In this paper, we present a bandwidth-centric job communication model that captures the interaction and impact of simultaneously co-allocating jobs across multiple clusters. We compare our dynamic model with previous research that utilizes a fixed execution time penalty for co-allocated jobs. We explore the interaction of simultaneously co-allocated jobs and the contention they often create in the network infrastructure of a dedicated computational multi-cluster.We also present several bandwidth-aware co-allocating meta-schedulers. These schedulers take inter-cluster network utilization into account as a means by which to mitigate degraded job run-time performance. We make use of a bandwidth-centric parallel job communication model that captures the time-varying utilization of shared inter-cluster network resources. By doing so, we are able to evaluate the performance of multi-cluster scheduling algorithms that focus not only on node resource allocation, but also on shared inter-cluster network bandwidth.
In this paper, we present a bandwidth-centric parallel job communication model that takes into account intercluster network utilization as a means by which to capture the interaction and impact of simultaneously co-allocated jobs in a mini-grid. Our model captures the time-varying utilization of shared inter-cluster network resources in the grid. We compare our dynamic model with previous research that utilizes a fixed execution time penalty for coallocated jobs. We have found that the fixed penalty model is more generous in its prediction of job turnaround time than our dynamic communication model. Additionally, we see that the penalty co-allocated jobs may experience without causing a severe performance degradation decreases as the number of clusters increases.
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