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A Distributed Optimization Method for the Geographically Distributed Data Centres Problem

  • Mohamed Wahbi
  • Diarmuid Grimes
  • Deepak Mehta
  • Kenneth N. Brown
  • Barry O’Sullivan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10335)

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.

Keywords

Virtual Machine Constraint Satisfaction Problem Hard Constraint Total Energy Cost Concurrent Computation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mohamed Wahbi
    • 1
  • Diarmuid Grimes
    • 1
  • Deepak Mehta
    • 1
  • Kenneth N. Brown
    • 1
  • Barry O’Sullivan
    • 1
  1. 1.Insight Centre for Data Analytics, School of Computer Science and ITUniversity College CorkCorkIreland

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