A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment

2. Related Work

X. Wei (2020) in their journal proposed a task scheduling optimization strategy using improved ant colony in cloud computing environment (TSOAC). In their proposed algorithm the author has addressed the issue of scheduling algorithms falling into a local optimal solution and combined their solution with efficient resource utilization to design a task scheduling optimal function to develop a task scheduling satisfaction function. A reward and punishment coefficients have been introduced to speed up the task allocation, but without focusing on the type of job and which VM it will be allocated to. The author has just mapped a correspondence with the first VM with the first job, second VM with second job etc. Drawing inspiration from this algorithm, the proposed algorithm in this paper not only addresses the problem of efficient path finding but also concentrates on doing efficient load balancing by paying attention to the particular types of jobs and allocation them VMs’ according to their need.

Jun-qing Li, Yun-qi Han et al. (2020) in their journal has proposed a multi objective artificial bee colony algorithm for flexible task scheduling in a cloud computing environment (MOABC). They have clubbed Artificial bee colony (ABC) with hybrid flowshop scheduling (HFS) to find out the most efficient server from a pool of servers.

Apart from multiple assumptions like processing of one job in one device only, not considering setup time on the same and also not considering setup time between two consecutive jobs in the same machine causes another major problem. In the first stage tasks are initially assigned to those which has minimum completion time. This might result the algorithm in falling into a local optimum and also a concern arises regarding proper resource utilization.