An Efficient Workflow Scheduling Using Genetically Modified Golden Jackal Optimization With Recurrent Autoencoder in Cloud Computing

Saurav Tripathi and Sarsij Tripathi

International Journal of Network Management, 2025, vol. 35, issue 1

Abstract: In this paper, a novel workflow scheduling framework is proposed using genetically modified golden jackal optimization (GM‐GJO) with recurrent autoencoder. An integrated autoencoder and bidirectional gated recurrent unit (iAE‐BiGRU) are used to forecast the number of virtual machines (VMs) needed to manage the system's present workload. The following step involves assigning the tasks of several workflows to cloud VMs through the use of the GM‐GJO method for multiworkflow scheduling. GM‐GJO provides optimal workflow scheduling by considering minimal maximizing utilization rate, minimizing makespan, and minimizing the number of deadline missed workflows. The proposed approach attempts to allocate the best possible set of resources for the workflows based on objectives such as deadline, cost, and quality of service (QoS). Extensive experiments were conducted with the CloudSIM tool, and the performance is evaluated in terms of scheduling length ratio, cost, QoS, etc. The execution time of 513.45 ms is achieved with a Sipht workflow of 30 tasks. When comparing the suggested strategy to the current methodologies, the suggested approach performs better.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1002/nem.2318

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:intnem:v:35:y:2025:i:1:n:e2318

Access Statistics for this article

More articles in International Journal of Network Management from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().