Hybrid Invasive Weed Optimization with Tabu Search Algorithm for an Energy and Deadline Aware Scheduling in Cloud Computing

Abstract

The current existing high flexibility, profitability, and potential have made cloud computing extremely popular among the companies. This is used for improving and applying resources in an efficient manner and optimize makespan of the tasks. Scheduling is easy while there are only a few tasks to complete with few resources. Contrastingly, at the time the users forward several demands to the environment of the cloud, there may be a need for optimally selecting and allocating resources for achieving the desired quality of service that makes scheduling challenging. In this work, using intelligent metaheuristic algorithms for processing the requests and tasks of users in energy-aware scheduling made for a deadline is proposed. Genetic Algorithm (GA) the evolutionary algorithm that is inspired by the natural process of selection and the evolution theory. The Invasive Weed Optimization (IWO) was yet another novel stochastic based on the population that was a derivative-free technique of optimization inspired by the growth of the weed plants. The TABU Search (TS) was a generalization technique of local search where the TABU list was used for preventing cycling and further generating the candidates of the neighborhood. A hybrid GA with the TS (GA-TS) with a hybrid IWO with TS (IWO-TS) has been proposed for the energy and deadline aware scheduling. The framework further offers optimization of energy and performance. The primary purpose of this algorithm has been to improve deadline and scheduling in cloud computing along with local as well as global search algorithms. This framework will offer optimization of performance and energy. The reason behind presenting this algorithm was improving both scheduling and deadline in cloud computing using both local and global algorithms and results proved the algorithm to have better results.