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Article type: Research Article
Authors: Sermakani, A.M.a; * | Paulraj, D.b
Affiliations: [a] S.A. Engineering College, Thiruverkadu, Chennai, Tamil Nadu, India | [b] Department of Computer Science and Engineering, R.M.K. Engineering College, Chennai, Tamil Nadu, India
Correspondence: [*] Corresponding author. A.M. Sermakani, S.A. Engineering College, Thiruverkadu, Chennai, Tamil Nadu, India. E-mail: [email protected].
Abstract: The contemporary development of cloud is a next generation federated cloud technology envisioned by virtualization to enable cost-efficient usage of computing resources. The resources are intended on scalability as data grows enormously with on demand services. Federated cloud is an efficient networked computing environment that can adopt infrastructure which aims for virtual unlimited pool during on demand services. The challenging task for federated cloud includes managing workloads of individual cloud, progressing virtual machine volumes, cost utilization, fair load distribution. In order to addresses these challenges, this approach uses “Optimized Bit Matrix based Node Acquisition for Federated cloud (BMNF)”. The framework process two different approaches: managing bit matrix and fulfilling load distribution in federated cloud based on cost aware workloads. The formation of bit matrix designed by each member in cloud services that validates load availability status. Load distribution factor concentrates on fair allocation with cost aware policy at individual level. BMNF policy segregates the request among various clouds by analyzing bits patterns. In addition to load distribution using bit matrix, it also focuses on improving cost utilization and targets with better quality of load distribution. The proposed working model is highly efficient with computation and communication overhead for federated cloud.
Keywords: Cloud computing, load distribution, virtualization, federated cloud, virtual machine allocation, quality of service
DOI: 10.3233/JIFS-232897
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 6, pp. 11613-11627, 2023
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