Abstract
Fog-node computing, in culmination with computing using cloud-network environment, has emerged as a promising improvement for Internet of Things (IoT) architectures. Computing using fog-nodes is a decentralized concept which includes the distribution of various resources like the processing power, storage capacity, and applications between the central cloud structure and the source of data. Fog nodes provide the benefits and power of the cloud nodes, closer to the point of data creation and consumption. Hence this architecture provides a flexible and efficient system for handling IoT tasks as per their resource requirements. We propose a hybrid optimization technique which combines the Optimization Algorithm based on Artificial Ecosystem (AEO) as well as the widely popular Algorithm based on Salp-Swarm together to improve the exploitation abilities of AEO. A system model is also described which shows various layers in the cloud-fog environment for IoT systems. These layers interact together to create an efficient architecture where optimization techniques can be applied to develop optimal task scheduling.
The developed hybrid algorithm is simulated using a synthetic dataset consisting of various non-preemptive tasks with each task length between 1–200000 MI. Its performance is analyzed by the use of metrics like makespan time and Performance Improvement Ratio, which shows an improvement of a maximum of 2.2% when compared with existing AEO algorithm. Finally, the scope for future development of this algorithm and this field is also discussed. The algorithm was implemented and analyzed using Java SDK-1.7 and MATLAB R2021b software.
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Patle, A., Kanaparthi, S.D., Naik, K.J. (2023). Enhanced Hybrid Optimization Technique to Find Optimal Solutions for Task Scheduling in Cloud-Fog Computing Environments. In: Venkataraman, R., Uthra, A., Sugumaran, V., Minu, R.I., Chelliah, P.R. (eds) Internet of Things. ICIoT 2022. Communications in Computer and Information Science, vol 1727. Springer, Cham. https://doi.org/10.1007/978-3-031-28475-5_10
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