Automatic and coordinated job recovery for high performance computing
Wei Tang, Zhiling Lan, Narayan Desai, Daniel Buettner · 2010
As the scale of high-performance computing systems continues to grow, the impact of failures on the systems is increasingly critical. Research has been performed on fault prediction and associated precautionary actions. While this approach is valuable, it is not adequate because of the inevitability of failures. Postfailure recovery is equally important; however, most current work relies mainly on checkpoint/restart, not addressing the problem from the system level. We propose AuCoRe, an automatic and coordinated job recovery framework. AuCoRe provides a coordination mechanism for failed-job recovery, taking the execution of regular jobs into account; users specify job recovery policy for their jobs, and an incentive mechanism minimizes gaming. We have implemented AuCoRe in Cobalt, a production resource manager, and evaluated it using real workloads from the Blue Gene/P system at Argonne National Laboratory. Experimental results demonstrate that AuCoRe improves system performance by efficiently managing job recovery.