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Q-clouds: managing performance interference effects for QoS-aware clouds
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Network I/O fairness in virtual machines
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Perfctr-Xen: a framework for performance counter virtualization
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Is co-scheduling too expensive for SMP VMs?
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Dynamic adaptive scheduling for virtual machines
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Supporting Overcommitted Virtual Machines through Hardware Spin Detection
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vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing
NINEPIN: Non-invasive and energy efficient performance isolation in virtualized servers
DSN '12 Proceedings of the 2012 42nd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)
Performance isolation and fairness for multi-tenant cloud storage
OSDI'12 Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation
Demand-based coordinated scheduling for SMP VMs
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Optimizing virtual machine scheduling in NUMA multicore systems
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As multicore processors become prevalent in modern computer systems, there is a growing need for increasing hardware utilization and exploiting the parallelism of such platforms. With virtualization technology, hardware utilization is improved by encapsulating independent workloads into virtual machines (VMs) and consolidating them onto the same machine. SMP virtual machines have been widely adopted to exploit parallelism. For virtualized systems, such as a public cloud, fairness between tenants and the efficiency of running their applications are keys to success. However, we find that existing virtualization platforms fail to enforce fairness between VMs with different number of virtual CPUs (vCPU) that run on multiple CPUs. We attribute the unfairness to the use of per-CPU schedulers and the load imbalance on these CPUs that incur inaccurate CPU allocations. Unfortunately, existing approaches to reduce unfairness, e.g., dynamic load balancing and CPU capping, introduce significant inefficiencies to parallel workloads. In this paper, we present Flex, a vCPU scheduling scheme that enforces fairness at VM-level and improves the efficiency of hosted parallel applications. Flex centers on two key designs: (1) dynamically adjusting vCPU weights (FlexW) on multiple CPUs to achieve VM-level fairness and (2) flexibly scheduling vCPUs (FlexS) to minimize wasted busy-waiting time. We have implemented Flex in Xen and performed comprehensive evaluations with various parallel workloads. Results show that Flex is able to achieve CPU allocations with on average no more than 5% error compared to the ideal fair allocation. Further, Flex outperforms Xen's credit scheduler and two representative co-scheduling approaches by as much as 10X for parallel applications using busy-waiting or blocking synchronization methods.