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CMODLB: an efficient load balancing approach in cloud computing environment

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Abstract

A hybrid of supervised (artificial neural network), unsupervised (clustering) machine learning, and soft computing (interval type 2 fuzzy logic system)-based load balancing algorithm, i.e., clustering-based multiple objective dynamic load balancing technique (CMODLB), is introduced to balance the cloud load in the present work. Initially, our previously introduced artificial neural network-based dynamic load balancing (ANN-LB) technique is implemented to cluster the virtual machines (VMs) into underloaded and overloaded VMs using Bayesian optimization-based enhanced K-means (BOEK-means) algorithm. In the second stage, the user tasks are scheduled for underloading VMs to improve load balance and resource utilization. Scheduling of tasks is supported by multi-objective-based technique of order preference by similarity to ideal solution with particle swarm optimization (TOPSIS-PSO) algorithm using different cloud criteria. To realize load balancing among PMs, the VM manager makes decisions for VM migration. VM migration decision is done based on the suitable conditions, if a PM is overloaded, and if another PM is minimum loaded. The former condition balances load, while the latter condition minimizes energy consumption in PMs. VM migration is achieved through interval type 2 fuzzy logic system (IT2FS) whose decisions are based on multiple significant parameters. Experimental results show that the CMODLB method takes 31.067% and 71.6% less completion time than TaPRA and BSO, respectively. It has maintained 65.54% and 68.26% less MakeSpan than MaxMin and R.R algorithms, respectively. The proposed method has achieved around 75% of resource utilization, which is highest compared to DHCI and CESCC. The use of novel and innovative hybridization of machine learning, multi-objective, and soft computing methods in the proposed algorithm offers optimum scheduling and migration processes to balance PMs and VMs.

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Acknowledgment

The first author (Sarita Negi) acknowledges Prof. Man Mohan Singh Ruthann, Dr. Rohit Mahar, and the Department of Computer Science and Engineering, H N B Garhwal University (Srinagar Garhwal), Uttarakhand, for their immense support and resources.

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Authors and Affiliations

  1. Computer Science and Engineering, Uttarakhand Technical University, Dehradun, 248007, Uttarakhand, India

    Sarita Negi

  2. Computer Science and Engineering, Hemvati Nandan Bahuguna Garhwal University, Garhwal, Srinagar, 249161, Uttarakhand, India

    Man Mohan Singh Rauthan & Neelam Panwar

  3. Computer Science and Engineering, Bipin Tripathi Kumaon Institute of Technology, Dwarahat, Almora, 263653, Uttarakhand, India

    Kunwar Singh Vaisla

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  1. Sarita Negi
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Correspondence to Sarita Negi.

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Negi, S., Rauthan, M.M.S., Vaisla, K.S. et al. CMODLB: an efficient load balancing approach in cloud computing environment. J Supercomput 77, 8787–8839 (2021). https://doi.org/10.1007/s11227-020-03601-7

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