Multi-objective communication-aware optimization for virtual machine placement in cloud datacenters

• Published in: Sustainable computing informatics and systems Vol. 28; p. 100374
• Main Authors: Farzai, Sara, Shirvani, Mirsaeid Hosseini, Rabbani, Mohsen
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2020
• Subjects: Cloud computing; Communication-Awareness; GA meta-heuristic; Multi-Objective optimization; Virtual machine placement; Cloud computing; Multi-Objective optimization; Communication-Awareness; Virtual machine placement; GA meta-heuristic
• ISSN: 2210-5379
• DOI: 10.1016/j.suscom.2020.100374

This paper formulates a new multi-objective virtual machine placement (VMP) problem, which is a challenging task in cloud datacenters (DCs). In cloud environment, there are two stakeholders, namely, users and providers. Both sides try to take more benefit whereas a trade-off between conflicting benefits is crucial. From providers’ perspective, power consumption and resource wastage are two objectives to be optimized whereas gaining high quality of service (QoS) is a critical point for users. The unpleasant issue that a user endures in the cloud environment is network delay; this is affected by common bandwidth linkage which is shared between different users’ applications; the reason for considering bandwidth usage optimization as the third objective function in users’ viewpoint. However, inefficient network bandwidth usage has drastic impact on overall performance even makes network links to get saturated and throttles communication-intensive applications. Therefore, VMs with high affinity and traffic dependency must be physically placed as close as possible so less traffic is sent on network layers. To figure out this combinatorial multi-objective problem, we extend a hybrid multi-objective genetic-based optimization solution. To evaluate this work, we conducted extensive scenarios with variable correlation coefficients between resources in requested VMs. The simulation results prove that our proposed hybrid meta-heuristic algorithm outperforms against state-of-the-art ACO-based, well-known heuristic-based FFD algorithms, and random-based approach in terms of total power consumption, resource wastage, the total data transfer rate in network, and number of active servers in use. Also, the simulations in larger search space demonstrated proposed approach has high potential of scalability.