Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment

Kamlesh Lakhwani, Gajanand Sharma, Ramandeep Sandhu, Naresh Kumar Nagwani, Sandeep Bhargava, Varsha Arya, Ammar Almomani

Source Title: International Journal of Cloud Applications and Computing (IJCAC)13(1)

ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781668479810|DOI: 10.4018/IJCAC.324809

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MLA

Lakhwani, Kamlesh, et al. "Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment." IJCAC vol.13, no.1 2023: pp.1-25. http://doi.org/10.4018/IJCAC.324809

APA

Lakhwani, K., Sharma, G., Sandhu, R., Nagwani, N. K., Bhargava, S., Arya, V., & Almomani, A. (2023). Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment. International Journal of Cloud Applications and Computing (IJCAC), 13(1), 1-25. http://doi.org/10.4018/IJCAC.324809

Chicago

Lakhwani, Kamlesh, et al. "Adaptive and Convex Optimization-Inspired Workflow Scheduling for Cloud Environment," International Journal of Cloud Applications and Computing (IJCAC) 13, no.1: 1-25. http://doi.org/10.4018/IJCAC.324809

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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.