A distributed optimization method for the geographically distributed data centres problem

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Date
2017-06
Authors
Wahbi, Mohamed
Grimes, Diarmuid
Mehta, Deepak
Brown, Kenneth N.
O'Sullivan, Barry
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Springer International Publishing AG
Published Version
10.1007/978-3-319-59776-8_12
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Abstract
The geographically distributed data centres problem (GDDC) is a naturally distributed resource allocation problem. The problem involves allocating a set of virtual machines (VM) amongst the data centres (DC) in each time period of an operating horizon. The goal is to optimize the allocation of workload across a set of DCs such that the energy cost is minimized, while respecting limitations on data centre capacities, migrations of VMs, etc. In this paper, we propose a distributed optimization method for GDDC using the distributed constraint optimization (DCOP) framework. First, we develop a new model of the GDDC as a DCOP where each DC operator is represented by an agent. Secondly, since traditional DCOP approaches are unsuited to these types of large-scale problem with multiple variables per agent and global constraints, we introduce a novel semi-asynchronous distributed algorithm for solving such DCOPs. Preliminary results illustrate the benefits of the new method.
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Keywords
Data centre , Virtual machine , Distributed optimization method , Distributed constraint optimization framework , DCOP , Semi-asynchronous distributed algorithm
Citation
Wahbi M., Grimes D., Mehta D., Brown K. N., O’Sullivan B. (2017) 'A distributed optimization method for the geographically distributed data centres problem’, in Salvagnin D. and Lombardi M. (eds) Integration of AI and OR Techniques in Constraint Programming. Proceedings of Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, Padua, Italy, 5-8 June. Lecture Notes in Computer Science, 10335, pp. 147-166. doi:10.1007/978-3-319-59776-8_12
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https://hdl.handle.net/10468/4548
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© 2017, Springer International Publishing AG. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-59776-8_12