Multi-Objective Optimization for Optimal VM Allocation using Tuna Swarm Optimization

Limitations of physical machines (PMs) and hence computation power are well known to humans today, due to which virtual machines (VMs) are used to enhance the computation capability and reduce the cost and space consumption for physical hardware. To leverage these VMs optimally, the placement of these VMs into PMs or clusters needs to be worked upon. Previously people have worked towards this allocation task using various approaches such as nature-inspired algorithms, decision-based algorithms, ML-driven algorithms, etc. Over the years, new algorithms are constantly being created which enhance the quality of the optimization task at hand. In this paper, we have compared Tuna Swarm Optimization with previously implemented techniques for multi-objective optimization in the placement of VMs.