NDLI: Proactive Resource Management for Failure Resilient High Performance Computing Clusters

Content Provider	IEEE Xplore Digital Library
Author	Song Fu Cheng-Zhong Xu
Copyright Year	2009
Abstract	Virtual machine (VM) technology provides an additional layer of abstraction for resource management in high performance computing (HPC) systems. In large-scale computing clusters, component failures become norms instead of exceptions, caused by the ever-increasing system complexity. VM construction and reconfiguration is a potent tool for efficient online system maintenance and failure resilience. In this paper, we study how VM-based HPC clusters benefits from failure prediction in resource management for dependable computing. We consider both the reliability and performance status of compute nodes in making selection decisions. We define a capacity-reliability metric to combine the effects of both factors, and propose the Best-fit algorithm to find the best qualified nodes on which to instantiate VMs to run user jobs. We have conducted experiments using failure traces from the Los Alamos National Laboratory (LANL) HPC clusters. The results show the enhancement of system dependability by using our proposed strategy with practically achievable accuracy of failure prediction. With the Best-fit strategies, the job completion rate is increased by 10.5% compared with that achieved in the current LANL HPC cluster. The task completion rate reaches 82.5% with improved utilization of relatively unreliable nodes.
Starting Page	257
Ending Page	264
File Size	298598
Page Count	8
File Format	PDF
ISBN	9781424435722
DOI	10.1109/ARES.2009.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-03-16
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Laboratories Virtual machining Maintenance Voice mail Resilience Failure resilience High performance computing Proactive management Clustering algorithms Dependable systems Large-scale systems Resource management Virtual manufacturing
Content Type	Text
Resource Type	Article

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