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A cost minimization data allocation algorithm for dynamic datacenter resizing
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DOI：10.1016/j.jpdc.2017.03.010.png)
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Modern datacenters dynamically adjust the number of active servers in different geographic regions to adapt to the dynamic workloads from user requests and electricity price heterogeneity. One of the main challenges for datacenter resizing is that the heavy network traffic among datacenters causes significant deterioration of the overall performance and considerably increases the operational expenditure of datacenters. In this paper, we propose an efficient data allocation technique that considers both the static and dynamic characteristics of datacenters to enable more efficient datacenter resizing. We first formulate the optimal data allocation problem, propose a genenc model for minimizing the communicating cost in datacenter resizing, and show that the data allocation problem is NP-hard. To produce feasible solution in polynomial time, we propose a heunstic algonthm considering the traffic flow in the network topology of datacenters by first transforming the data allocation problem into a chunk distribution tree (CDT) construction problem, and then reducing the CDT construction to a graph partitioning problem. The experimental results show that our efficient data allocation approach can improve the performance of MapReduce operations effectively with lower communicating and computing costs for datacenter resizing. (C) 2017 Elsevier Inc. All rights reserved.
Keyword:
Big data processing
Cost minimization
Datacenter resizing
Data allocation
MapReduce operation
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