptive Time-Slice Control

Tightly-coupled parallel applications in cloud systems may suffer from significant performance degradation because of the resource over-commitment issue. In this paper, we propose a dynamic approach based on the adaptive control over time-slice for virtual clusters, in order to mitigate the performance degradation for parallel applications in cloud and avoid the negative impact effectively on other non-parallel applications meanwhile. The key idea is to reduce the synchronization overhead inside and across virtual machines (VMs) in cloud systems, by dynamically adjusting the time-slices of VMs in terms of the spinlock latency at runtime. Such a design is motivated by our experimental finding that VM's time slice is a key factor determining the synchronization overhead as well as the parallel execution performance. We perform the evaluation on a real cluster environment deployed with XEN, using five well-known benchmarks with 10+ applications. Experiments show that our approach obtains 1.5-10× performance gain for running parallel applications, than other state-of-the-art solutions (including Credit Scheduling of Xen and the well-known methods like Co-Scheduling and Balance Scheduling), with nearly unaffected impact on the performance of non-parallel applications.