A multi-start local search heuristic for an energy efficient VMs assignment on top of the OpenNebula cloud manager

• We develop an energy reduction scheduler for cloud managers (OpenNebula).
• We propose a new energy and VMs performance models for the VM scheduling on cloud.
• We propose a Pareto scheduler addressing both energy and VMs performance.
• We prove the superiority of our schedulers over three other scheduling approaches.
• We reduce the energy of up to 200 physical machines of the Grid5000 infrastructure.

Reducing energy consumption is an increasingly important issue in cloud computing, more specifically when dealing with a large-scale cloud. Minimizing energy consumption can significantly reduce the amount of energy bills and the greenhouse gas emissions. Therefore, many researches are carried out to develop new methods in order to consume less energy. In this paper, we present an Energy-aware Multi-start Local Search algorithm (EMLS-ONC) that optimizes the energy consumption of an OpenNebula-based Cloud. Moreover, we propose a Pareto Multi-Objective version of the EMLS-ONC called EMLS-ONC-MO dealing with both the energy consumption and the Service Level Agreement (SLA). The objective is to find a Pareto tradeoff between reducing the energy consumption of the cloud while preserving the performance of Virtual Machines (VMs). The different schedulers have been experimented using different arrival scenarios of VMs and different hardware configurations (artificial and real). The results show that EMLS-ONC and EMLS-ONC-MO outperform the other energy- and performance-aware algorithms in addition to the one provided in OpenNebula by a significant margin on the considered criteria. Besides, EMLS-ONC and EMLS-ONC-MO are proved to be able to assign at least as many VMs as the other algorithms.