@article {10.3844/jcssp.2018.908.918,
article_type = {journal},
title = {Application of Viral System Algorithm in Load Balancing of Cloud Environment},
author = {Tiwari, Damodar and Singh, Shailendra and Sharma, Sanjeev},
volume = {14},
number = {7},
year = {2018},
month = {Jul},
pages = {908-918},
doi = {10.3844/jcssp.2018.908.918},
url = {https://thescipub.com/abstract/jcssp.2018.908.918},
abstract = {As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.},
journal = {Journal of Computer Science},
publisher = {Science Publications}
}