A Distributed Optimization Method for the Geographically Distributed Data Centres Problem

• Published in: Integration of AI and OR Techniques in Constraint Programming Vol. 10335; pp. 147 - 166
• Main Authors: Wahbi, Mohamed, Grimes, Diarmuid, Mehta, Deepak, Brown, Kenneth N., O’Sullivan, Barry
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2017; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Concurrent Computation; Constraint Satisfaction Problem; Hard Constraint; Total Energy Cost; Virtual Machine
• ISBN: 9783319597751; 3319597752
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-59776-8_12

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.