A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques
- PDF / 1,195,091 Bytes
- 9 Pages / 595.276 x 790.866 pts Page_size
- 17 Downloads / 196 Views
ORIGINAL RESEARCH
A reactive search optimization algorithm for scientific workflow scheduling using clustering techniques M. Karpagam1 · K. Geetha1 · C. Rajan2 Received: 2 April 2020 / Accepted: 14 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Cloud computing is the style that can give plenty of shared pool resources such as hardware or software to clients based on requests from the internet. These resources are then scaled up automatically based on the specifications of the clients. Workflow scheduling optimization is an area of research activities in infrastructure as a service (IaaS) of the cloud. This problem is NP-complete. Thus, building a workflow scheduler that is optimum, having a reasonable level of performance and speed of computation, can be quite challenging in a distributed cloud environment. Metaheuristic algorithms may be improved in terms of their solution and its quality and speed of convergence utilizing combining it with other metaheuristic algorithms or any other algorithms that are metaheuristic based on local search. Shuffled frog leaping algorithm (SFLA) was acknowledged a metaheuristic performing heuristic search with a heuristic function (mathematical function) seeking solutions to combinatorial optimization problems. An optimization ratio on makespan %, resource utilization and computational cost performs better for SFLA–RSO with clustering when the number of tasks are increased. Keywords Cloud computing · Workflow scheduling · NP-complete problem · Metaheuristic algorithm · Shuffled frog leaping algorithm (SFLA)
1 Introduction With Internet connection, the clients can rent the necessary services through web browsers. The increase in deploying applications in the environment of the infrastructure as a service (IaaS) cloud computing, distribution of the tasks of the workflow to certain instances of the cloud. This is used for decreasing cost and runtime that has emerged to be a very important challenge. Scheduling permits optimal resource allocation among the specified tasks for a finite time and a quality of service that is desired. More formally, the problem of scheduling will include tasks that were scheduled on the resources subject to any other constraint for optimizing other objective functions. The primary aim was to build a new schedule that can specify the time and the resource for each
* M. Karpagam [email protected] 1
Department of CSE, Excel Engineering College, Salem, Tamil Nadu, India
Department of IT, K S Rangasamy College of Technology, Namakkal, Tamil Nadu, India
2
task that is executed (Kalra and Singh 2015). Scheduling of the cloud is an NP-hard problem. The problem workflow scheduling faced for an innate issue obtained from diverse computing environments used, to address such problems of scheduling is an ongoing research area (Xiao et al. 2019). But, the environments of diverse computing are not always easy to set up and tend to have the capacity of providing uniform performance. Furthermore, the objective of different
Data Loading...
