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One of the key decisions made by both MapReduce and HPC cluster management frameworks is the placement of jobs within a cluster. To make this decision, they consider factors like resource constraints within a node or the proximity of data to a process. However, they fail to account for the degree of collocation on the cluster's nodes. A tight process placement can create contention for the intra-node shared resources, such as shared caches, memory, disk, or network bandwidth. A loose placement would create less contention, but exacerbate network delays and increase cluster-wide power consumption. Finding the best job placement is challenging, because among many possible placements, we need to find one that gives us an acceptable trade-off between performance and power consumption. We propose to tackle the problem via multi-objective optimization. Our solution is able to balance conflicting objectives specified by the user and efficiently find a suitable job placement.
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