IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Metrics for Parallel Job Scheduling and Their Convergence
JSSPP '01 Revised Papers from the 7th International Workshop on Job Scheduling Strategies for Parallel Processing
The Performance of Processor Co-Allocation in Multicluster Systems
CCGRID '03 Proceedings of the 3st International Symposium on Cluster Computing and the Grid
Characterization of Backfilling Strategies for Parallel Job Scheduling
ICPPW '02 Proceedings of the 2002 International Conference on Parallel Processing Workshops
On Advantages of Grid Computing for Parallel Job Scheduling
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
The workload on parallel supercomputers: modeling the characteristics of rigid jobs
Journal of Parallel and Distributed Computing
Characterization of Bandwidth-Aware Meta-Schedulers for Co-Allocating Jobs Across Multiple Clusters
The Journal of Supercomputing
Improving parallel job scheduling performance in multi-clusters through selective job coallocation
Improving parallel job scheduling performance in multi-clusters through selective job coallocation
Parallel job scheduling — a status report
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
A dynamic co-allocation service in multicluster systems
JSSPP'04 Proceedings of the 10th international conference on Job Scheduling Strategies for Parallel Processing
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In this paper, we utilize a bandwidth-centric job communication model that captures the interaction and impact of simultaneously co-allocating jobs across multiple clusters. We make use of a parallel job model that seeks to capture both local and global communication access patterns. By doing so, we are able to explore scheduling strategies that attempt to improve average job turnaround time by selectively mapping jobs across cluster boundaries in a process known as job co-allocation. In this research, we focus on scheduling strategies that make use of available information such as network link utilization, per-processor bandwidths, and job communication topology in order to make intelligent decisions regarding application partition sizes and job placement. We provide results that help to establish the relationship between the quantity of information available a priori to the scheduler and its ability to improve overall system performance. Additionally, we demonstrate the dramatic impact that salient workload characteristics can have on the effectiveness of co-allocation.