LECC: Location, energy, carbon and cost-aware VM placement model in geo-distributed DCs

Abstract

The wide adoption of the cloud computing model in the business environment has led to a rapid increase in the development of geo-distributed data centers (DCs) to support customers’ needs. On the other hand, cloud DCs that contain thousands of computing and storage nodes consumes a large amount of energy that leads to a high carbon footprint. Therefore, minimizing the geo-distributed DCs’ energy consumption is a must which will decrease the cloud providers’ operational cost and minimize the high non-environment carbon emission. In addition, minimizing the cloud users’ network latency, in such a distributed environment, is one of the important challenges faced by cloud providers. Thus, to address these challenges, this paper proposed a novel location, energy, carbon, and cost-aware (LECC) virtual machine (VM) placement model for geo-distributed cloud DCs. Both online and offline placement problems are tackled. The migration technology is employed in consolidating the VMs to less number of active servers for significant energy reductions. The VM placement problem is formulated as a multi-objective optimization problem and solved by greedy policies. Also, an intelligent machine-learning model is constructed and implemented to leverage the performance of the LECC model. To validate the usefulness of the LECC model, extensive simulations using synthetic and real data are conducted on CloudSim toolkit. The experimental results showed the merits of our proposed LECC model in solving the important VM placement problem.