Transparent Fault Tolerance for Job Healing in HPC Environments

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Title: Transparent Fault Tolerance for Job Healing in HPC Environments
Author: Wang, Chao
Advisors: Dr. Frank Mueller, Committee Chair
Dr. Xiaosong Ma, Committee Member
Dr. Yan Solihin, Committee Member
Dr. Nagiza Samatova, Committee Member
Abstract: As the number of nodes in high-performance computing environments keeps increasing, faults are becoming common place causing losses in intermediate results of HPC jobs. Furthermore, storage systems providing job input data have been shown to consistently rank as the primary source of system failures leading to data unavailability and job resubmissions. This dissertation presents a combination of multiple fault tolerance techniques that realize significant advances in fault resilience of HPC jobs. The efforts encompass two broad areas. First, at the job level, novel, scalable mechanisms are built in support of proactive FT and to significantly enhance reactive FT. The contributions of this dissertation in this area are (1) a transparent job pause mechanism, which allows a job to pause when a process fails and prevents it from having to re-enter the job queue; (2) a proactive fault-tolerant approach that combines process-level live migration with health monitoring to complement reactive with proactive FT and to reduce the number of checkpoints when a majority of the faults can be handled proactively; (3) a novel back migration approach to eliminate load imbalance or bottlenecks caused by migrated tasks; and (4) an incremental checkpointing mechanism, which is combined with full checkpoints to explore the potential of reducing the overhead of checkpointing by performing fewer full checkpoints interspersed with multiple smaller incremental checkpoints. Second, for the job input data, transparent techniques are provided to improve the reliability, availability and performance of HPC I/O systems. In this area, the dissertation contributes (1) a mechanism for offline job input data reconstruction to ensure availability of job input data and to improve center-wide performance at no cost to job owners; (2) an approach to automatic recover job input data at run-time during failures by recovering staged data from an original source; and (3) “just in time†replication of job input data so as to maximize the use of supercomputer cycles. Experimental results demonstrate the value of these advanced fault tolerance techniques to increase fault resilience in HPC environments.
Date: 2009-07-07
Degree: PhD
Discipline: Computer Science
URI: http://www.lib.ncsu.edu/resolver/1840.16/4437


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