Section: New Results

A Multi-objective Evolutionary Algorithm for Cloud Platform Reconfiguration

Participants: F. Legillon, N. Melab and E-G. Talbi

This contribution published in [37] is a result of an industrial collaboration with Tasker Cloud services company.

Offers of public IAAS providers are dynamic: new providers enter the market, existing ones change their pricing or improve their offering. The decision on whether and how to improve already deployed platforms, either by reconfiguration or migration to another provider, can be modelled as an NP-hard optimization problem. In this paper, we define a new realistic model for this migration problem, based on a multi-objective Optimization formulation. An evolutionary approach is introduced to tackle the problem, using newly defined specific operators. Experiments are conducted on multiple realistic data-sets, showing that the evolutionary approach is viable to tackle real-size instances in a reasonable amount of time.