ItemArticle

A scalable scheduling and resource management framework for cloud-native B2B applications

Title
A scalable scheduling and resource management framework for cloud-native B2B applications
Creator
Komarasamy, Dinesh; Rajavel, Rajkumar; Harimoorthy, Karthikeyan; Pitchai, Aravind
Description
In modern cloud computing environments, customers increasingly depend on on-demand resource provisioning to handle dynamic workloads. However, fluctuations in job arrival rates can result in prolonged queue times, which negatively affect overall system performance. Although existing scheduling algorithms provide efficient job management, they often fail to account for the combined impact of queue delays and the need for flexible resource provisioningparticularly in business-critical applications. In order to tackle these issues, the paper proposes a new Optimized Job Scheduling and Resource Scaling (OJSRS) algorithm designed to improve job execution efficiency and support elastic resource management in cloud environments. The OJSRS algorithm integrates two key components: Tree-based Job Scheduling (TJS) and Automated Resource Scaling and Scheduling (ARSS). The TJS component constructs a hierarchical structure that concurrently maps incoming jobs to the most suitable Virtual Machines (VMs), thereby minimizing queue delays. Meanwhile, ARSS adjusts resource allocation dynamically, increasing or decreasing capacity according to workload requirements and cloud service provider policies, enabling responsive and adaptive provisioning. Experimental results show that the OJSRS algorithm increases resource utilization by approximately 510% and accelerates job completion through proactive resource scaling. This approach provides a significant performance advantage for cloud-native business applications that require both efficiency and scalability. The Author(s) 2025.
Source
Scientific Reports;Volume;15;Issue;1;Article No.;44500;
Date
01-01-2025
Publisher
Nature Research
Subject
Automated resource scaling; Business-to-Business applications; Cloud computing; Cloud native platform; Decent work and economic growth; Job scheduling; Load balancing
Coverage
Komarasamy D., Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, India; Rajavel R., Department of AI, ML & Data Science, Christ University, Bangalore, India; Harimoorthy K., Department of Computer Science and Engineering, Christ University, Bangalore, India; Pitchai A., Department of Electrical and Computer Engineering, Mattu University, Mattu, Ethiopia
Rights
All Open Access; Gold Open Access; Green Open Access
Relation
ISSN: 20452322;
Format
online
Language
English
Type
Article

Citation

Komarasamy, Dinesh; Rajavel, Rajkumar; Harimoorthy, Karthikeyan; Pitchai, Aravind, “A scalable scheduling and resource management framework for cloud-native B2B applications,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22533.

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