As processor architectures have been enhancing their computing capacity by increasing core counts, independent workloads can be consolidated on a single node for the sake of high resource efficiency in data centers. With the prevalence of virtualization technology, each individual workload can be hosted on a virtual machine for strong isolation between co-located workloads. Along with this trend, hosted applications have increasingly been multithreaded to take advantage of improved hardware parallelism. Although the performance of many multithreaded applications highly depends on communication (or synchronization) latency, existing schemes of virtual machine scheduling do not explicitly coordinate virtual CPUs based on their communication behaviors. This paper presents a demand-based coordinated scheduling scheme for consolidated virtual machines that host multithreaded workloads. To this end, we propose communication-driven scheduling that controls time-sharing in response to inter-processor interrupts (IPIs) between virtual CPUs. On the basis of in-depth analysis on the relationship between IPI communications and coordination demands, we devise IPI-driven coscheduling and delayed preemption schemes, which effectively reduce synchronization latency and unnecessary CPU consumption. In addition, we introduce a load-conscious CPU allocation policy in order to address load imbalance in heterogeneously consolidated environments. The proposed schemes are evaluated with respect to various scenarios of mixed workloads using the PARSEC multithreaded applications. In the evaluation, our scheme improves the overall performance of consolidated workloads, especially communication-intensive applications, by reducing inefficient synchronization latency.

This paper presents virtual asymmetric multiprocessor, a new scheme of virtual desktop scheduling on multi-core processors for user-interactive performance. The proposed scheme enables virtual CPUs to be dynamically performance-asymmetric based on their hosted workloads. To enhance user experience on consolidated desktops, our scheme provides interactive workloads with fast virtual CPUs, which have more computing power than those hosting background workloads in the same virtual machine. To this end, we devise a hypervisor extension that transparently classifies background tasks from potentially interactive workloads. In addition, we introduce a guest extension that manipulates the scheduling policy of an operating system in favor of our hypervisor-level scheme so that interactive performance can be further improved. Our evaluation shows that the proposed scheme significantly improves interactive performance of application launch, Web browsing, and video playback applications when CPU-intensive workloads highly disturb the interactive workloads.

As processor architectures have been enhancing their computing capacity by increasing core counts, independent workloads can be consolidated on a single node for the sake of high resource efficiency in data centers. With the prevalence of virtualization technology, each individual workload can be hosted on a virtual machine for strong isolation between co-located workloads. Along with this trend, hosted applications have increasingly been multithreaded to take advantage of improved hardware parallelism. Although the performance of many multithreaded applications highly depends on communication (or synchronization) latency, existing schemes of virtual machine scheduling do not explicitly coordinate virtual CPUs based on their communication behaviors. This paper presents a demand-based coordinated scheduling scheme for consolidated virtual machines that host multithreaded workloads. To this end, we propose communication-driven scheduling that controls time-sharing in response to inter-processor interrupts (IPIs) between virtual CPUs. On the basis of in-depth analysis on the relationship between IPI communications and coordination demands, we devise IPI-driven coscheduling and delayed preemption schemes, which effectively reduce synchronization latency and unnecessary CPU consumption. In addition, we introduce a load-conscious CPU allocation policy in order to address load imbalance in heterogeneously consolidated environments. The proposed schemes are evaluated with respect to various scenarios of mixed workloads using the PARSEC multithreaded applications. In the evaluation, our scheme improves the overall performance of consolidated workloads, especially communication-intensive applications, by reducing inefficient synchronization latency.