Research Area:  Fog Computing

Abstract:

With the rapid increase in the functionality of IoT applications, the services provided by edge/IoT devices have surged in the recent past. Fog computing is gaining momentum as a contemporary computational framework for IoT-enabled smart applications that offers a latency sensitivity advantage over cloud computing. Effective task scheduling reduces application computation and latency durations while improves QoS. Many researchers have proposed a variety of heuristic and metaheuristic approaches for effective scheduling; however, there is still scope for improvement. The present work proposes a hybrid of heuristic and metaheuristic techniques for the scheduling of tasks. fireworks algorithm (FWA) is a metaheuristic algorithm, and Heterogeneous Earliest Finish Time (HEFT) is a heuristic algorithm. The bi-objective optimization approach is presented to minimize the makespan and cost factors. To compare the performance of the algorithm, the experiments have been conducted on distinct and independent scientific workflows. The results of exhaustive simulations have established the significance of the presented BH-FWA algorithm over other comparative approaches. To validate the relevance of the technique for fog computing networks, the metrics utilized for comparisons are makespan, cost, and throughput.

Keywords:  

Author(s) Name:  Ashish Mohan Yadav, Kuldeep Narayan Tripathi & S. C. Sharma

Publisher name:  Springer

DOI:  10.1007/s11227-021-04018-6

Volume Information:  volume 78, pages 4236–4260 (2022)

Paper Link:   https://link.springer.com/article/10.1007/s11227-021-04018-6