Multi Objective Virtual Machine Allocation in Cloud Data Centers

In this paper, we propose a Virtual Machine (VM) allocator for Cloud Computing Data Center (DC). We allocate a set of VMs on servers that are interconnected through a three-tier fat-tree network topology. VMs require four different resources: CPU, memory, disk, and bi-directional network bandwidth for communications directed to and coming from the external gateway. Our goal is not to overload computing devices (i.e. allocating more resource than servers' availability) while reducing servers and switches power consumption; in the current proposal, power consumption of each device follows a load-proportional trend. The allocation problem is combinatorial and non-convex, and it is a variant of the multi objective bin packing problem which is NP-Hard. For these reasons, we solve the problem using a particular kind of heuristics called Multi Objective Genetic Algorithm (MOGA) and inspired by the natural process of evolution; MOGA is quite often able to effectively approximate complex problems, such us the one considered. We perform a comparison with a simplified and single-objective formulation of the problem that is solved using CPLEX, while solutions are evaluated using specific quality indicators. The results show how the presented approach solves the allocation problem: MOGA retrieves good quality solutions in less than ten seconds allocating thousands of VMs and obtaining the same results as CPLEX.