Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Scheduling parallel applications in distributed networks
Cluster Computing
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Concurrency and Computation: Practice & Experience - Middleware for Grid Computing
Diagnosing performance overheads in the xen virtual machine environment
Proceedings of the 1st ACM/USENIX international conference on Virtual execution environments
The MHETA Execution Model for Heterogeneous Clusters
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Optimizing Live Migration of Virtual Machines in SMP Clusters for HPC Applications
NPC '09 Proceedings of the 2009 Sixth IFIP International Conference on Network and Parallel Computing
Paravirtualization for HPC systems
ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
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In this paper we present ARRIVE-F, a novel open source framework which addresses the issue of heterogeneity in compute farms. Unlike the previous attempts, our framework is not based on linear frequency models and does not require source code modifications or off-line profiling. The heterogeneous compute farm is first divided into a number of virtualized homogeneous sub-clusters. The framework then carries out a lightweight 'online' profiling of the CPU, communication and memory subsystems of all the active jobs in the compute farm. From this, it constructs a performance model to predict the execution times of each job on all the distinct sub-clusters in the compute farm. Based upon the predicted execution times, the framework is able to relocate the compute jobs to the best suited hardware platforms such that the overall throughput of the compute farm is increased. We utilize the live migration feature of virtual machine monitors to migrate the job from one sub-cluster to another. The prediction accuracy of our performance estimation model is over 80%. The implementation of ARRIVE-F is lightweight, with an overhead of 3%. Experiments on a synthetic workload of scientific benchmarks show that we are able to improve the throughput of amoderately heterogeneous compute farm by up to 25%, with a time saving of up to 33%.