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Data aggregation is a critical operation in many big-data applications; for example, data residing in several source racks (mappers) are to be aggregated into one or more specified racks called aggregators (reducers) in the data center network during the shuffle phase of a map-reduce task. In this paper, we explore algorithms for data aggregation to two aggregators in a data center network under the...
We consider the problem of data aggregation using two aggregators. We assume that the source racks can split the data they need to send to the aggregators across multiple paths. We show that obtaining a topology that minimizes aggregation time is NP-hard for k = 2, 3,4, where k the degree of ToR (top-of-rack) switches. We also show that an optimal topology can be computed in polynomial time for k...
In this paper, we develop algorithms for the data aggregation problem which arises in the context of big-data applications that employ the MapReduce operation. For the case when source racks can send their data to the aggregator using multiple paths, we show that an aggregation tree topology that minimizes aggregation time can be constructed in polynomial time. We consider also the problem of constructing...
In this paper, we show that the problem of configuring the topology of a data center network to optimize data aggregation is NP-hard even when the number of aggregators is 1. Further, the approximation ratio of the algorithm proposed by Wang, Ng, and Shaikh [3] for the case of a single aggregator is (k+1)/2, where k is the degree of ToR (top-of-rack) switches and this algorithm also exhibits an anomalous...
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