CMODLB: an efficient load balancing approach in cloud computing environment

• Published in: The Journal of supercomputing Vol. 77; no. 8; pp. 8787 - 8839
• Main Authors: Negi, Sarita, Rauthan, Man Mohan Singh, Vaisla, Kunwar Singh, Panwar, Neelam
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.08.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial neural networks; Cloud computing; Clustering; Compilers; Completion time; Computer Science; Decisions; Dynamic loads; Energy consumption; Fuzzy logic; Interpreters; Learning theory; Load balancing; Machine learning; Neural networks; Overloading; Particle swarm optimization; Processor Architectures; Programming Languages; Resource scheduling; Resource utilization; Soft computing; Task scheduling; Virtual environments; ANN; K-mean clustering; Load balancing; Interval type 2 fuzzy set; VM migration; Machine learning
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-020-03601-7

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.