VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge
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|Title:||VM reassignment in hybrid clouds for large decentralised companies: A multi-objective challenge||Authors:||Saber, Takfarinas
Murphy, Liam, B.E.
|Permanent link:||http://hdl.handle.net/10197/9041||Date:||2017||Abstract:||Optimising the data centres of large IT organisations is complex as (i) they are composed of various hosting departments with their own preferences and (ii) reassignment solutions can be evaluated from various independent dimensions. But in reality, the problem is even more challenging as companies can now choose from a pool of cloud services to host some of their workloads. This hybrid search space seems intractable, as each workload placement decision (seen as running in a virtual machine on a server) is required to answer many questions: can we host it internally? In which hosting department? Are the capital allocators of this hosting department ok with this placement? How much does it save us and is it safe? Is there a better option in the Cloud? Etc. In this paper, we define the multi-objective VM reassignment problem for hybrid and decentralised data centres. We also propose H2¿D2, a solution that uses a multi-layer architecture and a metaheuristic algorithm to suggest reassignment solutions that are evaluated by the various hosting departments (according to their preferences). We compare H2¿D2 against state-of-the-art multi-objective algorithms and find that H2¿D2 outperforms them both in terms of quantity (approx 30% more than the second-best algorithm on average) and quality of solutions (19% better than the second-best on average).||Funding Details:||Science Foundation Ireland||Type of material:||Journal Article||Publisher:||Elsevier||Journal:||Future Generation Computer Systems||Volume:||79||Issue:||Part 2||Start page:||751||End page:||764||Copyright (published version):||2017 Elsevier||Keywords:||Large decentralised data centres; Hybrid clouds; VM reassignment; Multi-objective optimisation; Hybrid algorithms; Interval objective||DOI:||10.1016/j.future.2017.06.015||Language:||en||Status of Item:||Peer reviewed|
|Appears in Collections:||Computer Science Research Collection|
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