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A Distributed Optimization Method for the Geographically Distributed Data Centres Problem

Authors: 

Mohamed Wahbi, Diarmuid Grimes, Deepak Mehta, Ken Brown, Barry O'Sullivan

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
CPAIOR 2017
Proceedings: 
Proceedings of the 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming
Digital Object Identifer (DOI): 
10.NA
Publication Date: 
05/06/2017
Pages: 
147-166
Conference Location: 
Italy
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No
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