A great many machines, from personal workstations to large clusters, are under utilized. Meanwhile, for the fear of slowing down the native tasks, resource scavenging systems hesitate to aggressively harness idle resources. We have developed a quantitative approach for fine-grained scavenging that can effectively utilizes very small slack periods without adversely impacting the native workload, and automatically adapts to changes in the native workload’s resource consumption. This paper envisions a generic framework, built upon the above approach, that facilitates the sharing of machines between a primary and a secondary workloads, providing a unified view of the diverse systems where idle resources are available. In such a framework the primary workload performance is bounded by a configurable slowdown factor (i.e., 5%) and the secondary workload aggressively utilizes the slack left by the primary workload. Thus our proposed framework creates a novel resourcesharing paradigm that results in greater resource utilization without sacrificing the performance of the primary workload.
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