Load-Tolerant Many-Objective Optimization for Resource Allocation in SDN-based IoT

Document Type : Original Article

Authors

1 Department of Computer, University of Kashan, Kashan

2 Department of Computer Engineering, Faculty of Electrical and Computer Engineering, University of Kashan

3 Faculty of Computer Engineering, University of Isfahan, Isfahan, Iran

Abstract

The increasing workload in Internet of Things (IoT) environments leads to network congestion and resource imbalance, which significantly degrades Quality of Service (QoS). Software-Defined Networking (SDN) provides a flexible control paradigm for improving QoS and load balancing; however, most existing approaches are limited to multi-objective formulations and do not explicitly address overload conditions. Therefore, there is a need for many-objective QoS optimization frameworks that can jointly consider multiple conflicting objectives while maintaining load tolerance in SDN-based IoT systems.
In this paper, a load-tolerant many-objective resource allocation framework is proposed to simultaneously optimize user and service provider QoS requirements. Specifically, cost and response time are minimized for users, while energy consumption is minimized and resource utilization is maximized for service providers. A Pareto-based many-objective evolutionary optimization process is employed to generate diverse non-dominated solutions, which are evaluated under overload conditions.
To address infeasible allocations under overload, a load-tolerance mechanism is introduced to identify admissible solutions and improve system robustness. This mechanism enables sustained task allocation even when Pareto-optimal solutions violate load threshold, thereby increasing the task acceptance rate under overload conditions.
Simulation results demonstrate that the proposed Many-objective Grey Wolf Optimization (MaGWO)-based framework improves resource allocation cost by 13.85%, response time by 17.2%, energy consumption by 15.8%, and resource utilization by 10.25%. Furthermore, the proposed load-tolerant strategy increases the task acceptance rate by 8.4% compared to NSGA-III and Many-objective Particle Swarm Optimization (MaPSO).

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References

Alsadie, D. (2021). TSMGWO: Optimizing task schedule using multi-objectives grey Wolf Optimizer for cloud data centers. IEEE Access, 9, 37707-37725.
Amazon (2024). https://aws.amazon.com/ec2/instance-types/
Arya, A., Gunarani, G. I., Rathinakumar, V., Sharma, A., Pati, A. K., & Sethi, K. C. (2024). NSGA-III based optimization model for balancing time, cost, and quality in resource-constrained retrofitting projects. Asian Journal of Civil Engineering, 25(7), 5613-5625.‏ ‏
Badr, S. A., Gamal, M., Ali, K. A. E., & Abdel-Kader, R. F. (2024). Multi-objective improved particle swarm optimization for efficient offloading algorithm in fog-cloud collaboration. Suez Canal Engineering, Energy and Environmental Science, 2(2), 17-26.
Balicki, J. (2022). Many-Objective quantum-inspired particle swarm optimization algorithm for placement of virtual machines in smart computing cloud. Entropy, 24(1), 58.
Belgacem, A. (2022). Dynamic resource allocation in cloud computing: analysis and taxonomies. Computing, 104(3), 681-710.‏
Cao, B., Fu, Y., Sun, Z., Liu, X., He, H., & Lv, Z. (2021, October). A resource allocation strategy in fog-cloud computing towards the Internet of Things in the 5G era. In 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) (pp. 1-6). IEEE.‏
Cao, B., Sun, Z., Zhang, J., & Gu, Y. (2021). Resource allocation in 5G IoV architecture based on SDN and fog-cloud computing. IEEE Transactions on Intelligent Transportation Systems, 22(6), 3832- 3840.
Figueiredo, E. M., Ludermir, T. B., & Bastos-Filho, C. J. (2016). Objective particle swarm optimization. Information Sciences, 374, 115-134.‏
Gohil, B. N., & Patel, D. R. (2022). Load balancing in cloud using improved gray wolf optimizer. Concurrency and Computation: Practice and Experience, 34(11), e6888.‏
Hashemi, M., Javaheri, D., Sabbagh, P., Arandian, B., & Abnoosian, K. (2021). A multi‐objective method for virtual machines allocation in cloud data centres using an improved grey wolf optimization algorithm. IET Communications, 15(18), 2342-2353.
Hosseinzadeh, M., Haider, A., Rahmani, A. M., Gharehchopogh, F. S., Rajabi, S., Khoshvaght, P., ... Lee, S. W. (2025). SDN-Based NFV deployment for multi-objective resource allocation in edge computing: A deep reinforcement learning for iot workload scheduling. Sustainable Computing: Informatics and Systems, 48, 101218.‏
Hussaini, S. M., Razak, T. A., & Jamil, M. A. (2024). Multi-Objective evolutionary algorithm to optimize IoT based scheduling problem using (NSGA-II algorithm). Journal of Intelligent Systems & Internet of Things, 12(2).‏
Imene, L., Sihem, S., Okba, K., & Mohamed, B. (2022). A third generation genetic algorithm NSGAIII for task scheduling in cloud computing. Journal of King Saud University-Computer and Information Sciences, 34(9), 7515-7529.‏
Keshari, S. K., Kansal, V., & Kumar, S. (2021). A cluster based intelligent method to manage load of controllers in SDN-IoT networks for smart cities. Scalable Computing: Practice and Experience, 22(4), 247-257.
Khan, M. A., & ur Rasool, R. (2024). A multi-objective grey-wolf optimization based approach for scheduling on cloud platforms. Journal of Parallel and Distributed Computing, 187, 104847.‏
Makhadmeh, S. N., Al-Betar, M. A., Doush, I. A., Awadallah, M. A., Kassaymeh, S., Mirjalili, S., & Zitar, R. A. (2023). Recent advances in Grey Wolf Optimizer, its versions and applications. IEEE Access, 12, 22991-23028.‏
Mirjalili, S., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106-119.‏
Mohammadi, R., Akleylek, S., & Ghaffari, A. (2023). SDN-IoT: SDN-based efficient clustering scheme for IoT using improved Sailfish optimization algorithm. PeerJ Computer Science, 9, e1424.
Nain, A., Sheikh, S., & Shahid, M. (2025). An efficient load distribution approach for optimizing resources in SDN‐Based edge computing environment. Concurrency and Computation: Practice and Experience, 37(12-14), e70113.‏
Ramesh, D., Kolla, S. S., Naik, D., & Narvaneni, R. (2025). HGWO-MultiQoS: A hybrid grey wolf optimization approach for QoS-constrained workflow scheduling in IaaS clouds. Simulation Modelling Practice and Theory, 142, 103127.‏
Rostami, M., & Goli-Bidgoli, S. (2024). An overview of QoS-aware load balancing techniques in SDN-based IoT networks. Journal of Cloud Computing, 13(1), 89.‏
Saif, F. A., Latip, R., Hanapi, Z. M., & Shafinah, K. (2023). Multi-objective grey wolf optimizer algorithm for task scheduling in cloud-fog computing. IEEE Access, 11, 20635-20646.‏
Sandanasamy, A., & Charles, P. J. (2025). Dynamic load balancing through TOPSIS based optimal server selection and resource allocation in SDN IoT network. OPSEARCH, 1-24.‏
Sefati, S., Mousavinasab, M., & Zareh Farkhady, R. (2022). Load balancing in cloud computing environment using the Grey wolf optimization algorithm based on the reliability: Performance evaluation. The Journal of Supercomputing, 78(1), 18-42.‏
Singh, G., & Chaturvedi, A. K. (2024). Hybrid modified particle swarm optimization with genetic algorithm (GA) based workflow scheduling in cloud-fog environment for multi-objective optimization. Cluster Computing, 27(2),1947-1964.‏
Singh, S. P., Kumar, G., Ahirwar, U., Selvarajan, S., & Khan, F. (2025). Multi-objective quantum hybrid evolutionary algorithms for enhancing quality-of-service in internet of things. Scientific Reports, 15(1), 1-27.
Tyagi, V., Singh, S., Wu, H., & Gill, S. S. (2024). Load balancing in sdn-enabled wsns toward 6g ioe: Partial cluster migration approach. IEEE Internet of Things Journal, 11(18), 29557-29568.‏
Journal of Data Analytics and Intelligent Decision-making
Volume 2, Issue 1 - Serial Number 4
January 2026
Pages 31-57

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