Name
Abstract | ||
|---|---|---|
Optimising the IT infrastructure of large, often geographically distributed, organisations goes beyond the classical virtual machine reassignment problem, for two reasons: (i) the data centres of these organisations are composed of a number of hosting departments which have different preferences on what to host and where to host it, (ii) the top-level managers in these data centres make complex decisions and need to manipulate possible solutions favouring different objectives to find the right balance. This challenge has not yet been comprehensively addressed in the literature and in this paper we demonstrate that a multi-objective VM reassignment is feasible for large decentralised data centres. We show on a realistic data set that our solution outperforms other classical multi-objective algorithms for VM reassignment in terms of quantity of solutions (by about 15% on average) and quality of the solutions set (by over 6% on average). |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/UCC.2015.21 | 2015 IEEE/ACM 8th International Conference on Utility and Cloud Computing (UCC) |
Keywords | Field | DocType |
Large Decentralised Data centres,Machine Reassignment,Multi-objective Optimisation,Evolutionary Algorithms | Virtual machine,Evolutionary algorithm,Computer science,Server,Information technology management,Cloud computing,Virtual machining,Distributed computing | Conference |
ISSN | Citations | PageRank |
2373-6860 | 4 | 0.41 |
References | Authors | |
24 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Takfarinas Saber | 1 | 25 | 4.90 |
| Anthony Ventresque | 2 | 108 | 17.08 |
| Ivona Brandic | 3 | 3048 | 172.43 |
| James Thorburn | 4 | 26 | 4.11 |
| Liam Murphy | 5 | 811 | 74.94 |