Cloud platforms can provide flexible and cost-effective environments for parallel applications. However, the resource over-commitment issues, i.e., cloud providers often provide much more executable virtual CPUs than available physical CPUs, still impede the synchronization operations of parallel applications, causing severe performance degradation. Existing methods optimize parallel applications by promoting the priorities of involved VMs. They cannot fully explore the performance of parallel applications, because they ignore the time-slice requirements of different phases of parallel applications. Furthermore, non-parallel applications experience unsatisfied performance because of low scheduling priorities. Given empirical analysis on time-slices of virtual machines (VMs), we find that shortening time-slices can mitigate synchronization overhead which incurs during communication phases, while over-short time-slices cause frequent cache misses in computation phases. Accordingly, we propose an Adaptive Time-slice Control (ATC) mechanism. ATC first detects the phases of parallel applications based on lock latency or cache misses. Then, ATC shortens time-slices during communication phases and prolongs time-slices during computation phases for parallel applications, and sets a uniform time-slice for non-parallel applications. We evaluate ATC using seven well-known benchmarks with 25+ applications. Experiments show that ATC obtains 1.5-75× performance gain for running parallel applications than state-of-the-art solutions, with nearly unaffected impact on non-parallel applications.

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.

Due to high flexibility and cost-effectiveness, cloud computing is increasingly being explored as an alternative to local clusters by academic and commercial users. Recent research already confirmed the feasibility of running tightly-coupled parallel applications with virtual clusters. However, such types of applications suffer from significant performance degradation, especially as the over-commitment is common in cloud. That is, the number of executable Virtual CPUs (VCPUs) is often larger than that of available Physical CPUs (PCPUs) in the system. The performance degradation is mainly due to the fact that the current virtual machine monitors (VMMs) are unaware of the synchronization requirements of the VMs which are running parallel applications. In this paper, There are two key contributions. (1) We propose an autonomous synchronization-aware VM scheduling (SVS) algorithm, which can effectively mitigate the performance degradation of tightly-coupled parallel applications running atop them in over-committed situation. (2) We integrate the SVS algorithm into Xen VMM scheduler, and rigorously implement a prototype. We evaluate our design on a real cluster environment with NPB benchmark and real-world trace. Experiments show that our solution attains better performance for tightly-coupled parallel applications than the state-of-the-art approaches like Xen's Credit scheduler, balance scheduling, and hybrid scheduling.

Due to high flexibility and cost-effectiveness, cloud computing is increasingly being explored as an alternative to local clusters by academic and commercial users. Recent research already confirmed the feasibility of running tightly-coupled parallel applications with virtual clusters. However, such types of applications suffer from significant performance degradation, especially as the overcommitment is common in cloud. That is, the number of executable Virtual CPUs (VCPUs) is often larger than that of available Physical CPUs (PCPUs) in the system. The performance degradation mainly results from that the current Virtual Machine Monitors (VMMs) cannot co-schedule (or coordinate at the same time) the VCPUs that host parallel application threads/processes with synchronization requirements.