Distributed Approximation Algorithm for Resource Clustering
نویسندگان
چکیده
In this paper, we consider the clustering of resources on large scale platforms. More precisely, we target parallel applications consisting of independant tasks, where each task is to be processed on a different cluster. In this context, each cluster should be large enough so as to hold and process a task, and the maximal distance between two hosts belonging to the same cluster should be small in order to minimize latencies of intra-cluster communications. This corresponds to maximum bin covering with an extra distance constraint. We describe a distributed approximation algorithm that computes resource clustering with coordinates in Q in O(log n) steps and O(n log n) messages, where n is the overall number of hosts. We prove that this algorithm provides an approximation ratio of 1 3 .
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