Abstract
Virtualization is one of the key features of cloud computing, where the physical machines are virtually divided into several virtual machines in the cloud. The user’s tasks are run on these virtual resources as per the requirements. When the user requests the services to the cloud, the user’s tasks are allotted to the virtual resources depending on their needs. An efficient scheduling mechanism is required for optimizing the involved parameters. Scientific workflows deals with a large amount of data with dependency constraints and is used to simplify the applications in the diverse scientific domains. Scheduling the workflow in cloud computing is a well-known NP-hard problem. Deploying such data- and compute-intensive workflow on the cloud needs an efficient scheduling algorithm. In this paper, we have proposed a multi-objective model based hybrid algorithm (HPSOGWO), which combines the desirable characteristics of two well-known algorithms, particle swarm optimization (PSO), and grey wolf optimization (GWO). The results are analyzed under complex real-world scientific workflows such as Montage, CyberShake, Inspiral, and Sipht. We have considered the two essential parameters: total execution time and total execution cost while working in the cloud environment. The simulation results show that the proposed algorithm performs well compared to other state-of-the-art algorithms such as round-robin (RR), ant colony optimization (ACO), heterogeneous earliest time first (HEFT), and particle swarm optimization (PSO).
Highlights
Cloud Computing is a buzzing word from decades in computer science as it offers advancements like hiding and abstraction of complexity, visualized resources, and efficient use of distributed resources
The HPSOGWO algorithm is compared with the round-robin (RR) [37], ant colony optimization (ACO) [38], heterogeneous earliest time first (HEFT) [39], and particle swarm optimization (PSO) [12] algorithms
A novel hybrid meta-heuristic algorithm based on a multiobjective model called HPSOGWO is proposed in the present paper
Summary
Cloud Computing is a buzzing word from decades in computer science as it offers advancements like hiding and abstraction of complexity, visualized resources, and efficient use of distributed resources. Pegasus project published some of the realistic scientific workflows like Montage, CyberShake, Epigenomics, LIGO, and SIPHT [6] [7] It is good to find a near-optimal solution to the workflow scheduling problem with a meta-heuristic algorithm. Many meta-heuristic optimization algorithms have been used to solve workflow problems in cloud computing. Pandey et al [12] used Particle Swarm Optimization (PSO) to schedule workflow applications in a cloud computing environment. We proposed a hybrid algorithm combining Particle Swarm Optimization (PSO) and Grey Wolf Optimize (GWO), named HPSOGWO. The HPSOGWO is tested on the scientific workflow like montage, cybershake, inspiral, and sipht to optimize total execution cost and time. We review some of the scheduling algorithms used in cloud computing
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
