A Comprehensive Framework for Many-Objective Resource Scheduling and Carbon-Aware Orchestration in Distributed Fog-Cloud Ecosystems
Keywords:
Fog Computing, Many-Objective Optimization, Cloud Task Scheduling, Carbon-Aware ComputingAbstract
The rapid proliferation of Internet of Things (IoT) devices and the subsequent data deluge have necessitated a paradigm shift from centralized cloud computing to distributed architectures encompassing fog and edge computing. As these environments evolve, the complexity of task scheduling has transitioned from simple multi-objective optimization to many-objective optimization, where conflicting goals-such as energy efficiency, reliability, deadline constraints, cost, and carbon footprint-must be balanced simultaneously. This research article provides an extensive investigation into the theoretical underpinnings and practical applications of many-objective evolutionary algorithms and swarm intelligence within the context of fog-integrated cloud environments. By synthesizing contemporary research on diversity assessment, grid-based evolutionary strategies, and carbon-aware Kubernetes-native scheduling, this study proposes a holistic framework for managing scientific workflows and big data pipelines. The analysis explores the nuances of balanceable fitness estimation, clustering-based selection mechanisms, and the integration of data analytics for congestion management. Special emphasis is placed on the emerging necessity of carbon-aware scheduling to mitigate the environmental impact of large-scale distributed systems. The findings suggest that while traditional meta-heuristics are effective for low-dimensional problems, the scalability of many-objective approaches, such as those implemented in the PlatEMO platform, is essential for the next generation of smart grid and industrial IoT applications.
References
1. Aazam, M., et al. (2018). Offloading in fog computing for IoT: Review, enabling technologies, and research opportunities. Future Generation Computer Systems.
2. Alkhanak, E. N., et al. (2018). A hyper-heuristic cost optimisation approach for scientific workflow scheduling in cloud computing. Future Generation Computer Systems.
3. Almi’ani, K., et al. (2018). On efficient resource use for scientific workflows in clouds. Computer Networks.
4. S. Bhat, S. R. Sirikonda, V. Katoch and R. Jain, "Carbon-Kube: A Kubernetes-Native Framework for Multi-Objective Carbon-Aware Scheduling of Big Data Pipelines," 2026 9th International Conference on Electronics, Materials Engineering & Nano-Technology (IEMENTech), Kolkata, India, 2026, pp. 1-6, doi: 10.1109/IEMENTech202669403.2026.11434192.
5. Bittencourt, L. F., et al. (2017). Mobility-aware application scheduling in fog computing. IEEE Cloud Computing.
6. Cui, Z., Zhao, T., Wu, L., Qin, A. K., & Li, J. (2023). Multi-objective cloud task scheduling optimization based on evolutionary multi-factor algorithm. IEEE Transactions on Cloud Computing.
7. Dehnavi, S., et al. (2019). A reliability-aware resource provisioning scheme for real-time industrial applications in a fog-integrated smart factory. Microprocessors and Microsystems.
8. Grewal, S. K., & Mangla, N. (2023). Deadline and Cost Optimization based Task Scheduling (DCOTS) in Cloud Computing Environment. 4th International Conference on Intelligent Engineering and Management (ICIEM).
9. Hassan, H. A., et al. (2020). A smart energy and reliability aware scheduling algorithm for workflow execution in DVFS-enabled cloud environment. Future Generation Computer Systems.
10. Leiva, J., Pardo, R. C., & Aguado, J. (2019). Data analytics-based multi-objective particle swarm optimization for determination of congestion thresholds in lv networks. Energies.
11. Lin, Q., et al. (2018). Particle swarm optimization with a balanceable fitness estimation for many-objective optimization problems. IEEE Transactions on Evolutionary Computation.
12. Lin, Q., et al. (2019). A clustering-based evolutionary algorithm for many-objective optimization problems. IEEE Transactions on Evolutionary Computation.
13. Mahmoud, M. M., et al. (2018). Towards energy-aware fog-enabled cloud of things for healthcare. Computers and Electrical Engineering.
14. Ruan, X., et al. (2019). Virtual machine allocation and migration based on performance-to-power ratio in energy-efficient clouds. Future Generation Computer Systems.
15. Sadhukhan, A., & Sivasubramani, S. (2018). Multi-objective load scheduling in a smart grid environment. 20th National Power Systems Conference (NPSC).
16. Saeedi, S., et al. (2020). Improved many-objective particle swarm optimization algorithm for scientific workflow scheduling in cloud computing. Computers & Industrial Engineering.
17. Singh, J., & Tiwari, R. (2018). Multi-Objective Optimal Scheduling of Electric Vehicles in Distribution System. 20th National Power Systems Conference (NPSC).
18. Tian, Y., Cheng, R., Zhang, X., & Jin, Y. (2017). PlatEMO: A MATLAB platform for evolutionary multi-objective optimization. IEEE Computational Intelligence Magazine.
19. Tortonesi, M., et al. (2019). Taming the IoT data deluge: An innovative information-centric service model for fog computing applications. Future Generation Computer Systems.
20. Tsai, C.-W. (2018). SEIRA: AN effective algorithm for IoT resource allocation problem. Computer Communications.
21. Wang, H., Jin, Y., & Yao, X. (2017). Diversity assessment in many-objective optimization. IEEE Transactions on Cybernetics.
22. Yang, S., et al. (2013). A grid-based evolutionary algorithm for many-objective optimization. IEEE Transactions on Evolutionary Computation.
23. Yassine, A., et al. (2019). IoT Big data analytics for smart homes with fog and cloud computing. Future Generation Computer Systems.
24. Yuping, L. (2019). Optimization of multi-objective virtual machine based on ant colony intelligent algorithm. International Journal of Performability Engineering.
25. Zhou, X., et al. (2019). Minimizing cost and makespan for workflow scheduling in cloud using fuzzy dominance sort based HEFT. Future Generation Computer Systems.
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Copyright (c) 2026 James Trachen (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
