Energy Efficient Resource Allocation for Cloud Computing

Abstract

The resource allocation problem in a cloud computing environment has been shown, in general, to be NP-complete, requiring the development of heuristic techniques. The complexity of the resource allocation problem increases with the size of cloud infrastructure and becomes difficult to solve effectively. The exponential solution space for the resource allocation problem can search using heuristic techniques to obtain a sub-optimal solution at the acceptable time. This thesis presents the resource allocation problem in cloud computing as a linear programming problem, with the objective to minimize energy consumed in computation. This resource allocation problem has been treated using heuristic and meta heuristic approach. All these heuristics from the literature have been selected: adapted, implemented, and analyzed under one set of common assumptions considering Expected time to compute (ETC) task model. These heuristic algorithms operate in two phases, selection of task from the task pool, followed by selection of cloud resource. A set of ten greedy heuristics for resource allocation using the greedy paradigm has been used, that operates in two stages. At each stage a particular input is selected through a selection procedure. The selection procedure can be realized using a 2-phase heuristic. In particular, we have used 'FcfsRand', 'FcfsRr','FcfsMin','FcfsMax', 'MinMin', 'MedianMin', 'MaxMin', 'MinMax', 'MedianMax', and 'MaxMax'. The simulation results indicate in the favor of MaxMax. The novel genetic algorithm framework has been proposed for task scheduling to minimize the energy consumption in cloud computing infrastructure. The performance of the proposed GA resource allocation strategy has been compared Random and Round Robin scheduling using in house simulator. The experimental results show that the GA based scheduling model outperforms the existing Rondom and Round Robin scheduling models.