Novel hybrid ANN and clustering inspired load balancing algorithm in cloud environment

• Published in: Applied soft computing Vol. 113; p. 107963
• Main Authors: Negi, Sarita, Panwar, Neelam, Rauthan, Man Mohan Singh, Vaisla, Kunwar Singh
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-12-2021
• Subjects: ANN; Cloud computing; Clustering; K-means; Load balancing; PSO; Soft computing; K-means; Cloud computing; ANN; Soft computing; Clustering; Load balancing; PSO
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2021.107963

Load balancing in the cloud environment is a NP-hard problem. To address this problem, a new hybridized soft computing inspired technique named Clustering based Artificial Neural Network for Dynamic Load Balancing (CANN-DLB) is introduced. Artificial Neural Network aims to achieve an optimized load of VMs by training the cloud environment. Back-propagation ANN is performed to calculate the optimized Virtual Machine (VM) load in cloud systems for improving QoS. The CANN-DLB technique uses K-means clustering algorithm over calculated VM loads and clusters them into under-loaded and over-loaded VMs. The scheduling of homogeneous independent non-preemptive tasks is performed using Particle Swarm Optimization (PSO) technique. CloudSim (Cloud Simulator) tool has been used to implement the proposed algorithm. The performance of the CANN-DLB technique has been achieved for Space Shared and Time Shared VM task scheduler methods. The simulation results of the CANN-DLB algorithm are compared with existing load balancing and scheduling algorithms with the objectives to improve load balance fairness. Results of CANN-DLB have achieved 69.5%, 96.9%, 96.0% and 97.4% less processing cost and 87.12%, 87.12%, 87.57% and 81.78% less degree of imbalance compared to CM-eFCFS, Static RR, MinMin and MaxMin method respectively. The system fairness with respect to system load is calculated to analyze CANN-DLB algorithm performance on heavy load. The proposed algorithm achieved 12% higher load fairness than the existing Two-level method and proves that the introduced model works better in dynamic scenarios. Plotted graphs show that the proposed idea is innovative for load balancing in a dynamic cloud environment. This hybridization of the ANN and K-means clustering method produces remarkable results as compared to existing algorithms for different cases.
•The work introduced a new hybridized soft computing inspired technique named Clustering based Artificial Neural Network for Dynamic Load Balancing (CANN-DLB).•Back-propagation ANN and K-Means algorithm are performed to calculate the optimized VM loadin cloud systems for improving QoS.•Runtime incoming user tasks are allocated to underloaded VMs to maintain load balancing using PSO technique.•CANN-DLB achieved 0.99% of resource utilization which is higher than existing algorithms and 69.5%, 96.9%, 96.0% and 97.4% less processing cost than previous algorithms respectively.•For Proposed CANN-DLB, degree of imbalance is reduced to 87.12%, 87.12%, 87.57% and 81.78% than CM-eFCFS, Static RR, MinMin and MaxMin method respectively.