An energy-efficient black widow-based adaptive VM placement approach for cloud computing

The demand for cloud-based computation is increasing exponentially in the age of information technology virtualization, which is increasing data centre energy consumption and service level agreement (SLA) violations. This contributes to global warming and excessive load on existing infrastructure. As a result, it is critical to improve the resource utilisation of cloud data centres (CDCs). Virtual machine (VM) consolidation is the most effective method for optimising resource utilisation in CDCs. In this context, this research proposes the global optimal search-based meta-heuristic algorithm black widow adaptive VM placement (BWAVP) approach-based VM consolidation that combines energy conservation, resource utilisation and required quality of services with accurate VM-PM mapping. The investigation of the BWAVP approach shows that it reduces energy consumption by 18% on average when compared to other VM placement approaches, and reduces SLA violations, and VM migrations by more than 80%. Further, The research proposed a localized adaptive over and underutilisation host detection technique that further reduces energy consumption by 10% while maintaining the quality of services (QoSs) at the same level.