Task scheduling in cloud-fog computing using discrete binary particle swarm meta-heuristic with modified sigmoid function
*Y. Nasir AhmedCorresponding authornasir_ca_phd_18@crescent.educationDepartment of Computer ApplicationsB. S. Abdur Rahman Crescent Institute of Science and TechnologyVandalur, Chennai, Tamil Nadu, 600048, IndiaView full profile → , S. Pakkir Mohideenpakirmoitheen@crescent.educationDepartment of Computer ApplicationsB. S. Abdur Rahman Crescent Institute of Science and TechnologyVandalur, Chennai, Tamil Nadu, 600048, IndiaView full profile → , Mohammad Pashamdpasha@mjcollege.ac.inDepartment of Information TechnologyMuffakham Jah College of Engineering and TechnologyHyderabad, Telangana (TS), 500034, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Published Online:
- 11 Nov 2023
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-1226
- Pages:
- 1023–1033
Abstract
Keywords
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References
[1] Aburukba, Raafat O., TahaLandolsi, and Dalia Omer, A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices, Journal of Network and Computer Application, 180 (2021): 102994.
[2] Kratzke, Nane. “A brief history of cloud application architectures.” Applied Sciences 8.8 (2018): 1368.
[3] Mastoi, Qurat-ul-ain, et al, A novel cost-efficient framework for critical heartbeat task scheduling using the Internet of medical things in a fog cloud system, Sensors 20.2 (2020): 441.
[4] Poli, Riccardo, James Kennedy, and Tim Blackwell. “Particle swarm optimization.” Swarm intelligence 1.1 (2007): 33-57.
[5] Farid, Mazen, et al, A survey on QoS requirements based on particle swarm optimization scheduling techniques for workflow scheduling in cloud computing, Symmetry 12.4 (2020): 551.
[6] Ali, Ismail M., et al, An automated task scheduling model using non-dominated sorting genetic algorithm II for fog-cloud systems, IEEE Transactions on Cloud Computing (2020).
[7] Mokni, Marwa, et al,Cooperative agents-based approach for workflow scheduling on fog-cloud computing, Journal of Ambient Intelligence and Humanized Computing (2021): 1-20.
[8] Naha, Ranesh Kumar, et al, Deadline-based dynamic resource allocation and provisioning algorithms in fog-cloud environment, Future Generation Computer Systems 104 (2020): 131-141.
[9] Abbasi, Mahdi, et al, Efficient resource management and workload allocation in fog–cloud computing paradigm in IoT using learning classifier systems, Computer communications 153 (2020): 217-228.
[10] Hussein, Mohamed K., and Mohamed H. Mousa, Efficient task offloading for IoT-based applications in fog computing using ant colony optimization, IEEE Access 8 (2020): 37191-37201.
[11] Kaur, Mandeep, and Rajni Aron, Energy-aware load balancing in fog cloud computing, Materials Today: Proceedings, (2020).
[12] Abdel-Basset, Mohamed, et al, Energy-aware metaheuristic algorithm for industrial-Internet-of-Things task scheduling problems in fog computing applications,IEEE Internet of Things Journal, 8.16 (2020): 12638-12649.
[13] Natesha, B. V., and Ram Mohana Reddy Guddeti, Heuristic-based IoT application modules placement in the fog-cloud computing environment, 2018 IEEE/ACM international conference on utility and cloud computing companion (UCC Companion), IEEE, 2018.
[14] Sang, Yongxuan, et al, A three-stage heuristic task scheduling for optimizing the service level agreement satisfaction in device-edge-cloud cooperative computing, PeerJ Computer Science, 8 (2022): e851.
[15] Abdelmoneem, Randa M., Abderrahim Benslimane, and EmanShaaban, Mobility-aware task scheduling in cloud-Fog IoT-based healthcare architectures, Computer Networks, 179 (2020): 107348.
[16] Mahmud, Redowan, et al, Profit-aware application placement for integrated fog–cloud computing environments, Journal of Parallel and Distributed Computing, 135 (2020): 177-190.
[17] Lakhan, Abdullah, et al, Smart-contract aware ethereum and client-fog-cloud healthcare system, Sensors, 21.12 (2021): 4093.
