TY - GEN
T1 - A distributed optimization method for the geographically distributed data centres problem
AU - Wahbi, Mohamed
AU - Grimes, Diarmuid
AU - Mehta, Deepak
AU - Brown, Kenneth N.
AU - O’Sullivan, Barry
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85020821293
U2 - 10.1007/978-3-319-59776-8_12
DO - 10.1007/978-3-319-59776-8_12
M3 - Conference proceeding
AN - SCOPUS:85020821293
SN - 9783319597751
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 147
EP - 166
BT - Integration of AI and OR Techniques in Constraint Programming - 14th International Conference, CPAIOR 2017, Proceedings
A2 - Salvagnin, Domenico
A2 - Lombardi, Michele
PB - Springer Verlag
T2 - 14th International Conference on Integration of Artificial Intelligence and Operations Research Techniques in Constraint Programming, CPAIOR 2017
Y2 - 5 June 2017 through 8 June 2017
ER -