Affiliation:
1. School of Computer Science and Engineering, VIT-AP University, Amaravathi 522237, Andhra Pradesh, India
Abstract
Cloud-fog computing is a wide range of service environments created to provide quick, flexible services to customers, and the phenomenal growth of the Internet of Things (IoT) has produced an immense amount of data on a daily basis. To complete tasks and meet service-level agreement (SLA) commitments, the provider assigns appropriate resources and employs scheduling techniques to efficiently manage the execution of received IoT tasks in fog or cloud systems. The effectiveness of cloud services is directly impacted by some other important criteria, such as energy usage and cost, which are not taken into account by many of the existing methodologies. To resolve the aforementioned problems, an effective scheduling algorithm is required to schedule the heterogeneous workload and enhance the quality of service (QoS). Therefore, a nature-inspired multi-objective task scheduling algorithm called the electric earthworm optimization algorithm (EEOA) is proposed in this paper for IoT requests in a cloud-fog framework. This method was created using the combination of the earthworm optimization algorithm (EOA) and the electric fish optimization algorithm (EFO) to improve EFO’s potential to be exploited while looking for the best solution to the problem at hand. Concerning execution time, cost, makespan, and energy consumption, the suggested scheduling technique’s performance was assessed using significant instances of real-world workloads such as CEA-CURIE and HPC2N. Based on simulation results, our proposed approach improves efficiency by 89%, energy consumption by 94%, and total cost by 87% over existing algorithms for the scenarios considered using different benchmarks. Detailed simulations demonstrate that the suggested approach provides a superior scheduling scheme with better results than the existing scheduling techniques.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference39 articles.
1. Enhanced multi-verse optimizer for task scheduling in cloud computing environments;Shukri;Expert Syst. Appl.,2021
2. Multi-objective task scheduling in a cloud computing environment by hybridized bat algorithm;Bezdan;J. Intell. Fuzzy Syst.,2022
3. Elite learning Harris hawks optimizer for multi-objective task scheduling in cloud computing;Amer;J. Supercomput.,2022
4. Fortino, G., Guerrieri, A., Pace, P., Savaglio, C., and Spezzano, G. (2022). Iot platforms and security: An analysis of the leading industrial/commercial solutions. Sensors, 22.
5. Data mining for the internet of things: Literature review and challenges;Chen;Int. J. Distrib. Sens. Netw.,2015
Cited by
14 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献