Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Kamlesh Lakhwani, Gajanand Sharma, Ramandeep Sandhu, Naresh Kumar Nagwani, Sandeep Bhargava, Varsha Arya and Ammar Almomani
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Kamlesh Lakhwani: Department of Computer Science and Engineering, JECRC University, Jaipur, India
Gajanand Sharma: Department of Computer Science and Engineering, JECRC University, Jaipur, India
Ramandeep Sandhu: School of Computer Science Engineering, Lovely Professional University (LPU), Jalandhar, India
Naresh Kumar Nagwani: Department of Computer Science and Engineering, National Institute of Technology, Raipur, India
Sandeep Bhargava: EHR Logic IT Services Pvt. Ltd., India
Varsha Arya: Department of Business Administration, Asia University, Taiwan & Lebanese American University, Beirut, Lebanon & Chandigarh University, Chandigarh, India
Ammar Almomani: School of Computing, Skyline University College, Sharjah, UAE & Al- Balqa Applied University, Jordan

International Journal of Cloud Applications and Computing (IJCAC), 2023, vol. 13, issue 1, 1-25

Abstract: Scheduling large-scale and resource-intensive workflows in cloud infrastructure is one of the main challenges for cloud service providers (CSPs). Cloud infrastructure is more efficient when virtual machines and other resources work up to their full potential. The main factor that influences the quality of cloud services is the distribution of workflow on virtual machines (VMs). Scheduling tasks to VMs depends on the type of workflow and mechanism of resource allocation. Scientific workflows include large-scale data transfer and consume intensive resources of cloud infrastructures. Therefore, scheduling of tasks from scientific workflows on VMs requires efficient and optimized workflow scheduling techniques. This paper proposes an optimised workflow scheduling approach that aims to improve the utilization of cloud resources without increasing execution time and execution cost.

Date: 2023
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