Data-Intensive Workload Consolidation for the Hadoop Distributed File System
GRID '12 Proceedings of the 2012 ACM/IEEE 13th International Conference on Grid Computing
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Many data centers employ server consolidation to maximize the efficiency of platform resource usage. As a result, multiple virtual machines (VMs) simultaneously run on each data center platform. Contention for shared resources between these virtual machines has an undesirable and non-deterministic impact on their performance behavior in such platforms. This paper proposes the use of shared resource monitoring to (a) understand the resource usage of each virtual machine on each platform, (b) collect resource usage and performance across different platforms to correlate implications of usage to performance, and (c) migrate VMs that are resource-constrained to improve overall data center throughput and improve Quality of Service (QoS). We focus our efforts on monitoring and addressing shared cache contention and propose a new optimization metric that captures the priority of the VM and the overall weighted throughput of the data center. We conduct detailed experiments emulating data center scenarios including on-line transaction processing workloads (based on TPC-C) middle-tier workloads (based on SPECjbb and SPECjAppServer) and financial workloads (based on PARSEC). We show that monitoring shared resource contention (such as shared cache) is highly beneficial to better manage throughput and QoS in a cloud-computing data center environment.