Cloud computing is a novel developing computing paradigm where implementations, information, and IT services are given over the internet. The parallel-machine scheduling (Task-Resource) is the important role in cloud computing environment. But parallel-machine scheduling issues are premier that associated with the efficacy of the whole cloud computing facilities. A good scheduling algorithm has to decrease the implementation time and cost along with QoS necessities of the consumers. To overcome the issues present in the parallel-machine scheduling, we have proposed an oppositional learning based grey wolf optimizer (OGWO) on the basis of the proposed cost and time model on cloud computing environment. Additionally, the concept of opposition based learning is used with the standard GWO to enhance its computational speed and convergence profile of the proposed method. The experimental results show that the proposed method outperforms among all methods and provides quality schedules with less memory utilization and computation time.
The demand for massive computing power and storage space has been escalating in various fields and in order to satisfy this need a new technology known as cloud computing is introduced. The capability of providing these services effectively and economically has made cloud computing technology more popular. With the advent of virtualization, IT services being offered have started to shift to cloud computing. Virtualization had paved way for resource availability in an inexhaustible manner. As Cloud Computing is still at its unrefined form and to derive its full potential more analysis is needed. The way in which resources and tasks get allocated in cloud environment requires more analysis. This in turn accounts for the Quality of Services (QoS) of the services offered by cloud service providers. This paper proposes to simulate the Performance-Cost Grey Wolf Optimization (PCGWO) algorithm based to achieve optimization in the process of allocation of resources and tasks in cloud computing domain using CloudSim toolkit. The main purpose is to lower both the processing time and cost in accordance to objective function. The superiority of proposed technique is evident from the simulation results that show a comprehensive reduction in task completion time and cost. Also using this technique more no. of tasks can be efficiently completed within the deadline. Thus the results indicate that in accordance to performance the PCGWO method fares better than existing algorithms
Cloud computing is one of the emerging areas in computing platforms, supporting heterogeneous, parallel and distributed environments. An important challenging issue in cloud computing is task scheduling, which directly influences system performance and its efficiency. The primary objective of task scheduling involves scheduling tasks related to resources and minimizing the time span of the schedule. In this study, we propose a Modified Mean Grey Wolf Optimization (MGWO) algorithm to enhance system performance, and consequently reduce scheduling issues. The main objective of this method is focused upon minimizing the makespan (execution time) and energy consumption. These two objective functions are elaborated in the algorithm in order to suitably regulate the quality of results based on response, in order to achieve a near optimal solution. The implementation results of the proposed algorithm are evaluated using the CloudSim toolkit for standard workloads (normal and uniform). The advantage of the proposed method is evident from the simulation results, which show a comprehensive reduction in makespan and energy consumption. The outcomes of these results show that the proposed Mean GWO algorithm achieves a 8.85% makespan improvement compared to the PSO algorithm, and 3.09% compared to the standard GWO algorithm for the normal dataset. In addition, the proposed algorithm achieves 9.05% and 9.2% improvement in energy conservation compared to the PSO and standard GWO algorithms for the uniform dataset, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.