Welcome to the InfoSci Platform
Reference Hub4
Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms

Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms

Shikha Mehta, Parmeet Kaur
Copyright: © 2019 |Pages: 22
ISBN13: 9781522558521|ISBN10: 1522558527|EISBN13: 9781522558538
DOI: 10.4018/978-1-5225-5852-1.ch008
Cite Chapter Cite Chapter

MLA

Mehta, Shikha, and Parmeet Kaur. "Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms." Nature-Inspired Algorithms for Big Data Frameworks, edited by Hema Banati, et al., IGI Global, 2019, pp. 196-217. https://doi.org/10.4018/978-1-5225-5852-1.ch008

APA

Mehta, S. & Kaur, P. (2019). Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms. In H. Banati, S. Mehta, & P. Kaur (Eds.), Nature-Inspired Algorithms for Big Data Frameworks (pp. 196-217). IGI Global. https://doi.org/10.4018/978-1-5225-5852-1.ch008

Chicago

Mehta, Shikha, and Parmeet Kaur. "Scheduling Data Intensive Scientific Workflows in Cloud Environment Using Nature Inspired Algorithms." In Nature-Inspired Algorithms for Big Data Frameworks, edited by Hema Banati, Shikha Mehta, and Parmeet Kaur, 196-217. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-5852-1.ch008

Export Reference

Mendeley
Favorite

Abstract

Workflows are a commonly used model to describe applications consisting of computational tasks with data or control flow dependencies. They are used in domains of bioinformatics, astronomy, physics, etc., for data-driven scientific applications. Execution of data-intensive workflow applications in a reasonable amount of time demands a high-performance computing environment. Cloud computing is a way of purchasing computing resources on demand through virtualization technologies. It provides the infrastructure to build and run workflow applications, which is called ‘Infrastructure as a Service.' However, it is necessary to schedule workflows on cloud in a way that reduces the cost of leasing resources. Scheduling tasks on resources is a NP hard problem and using meta-heuristic algorithms is an obvious choice for the same. This chapter presents application of nature-inspired algorithms: particle swarm optimization, shuffled frog leaping algorithm and grey wolf optimization algorithm to the workflow scheduling problem on the cloud. Simulation results prove the efficacy of the suggested algorithms.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.