Multi-objective Virtual Machine Reassignment for Large Data Centres

Data centres are large IT facilities composed of an intricate collection of interconnected and virtualised computers, connected services, and complex service-level agreements. Optimising data centres, often attempted by reassigning virtual machines to servers, is both desirable and challenging. It is desirable as it could save a large amount of money: using servers better would lead to decommissioning unused ones and organising services better would increase reliability and maintenance. It is also challenging as the search space is very large and very constrained, which makes the solutions difficult to find. Moreover, in practice assignments can be evaluated from different perspectives, such as electricity cost, overall reliability, migration overhead and cloud cost. Managers in data centres then make complex decisions and need to manipulate possible solutions favouring different objectives to find the right balance. Another element I consider in the context of this work is that organisations hosting large IT facilities are often geographically distributed – which means these organisations are composed of a number of hosting departments which have different preferences on what to host and where to host it, and a certain degree of autonomy. The problem is even more challenging as companies can now choose from a pool of public cloud services to host some of their virtual machines.In this thesis, I address the problem of multi-objective virtual machine (VM) reassignment for large data centres from three realistic and challenging perspectives.• First, I demonstrate how intractable is the exact resolution of the problem in a centralised context: I perform a thorough performance evaluation of classical solvers and metaheuristics, and I propose a novel hybrid algorithm which outperforms them.• Second, I design a two-level system addressing multi-objective VM reassignment for large decentralised data centres. My system takes care of both the reassignment of VMs and their placement within the hosting departments and I propose algorithms that optimise each of the levels.• Third, I extend my work to the hybrid cloud world – i.e., when companies can decide to use their own internal resources or pay for public clouds computing resources. The problem becomes now more dynamic (as prices evolve) and challenging, and I propose a novel algorithm that takes all these elements into account.