An optimised cuckoo-based discrete symbiotic organisms search strategy for tasks scheduling in cloud computing environment

Currently, the cloud computing paradigm is experiencing rapid growth as there is a shift from other distributed computing methods and traditional IT infrastructure towards it. Consequently, optimised task scheduling techniques have become crucial in managing the expanding cloud computing environment. In cloud computing, numerous tasks need to be scheduled on a limited number of diverse virtual machines to minimise the imbalance between the local and global search space; and optimise system utilisation. Task scheduling is a challenging problem known as NP-complete, which means that there is no exact solution, and we can only achieve near-optimal results, particularly when using large-scale tasks in the context of cloud computing. This paper proposes an optimised strategy, Cuckoo-based Discrete Symbiotic Organisms Search (C-DSOS) that incorporated with Levy-Flight for optimal task scheduling in the cloud computing environment to minimise degree of imbalance. The strategy is based on the Standard Symbiotic Organism Search (SOS), which is a nature-inspired metaheuristic optimisation algorithm designed for numerical optimisation problems. SOS simulates the symbiotic relationships observed in ecosystems, such as mutualism, commensalism, and parasitism. To evaluate the proposed technique, the CloudSim toolkit simulator was used to conduct experiments. The results demonstrated that C-DSOS outperforms the Simulated Annealing Symbiotic Organism Search (SASOS) algorithm, which is a benchmarked algorithm commonly used in task scheduling problems. C-DSOS exhibits a favourable convergence rate, especially when using larger search spaces, making it suitable for task scheduling problems in the cloud. For the analysis, a t-test was employed, reveals that C-DSOS is statistically significant compared to the benchmarked SASOS algorithm, particularly for scenarios involving a large search space.Comment: 21 pages, 5 figures, 2 algorithms, 6 table

Mercedes-Benz USA Labor Planning Dashboard

Mercedes-Benz USA specializes in producing high-quality vehicles that exceed customer expectations at a cost-effective rate. The company utilizes a labor planning dashboard that predicts the daily use of their lines at their part distribution centers by allocating their employees to different zones in inbound, outbound, or both. The supervisors manually input all the data to designate employees to various sections within those zones. Our team was tasked with improving and proposing an updated version of the labor planning dashboard by meeting their requirements while making it effective, responsive, and user-friendly. Through trial and error, the new labor planning dashboard combats these issues by eliminating an excessive amount of manual input and creates an automated dashboard by implementing a linear program solver known as an Assignment Problem