Light-Weight Virtualization Driven Runtimes For Big Data Applications
Datacenters are evolving to host heterogeneous Big Data workloads on shared clusters to reduce the operational cost and achieve higher resource utilization. However, it is challenging to schedule heterogeneous workloads with diverse resource requirements and QoS constraints. For example, when consolidating latency critical jobs and best-effort batch jobs in the same cluster, latency critical jobs may suffer from long queuing delay if their resource requests cannot be met immediately; while best-effort jobs would suffer from killing overhead when preempted. Moreover, resource contention may harm ...
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