Building and scaling virtual clusters with residual resources from interactive clouds

R. Benjamin Clay, Zhiming Shen, Xiaosong Ma
<span title="">2013</span> <i title="ACM Press"> Proceedings of the 22nd international symposium on High-performance parallel and distributed computing - HPDC &#39;13 </i> &nbsp;
The popularity of cloud-based interactive computing services (e.g., virtual desktops) brings new management challenges. Each interactive user leaves abundant but fluctuating residual resources while being intolerant to latency, precluding the use of aggressive VM consolidation. In this paper, we present the Resource Harvester for Interactive Clouds (RHIC), an autonomous management framework that harnesses dynamic residual resources aggressively without slowing the harvested interactive
more &raquo; ... RHIC builds adhoc clusters for running throughput-oriented "background" workloads using a hybrid of residual and dedicated resources. These hybrid clusters offer significant gains over normal dedicated clusters: 20-40% cost and 20-29% energy in our testbed. For a given background job, RHIC intelligently discovers/maintains the ideal cluster size and composition, to meet user-specified goals such as cost/energy minimization or deadlines. RHIC employs black-box workload performance modeling, requiring only system-level metrics and incorporating techniques to improve modeling accuracy under bursty and heterogeneous residual resources. We demonstrate the effectiveness and adaptivity of our RHIC prototype with two parallel data analytics frameworks, Hadoop and HBase. Our results show that RHIC finds near-ideal cluster sizes/compositions across 28 workload/goal combinations, with 5% average error for cost minimization and 3% for energy, relative to exhaustive searches, and runtimes 2% under deadlines. Further, RHIC significantly outperforms alternative approaches, tolerates high instability in the harvested interactive cloud, works with heterogeneous hardware and imposes only 0.5% overhead.
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