Scalarization Methods for Many-Objective Virtual Machine Placement of Elastic Infrastructures in Overbooked Cloud Computing Data Centers Under Uncertainty

Cloud computing datacenters provide thousands to millions of virtual machines (VMs) on-demand in highly dynamic environments, requiring quick placement of requested VMs into available physical machines (PMs). Due to the randomness of customer requests, the Virtual Machine Placement (VMP) should be formulated as an online optimization problem. The first part of this work proposes a formulation of a provider-oriented VMP problem for federated-cloud considering the optimization of the following objective functions: (i) power consumption, (ii) economical revenue, (iii) quality of service and (iv) resource utilization. In order to analyze alternatives to solve the formulated problem, an experimental comparison of five different online deterministic heuristics against an offline memetic algorithm with migration of VMs was performed, considering several experimental workloads. Simulations indicate that First-Fit Decreasing algorithm (A4) outperforms other evaluated heuristics on average. Experimental results proved that an offline memetic algorithm improves the quality of the solutions with migrations of VMs at the expense of placement reconfigurations. The second part of this work presents a two-phase schema formulation of a VMP problem considering the optimization of three objective functions: (i) power consumption, (ii) economical revenue, and (iii) resource utilization, in an IaaS environment with elasticity and overbooking capabilities. The two-phase schema formulation describes that the allocation of the VMs can be separated into two sub-problems, the incremental allocation (iVMP) and the reconfiguration of a placement (VMPr). In order to portray the dynamic nature of an IaaS environment a customizable workload trace generator was developed to simulate uncertainty in the scenarios with elasticity and overbooking of resources in VM requests. To analyze alternatives to solve the formulated problem, an experimental comparison of three different objective function scalarization methods as part of the iVMP and VMPr was performed considering several experimental workloads. Simulations indicate that the Euclidean distance to origin outperforms other evaluated scalarization methods on average. Experimental results proved that the Euclidean distance is preferable over the other scalarizatiom methods to improve the values of the power consumption objective function.