BMTVDS2: a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements

• Published in: SN applied sciences Vol. 5; no. 8; pp. 221 - 12
• Main Authors: Gaikwad, Amol D., Singh, Kavita R., Kamble, Shailesh D.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2023; Springer Nature B.V; Springer
• Subjects: Agreements; Algorithms; Applied and Technical Physics; Biomimetics; Chemistry/Food Science; Cloud; Datasets; Deadlines; Deep learning; Earth Sciences; Energy consumption; Engineering; Environment; Genetic algorithms; GWO; LCA; Materials Science; Neural networks; Optimization; Optimization algorithms; Provisioning; Real time; Resource allocation; Scheduling; SLA; Task; Task scheduling; Task; Cloud; Diversity; Deadline; SLA; Scheduling; LCA; Dependency; GWO; Heterogenous
• ISSN: 2523-3963; 2523-3971
• DOI: 10.1007/s42452-023-05452-2

This paper discusses the design of a novel hybrid bioinspired model for task-and-VM-dependency and deadline aware scheduling via dual service level agreements. The model uses a combination of grey wolf optimization with the league championship algorithm, to perform efficient scheduling operations. These optimization techniques model a fitness function that incorporates task make-span, task deadline, mutual dependencies with other tasks, the capacity of VMs, and energy needed for scheduling operations. This assists in improving its scheduling performance for multiple use cases. To perform these tasks, the model initially deploys a task-based service level agreement (SLA) method, which assists in enhancing task and requesting-user diversity. This is followed by the design of a VM-based SLA model, which reconfigures the VM's internal characteristics to incorporate multiple task types. The model also integrates deadline awareness along with task-level and VM-level dependency awareness, which assists in improving its scheduling performance under real-time task and cloud scenarios. The proposed model is able to improve cloud utilization by 8.5%, increase task diversity by 8.3%, reduce the delay needed for resource provisioning by 16.5%, and reduce energy consumption by 9.1%, making for a wide variety of real-time cloud deployments.