Metaheuristic Scheduling for Cloud: A Survey

Tsai, Chun-Wei; Rodrigues, Joel J. P. C.

doi:10.1109/jsyst.2013.2256731

Cited by 214 publications

(89 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Many meta-heuristic, heuristic and hybrid algorithms have been proposed, including ACO, GA, PSO, etc. (Tsai and Rodrigues 2014;Kalra and Singh 2015). In particular, workflow scheduling (Masdari et al 2016) has also been investigated, and corresponding scheduling schemes including meta-heuristic-based scheduling, heuristic workflow scheduling, and their hybrid have been extensively studied (Xu et al 2009).…”

Section: Scheduling In Cloud Computingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

View full text Add to dashboard Cite

For the past eight years, cloud manufacturing as a new manufacturing paradigm has attracted a large amount of research interest worldwide. The aim of cloud manufacturing is to deliver on-demand manufacturing services to consumers over the Internet. Scheduling is one of the critical means for achieving the aim of cloud manufacturing. Thus far, about 158 articles have been published on scheduling in cloud manufacturing. However, research on scheduling in cloud manufacturing faces numerous challenges. Thus, there is an urgent need to ascertain the current status and identify issues and challenges to be addressed in the future. Covering articles published on the subject over the past eight years, this article aims to provide a state-of-the-art literature survey on scheduling issues in cloud manufacturing. A detailed statistical analysis of the literature is provided based on the data gathered from the Elsevier's Scopus abstract and citation database. Typical characteristics of scheduling issues in cloud manufacturing are systematically summarised. A comparative analysis of scheduling issues in cloud manufacturing and other scheduling issues such as cloud computing scheduling, workshop scheduling and supply chain scheduling is also carried out. Finally, future research issues and challenges are identified.

show abstract

Section: Scheduling In Cloud Computingmentioning

confidence: 99%

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Liu

Wang

et al. 2018

International Journal of Production Research

221

View full text Add to dashboard Cite

show abstract

“…In the literature, metaheuristics are widely used to obtain good quality solutions within acceptable execution time and there exist already several surveys on metaheuristics [228,229]. In this section, the survey on metaheuristics will be divided into three parts.…”

Section: Metaheuristicsmentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

View full text Add to dashboard Cite

Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

show abstract

“…However, due to the NP-complete nature of the problem, no feasible exact (optimal) solutions are proposed. On the other hand, metaheuristic algorithms which employ iterative strategies to find solutions in a reasonable time [24], have shown their effectiveness for solving a wide range of hard-to-solve combinatorial and multi-objective optimization problems. The most popular metaheuristic algorithms that are presented to solve NP problems are genetic algorithm (GA) [25], simulated annealing (SA) [26], tabu search (TS) [27], particle swarm optimization (PSO) [28], ant colony optimization (ACO) [29] and artificial bee colony optimization (ABC) [30].…”

Section: Related Workmentioning

confidence: 99%

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Attiya¹,

Zhang²

2017

IJCA

View full text Add to dashboard Cite

Recent advances in various areas such as networking, information and communication technologies have greatly boosted the potential capabilities of cloud computing and made it become more prevalent in recent years. Cloud computing is a promising computing paradigm that facilitates the delivery of IT infrastructure, platforms, and applications of any kind to consumers as services over the internet. Although cloud computing systems nowadays provide better ways to accomplish the job requests in terms of responsiveness and scalability under various workloads, scheduling of jobs or tasks in cloud environment is still NPcomplete and complex in nature due to the dynamicity of resources and on-demand user application requirements. In this paper, a simplified version of particle swarm optimization (PSO) algorithm is proposed to solve the job scheduling problem in cloud computing environment. To evaluate the performance of the proposed approach, this study compares the proposed PSO strategy with genetic algorithm (GA), by having both of them implemented on CloudSim toolkit. The results obtained demonstrate that the presented PSO algorithm can significantly reduce the makespan of job scheduling problem compared with the other metaheuristic algorithm evaluated in this paper.

show abstract

Metaheuristic Scheduling for Cloud: A Survey

Cited by 214 publications

References 88 publications

Scheduling in cloud manufacturing: state-of-the-art and research challenges

Scheduling in cloud manufacturing: state-of-the-art and research challenges

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

A Simplified Particle Swarm Optimization for Job Scheduling in Cloud Computing

Contact Info

Product

Resources

About