Order@Cloud: An agnostic meta‐heuristic for VM provisioning, adaptation, and organisation

Abstract

Summary We propose a flexible meta‐heuristic framework for virtual machine (VM) organisation, provisioning, and adaptation in the cloud domain, based on migration costs and environment constraints. Order@Cloud improves VM placements according to multiple objectives represented by rules, qualifiers, and improvement cost, which can be easily modified and extended. Order@Cloud theoretically guarantees the adoption of a better set of placements, after considering their costs and benefits, by prioritising the worst VM placements. While existing solutions address only specific objectives, our framework is objective‐agnostic and extensible, which enables the adoption and implementation of new policies and priorities. We conduct experiments using a real cloud environment data and discuss the framework's performance, flexibility, and optimality and provide insights on the challenges and benefits of deploying this framework.

A Survey on the Current Challenges of Energy-Efficient Cloud Resources Management

“…4 The main operators used in the GA phases SN Computer Science two approaches still have to adapt to dynamic task allocation in order to reallocate or migrate tasks. As recent works focus on multiobjective (MO) to cover multiple aspects of virtual machine placement (VMP), the same concept of adapted Pareto filter appeared again in [20]. This concept was accompanied with an alternative variable neighborhoods search (VNS) that ignores VMs hindering the search for better scenario, so that no migration will be needed.…”