Interference and locality-aware task scheduling for MapReduce applications in virtual clusters
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
Autonomic Provisioning with Self-Adaptive Neural Fuzzy Control for Percentile-Based Delay Guarantee
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Towards fair and efficient SMP virtual machine scheduling
Proceedings of the 19th ACM SIGPLAN symposium on Principles and practice of parallel programming
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A virtualized data center faces important but challenging issue of performance isolation among heterogeneous customer applications. Performance interference resulting from the contention of shared resources among co-located virtual servers has significant impact on the dependability of application QoS. We propose and develop NINEPIN, a non-invasive and energy efficient performance isolation mechanism that mitigates performance interference among heterogeneous applications hosted in virtualized servers. It is capable of increasing data center utility. Its novel hierarchical control framework aligns performance isolation goals with the incentive to regulate the system towards optimal operating conditions. The framework combines machine learning based self-adaptive modeling of performance interference and energy consumption, utility optimization based performance targeting and a robust model predictive control based target tracking. We implement NINEPIN on a virtualized HP ProLiant blade server hosting SPEC CPU2006 and RUBiS benchmark applications. Experimental results demonstrate that NINEPIN outperforms a representative performance isolation approach, Q-Clouds, improving the overall system utility and reducing energy consumption.