Task Scheduling Algorithm in Fog Computing Layer for Optimizing Multiple Quality of Service Parameters Using Jellyfish Search Optimization

Subject Areas : Journal of Computer & Robotics

Zahra Jafari ¹ , Ahmad habibi Zadeh Novin ² , Azadeh Zamanifar ³

1 -
2 - دانشگاه علوم و تحقیقات
3 -

Received: 2024-10-18 Accepted : 2024-12-22

Keywords: Task scheduling, Fog computing layer, Optimization algorithms, Deep learning, Multi-objective, Internet of Things, Metaheuristic algorithms,

Abstract :

The fog computing layer demonstrates significant potential for processing data and executing tasks for various Internet of Things (IoT) applications that are sensitive to latency. However, the resource constraints in fog devices limit the deployment of multiple applications, primarily due to inefficient resource management and discovery mechanisms in heterogeneous IoT environments. An efficient resource allocation strategy is one of the most effective ways to enhance the quality of service (QoS) and improve system performance. However, identifying the optimal resource allocation strategy for IoT applications involving multiple QoS parameters presents a complex, NP-hard challenge. This study proposes a task scheduling algorithm called CLJSO (ConvLstm-Jellyfish Search Optimization) for the fog computing layer, designed to optimize three crucial parameters: task completion time, cost, and energy consumption. The task scheduling process begins with predicting the workload on machines based on resource characteristics and request volumes using a ConvLstm neural network. Subsequently, the initial population of machines is generated and input into the Jellyfish Search Optimization (JSO) algorithm to execute the scheduling. Experimental results indicate that the proposed CLJSO algorithm surpasses existing approaches regarding task scheduling efficiency within the fog layer, including CGO, AOS, CSA, JS, EHEO, FSPGSA, and HGSWC.

References:

[1] Chai, F., Zhang, Q., Yao, H., Xin, X., Gao, R., & Guizani, M. (2023). Joint Multi-task Offloading and Resource Allocation for Mobile Edge Computing Systems in Satellite IoT. IEEE Transactions on Vehicular Technology.
[2] Pamuklu, T., Syed, A., Kennedy, W. S., & Erol-Kantarci, M. (2023). Heterogeneous GNN-RL Based Task Offloading for UAV-aided Smart Agriculture. IEEE Networking Letters.
[3] Subhan, F., Mirza, A., Su’ud, M. B. M., Alam, M. M., Nisar, S., Habib, U., & Iqbal, M. Z. (2023). AI-enabled wearable medical internet of things in healthcare system: A survey. Applied Sciences, 13(3), 1394.
[4] Atiq, H. U., Ahmad, Z., Uz Zaman, S. K., Khan, M. A., Shaikh, A. A., & Al-Rasheed, A. (2023). Reliable resource allocation and management for IoT transportation using fog computing. Electronics, 12(6), 1452.
[5] Ai, Z., Zhang, W., Li, M., Li, P., & Shi, L. (2023). A smart collaborative framework for dynamic multi-task offloading in IIoT-MEC networks. Peer-to-Peer Networking and Applications, 16(2), 749-764.
[6] cisco, Cisco IoT Solutions, 2020, [Online;] https://www.cisco.com/c/en_in/ solutions/internet-of-things/overview.htm. (Accessed 19 July 2021).
[7] E. Lamarre, Internet of Things, 2020, [Online;] https://www.mckinsey.com/ featured-insights/internet-of-things/how-we-help-clients. (Accessed 19 July 2021).
[8] Leo John, F., Lakshmi, D., & Kuncharam, M. (2023). Introduction to the Internet of Things: Opportunities, Perspectives and Challenges. Smart Grids and Internet of Things: An Energy Perspective, 1-34.
[9] Hamdan, S., Almajali, S., Ayyash, M., Salameh, H. B., & Jararweh, Y. (2023). An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems. Simulation Modelling Practice and Theory, 122, 102685.
[10] F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in: Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, 2012, pp. 13–16.
[11] H. Ning, F. Farha, Z.N. Mohammad, M. Daneshmand, A survey and tutorial on ‘‘connection exploding meets efficient communication’’ in the Internet of Things, IEEE Internet Things J. 7 (11) (2020) 10733–10744.
[12] H. Li, X. Li, W. Wang, Joint optimization of computation cost and delay for task offloading in vehicular fog networks, Trans. Emerg. Telecommun. Technol. 31 (2) (2020) e3818.
[13] I.M. Abbadi, Toward trustworthy clouds’ internet scale critical infrastructure,in: International Conference on Information Security Practice and Experience, Springer, 2011, pp. 71–82.
[14] M. Al-Khafajiy, T. Baker, H. Al-Libawy, Z. Maamar, M. Aloqaily, Y. Jararweh, Improving fog computing performance via fog-2-fog collaboration, Future Gener. Comput. Syst. 100 (2019) 266–280.
[15] Apat, Hemant Kumar and Sahoo, Bibhudutta and Goswami. (2024). A hybrid meta-heuristic algorithm for multi-objective IoT service placement in fog computing environments. IEEE Access, Future Generation Computer Systems 154 (2024) 266–280.
[16] M. Abbasi, E.M. Pasand, M.R. Khosravi, Workload allocation in iot-fog-cloud architecture using a multi-objective genetic algorithm, J. Grid Comput. (2020) 1–14.
[17] S. Ghanavati, J. Abawajy, D. Izadi, An energy aware task scheduling model using ant-mating optimization in fog computing environment, IEEE Trans. Serv. Comput. 15 (4) (2020) 2007–2017.
[18] V. Gowri, B. Baranidharan, Multi objective hybrid load balancing based optimization algorithm for improving fog computing performance, 2023.
[19] D. Subramoney, C.N. Nyirenda, Multi-swarm PSO algorithm for static workflow scheduling in cloud-fog environments, IEEE Access 10 (2022) 117199–117214.
[20] Badri, S., Alghazzawi, D. M., Hasan, S. H., Alfayez, F., Hasan, S. H., Rahman, M., & Bhatia, S. (2023). An Efficient and Secure Model Using Adaptive Optimal Deep Learning for Task Scheduling in Cloud Computing. Electronics, 12(6), 1441.
[21] Tong, Z., Chen, H., Deng, X., Li, K., & Li, K. (2019). A Scheduling Scheme in the Cloud Computing Environment Using Deep Q-learning. Information Sciences. doi:10.1016/j.ins.2019.10.035.
[22] Tong, Z., Deng, X., Chen, H., Mei, J., & Liu, H. (2019). QL-HEFT: a novel machine learning scheduling scheme base on cloud computing environment. Neural Computing and Applications. doi:10.1007/s00521-019-04118-8.
[23] Aslanpour, Mohammad Sadegh and Toosi, Adel N and Cheema, Muhammad Aamir and Chhetri, Mohan Baruwal and Salehi, Mohsen Amini. (2024). Load balancing for heterogeneous serverless edge computing:A performance-driven and empirical approach. IEEE Access, Future Generation Computer Systems 154 (2024) 266–280.
[24] Zhang, Hui and Zhang, Weihua (2024) Application of GWO-attention-ConvLSTM Model in Customer Churn Prediction and Satisfaction Analysis in Customer Relationship Management. Heliyon, Elsevier
[25] Rao, Muchang and Qin, Hang (2024) Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling.Computers, Materials.
[26] [109] Hosseinioun, P., Kheirabadi, M., Kamel Tabbakh, S. R., & Ghaemi, R. (2022). aTask scheduling approaches in fog computing: A survey. Transactions on Emerging Telecommunications Technologies, 33(3), e3792.
[27]. Chou, J. S., & Truong, D. N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535.
[28] Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849-872.
[29] Abd Elaziz, M.; Attiya, I. An improved Henry gas solubility optimization algorithm for task scheduling in cloud computing. Artif. Intell. Rev. 2021, 54, 3599–3637. [CrossRef]
[30] Talatahari, S.; Azizi, M. Chaos Game Optimization: A novel metaheuristic algorithm. Artif. Intell. Rev. 2021, 54, 917–1004 [CrossRef].
[31] Azizi, M. Atomic orbital search: A novel metaheuristic algorithm. Appl. Math. Model. 2021, 93, 657–683. [CrossRef]
[32] Braik, M.S. Chameleon Swarm Algorithm: A bio-inspired optimizer for solving engineering design problems. Expert Syst. Appl. 2021, 174, 114685. [CrossRef].