Conference Publication Details
Mandatory Fields
Wahbi, Mohamed; Grimes, Diarmuid; Mehta, Deepak; Brown, Kenneth N.; O’Sullivan, Barry
CPAIOR 2017: Fourteenth International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming
A Distributed Optimization Method for the Geographically Distributed Data Centres Problem
WOS: 1 ()
Optional Fields
Data centre Virtual machine Distributed optimization method Distributed constraint optimization framework DCOP Semi-asynchronous distributed algorithm
Salvagnin D.; Lombardi M.
Padova, Italy
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.
Grant Details