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Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud

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Abstract

Scheduling is a considerable problem in cloud to increase the quality of service provisioning with higher resource efficiency. The conventional task scheduling algorithms designed for balancing load in a cloud environment. But, minimizing the Service level agreement (SLA) violation, resource wastage and the energy consumption during the task scheduling process was not solved effectively. In order to resolve these limitations, a new virtual machine (VM) consolidation technique called Nature-inspired Meta-heuristic Threshold based firefly optimized lottery scheduling (NMT-FOLS) Technique is proposed. Initially, user requests are transmitted to the cloud server (CS). Next, NMT-FOLS Technique utilizes Adaptive Regressive Holt–Winters Workload Predictor to discover the workload state as normal or timely or bursty. Using the workload predictor result, NMT-FOLS Technique exploits task scheduler to allocate user requested tasks to optimal VMs. NMT-FOLS Technique applies multi-objective firefly optimization based task scheduling algorithm in normal workload state and multi-objective firefly optimized lottery scheduling algorithm in timely and bursty workload situations. At last, the selected scheduling algorithm in NMT-FOLS Technique assigns the user requested task to best VMs in CS to perform the demanded services. Hence, NMT-FOLS Technique gets better task scheduling performance to balance normal, timely and bursty workloads in CS with lesser time. NMT-FOLS Technique decreases the SLA violation in cloud through scheduling of user tasks to optimal VM. NMT-FOLS performs an experimental process using metrics such as SLA violation, task scheduling efficiency (TSE), makespan, energy utilization and memory usage with number of user-requested tasks over the considered Amazon dataset. From the experimental result, the NMT-FOLS technique improves scheduling efficiency up to 94.6% and reduces the SLA violations and energy utilization from different test cases on an average to 78%, and 63% compared to state-of-the-art works.

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  1. Vellore Institute of Technology (VIT), Chennai, 600124, India

    J. Prassanna & Neelanarayanan Venkataraman

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Prassanna, J., Venkataraman, N. Adaptive regressive holt–winters workload prediction and firefly optimized lottery scheduling for load balancing in cloud. Wireless Netw 27, 5597–5615 (2021). https://doi.org/10.1007/s11276-019-02090-8

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