Load Balancing in Edge-Cloud Ecosystems: A Survey of Hybrid Orchestration Models
Main Article Content
Abstract
Edge-cloud computing has emerged as a promising paradigm for supporting the growing demands of Internet of Things
(IoT) applications, intelligent services, and data-intensive workloads. By integrating distributed edge resources with centralized cloud
infrastructures, edge-cloud ecosystems enable low-latency processing, improved scalability, and efficient resource utilization.
However, the dynamic and heterogeneous nature of these environments introduces significant challenges in workload distribution,
resource allocation, and service orchestration. Consequently, load balancing and orchestration mechanisms have become essential
for maintaining Quality of Service (QoS) and ensuring efficient system operation. This survey presents a comprehensive review of
load-balancing techniques and hybrid orchestration models in edge-cloud ecosystems. A taxonomy of load-balancing approaches is
discussed based on decision location, orchestration strategy, and system architecture. The study further examines recent advances in
distributed computing frameworks, Software-Defined Networking (SDN), Network Function Virtualization (NFV), workload
scheduling, collaborative resource management, and intelligent orchestration techniques. A comparative analysis of existing research
is provided to identify key strengths, limitations, and emerging trends. The findings indicate that hybrid orchestration models, which
combine centralized coordination with decentralized decision-making, offer significant advantages in terms of scalability,
adaptability, resource utilization, and service performance. Additionally, critical challenges and future research directions are
highlighted to support the development of efficient, secure, and intelligent edge-cloud ecosystems.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) License. Authors retain the copyright of their work and grant the Journal of Global Research in Multidisciplinary Studies (JGRMS) the right of first publication. This license permits unrestricted use, distribution, adaptation, and reproduction in any medium or format, provided the original author(s), source, and publication are properly credited. Users may copy, redistribute, remix, transform, and build upon the published material for any purpose, including commercial use, in accordance with the terms of the CC BY 4.0 License.
How to Cite
References
REFERENCES
[1] S. S. Nasrin, “Nebula Core : A Scalable Multimodal Framework for Distributed Intelligence in Edge-Cloud Systems Methodology :,” vol. 1, pp. 2–5, 2025, doi: 10.13140/RG.2.2.13170.36807.
[2] X. Xu, S. Zang, M. Bilal, X. Xu, and W. Dou, “Intelligent architecture and platforms for private edge cloud systems: A review,” Futur. Gener. Comput. Syst., vol. 160, no. June, pp. 457–471, 2024, doi: 10.1016/j.future.2024.06.024.
[3] A. Joon, B. K. R. Janumpally, A. Gogineni, and P. Chatterjee, “Efficient Large-Scale Intrusion Identification and Prevention in Distributed Cloud Networks Using Artificial Intelligence,” in 2025 5th International Conference on Intelligent Technologies (CONIT), HUBBALI, India: IEEE, 2025, pp. 1–8, September. doi: 10.1109/CONIT65521.2025.11167760.
[4] S. Singh, S. A. Pahune, P. Chatterjee, and R. Sura, “Advanced Machine Learning Techniques for Prediction of Customer Churn in Telecommunication Sector,” in 2025 IEEE 6th India Council International Subsections Conference (INDISCON), Rourkela, India: IEEE, 2025, pp. 1–6, August. doi: 10.1109/INDISCON66021.2025.11252233.
[5] F. C. Andriulo, M. Fiore, M. Mongiello, E. Traversa, and V. Zizzo, “Edge Computing and Cloud Computing for Internet of Things: A Review,” Informatics, vol. 11, no. 4, Sep. 2024, doi: 10.3390/informatics11040071.
[6] K. Jangiti, “Design and Validation of a Machine Identity Governance Framework for AI Agents in Multi-Cloud Environments,” in SoutheastCon 2026, IEEE, Feb. 2026, pp. 1–6. doi: 10.1109/SoutheastCon63549.2026.11476363.
[7] R. K. Gadiraju, “Artificial Intelligence for Resource Optimization in Cloud Computing Environments,” J. Electr. Syst., vol. 20, no. 6, pp. 3164–3174, March, 2024.
[8] T. P. Patel, “Adaptive Token Routing for Heterogeneous LLM Inference in Edge-Cloud Continuum,” in SoutheastCon 2026, Huntsville, AL, USA: IEEE, 2026, pp. 1–7, April. doi: 10.1109/SoutheastCon63549.2026.11476596.
[9] M. Trigka and E. Dritsas, “Edge and Cloud Computing in Smart Cities,” Futur. Internet, vol. 17, no. 3, 2025, doi: 10.3390/fi17030118.
[10] I. Korontanis, A. Makris, and K. Tserpes, “A Survey on Modeling Languages for Applications Hosted on Cloud-Edge Computing Environments,” Appl. Sci., vol. 14, no. 6, 2024, doi: 10.3390/app14062311.
[11] R. Vaño, I. Lacalle, P. Sowiński, R. S-Julián, and C. E. Palau, “Cloud-Native Workload Orchestration at the Edge: A Deployment Review and Future Directions,” Sensors. 2023. doi: 10.3390/s23042215.
[12] V. K. Sharma and K. S. Abhilash, “Latency-Aware Edge-Cloud Architecture for 5G IoT Integration,” in 2025 6th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, Sep. 2025, pp. 1398–1405. doi: 10.1109/ICESC65114.2025.11212232.
[13] A. Ullah et al., “Orchestration in the Cloud-to-Things compute continuum: taxonomy, survey and future directions,” Journal of Cloud Computing. 2023. doi: 10.1186/s13677-023-00516-5.
[14] Y. Chiang et al., “Management and Orchestration of Edge Computing for IoT: A Comprehensive Survey,” IEEE Internet Things J., 2023, doi: 10.1109/JIOT.2023.3245611.
[15] F. Al-Doghman, N. Moustafa, I. Khalil, N. Sohrabi, Z. Tari, and A. Y. Zomaya, “AI-Enabled Secure Microservices in Edge Computing: Opportunities and Challenges,” IEEE Trans. Serv. Comput., 2023, doi: 10.1109/TSC.2022.3155447.
[16] B. Kar, W. Yahya, Y. D. Lin, and A. Ali, “Offloading Using Traditional Optimization and Machine Learning in Federated Cloud-Edge-Fog Systems: A Survey,” IEEE Commun. Surv. Tutorials, 2023, doi: 10.1109/COMST.2023.3239579.
[17] M. Gaglianese, J. Soldani, S. Forti, and A. Brogi, “Green Orchestration of Cloud-Edge Applications: State of the Art and Open Challenges,” in Proceedings - 17th IEEE International Conference on Service-Oriented System Engineering, SOSE 2023, 2023. doi: 10.1109/SOSE58276.2023.00036.
[18] Z. A. Khan, I. A. Aziz, N. A. B. Osman, and I. Ullah, “A Review on Task Scheduling Techniques in Cloud and Fog Computing: Taxonomy, Tools, Open Issues, Challenges, and Future Directions,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3343877.
[19] H. N. Dholariya, “Regulatory-Grade Autonomous Data Modernization: The RAMA Framework for Compliance-Aware AI-Native Cloud Architectures,” J. Comput. Anal. Appl., vol. 35, no. 1, pp. 883–898, Jan, Jan. 2026, doi: 10.48047/jocaaa.2026.35.01.40.
[20] A. Furutanpey et al., “Architectural Vision for Quantum Computing in the Edge-Cloud Continuum,” in Proceedings - 2023 IEEE International Conference on Quantum Software, QSW 2023, 2023. doi: 10.1109/QSW59989.2023.00021.
[21] H. J. Lee, S. Noghabi, B. Noble, M. Furlong, and L. P. Cox, “BumbleBee: Application-aware adaptation for edge-cloud orchestration,” in Proceedings - 2022 IEEE/ACM 7th Symposium on Edge Computing, SEC 2022, 2022. doi: 10.1109/SEC54971.2022.00017.
[22] J. Dogani, R. Namvar, and F. Khunjush, “Auto-scaling techniques in container-based cloud and edge/fog computing: Taxonomy and survey,” Computer Communications. 2023. doi: 10.1016/j.comcom.2023.06.010.
[23] J. Zhang, F. Keramat, X. Yu, D. M. Hernandez, J. P. Queralta, and T. Westerlund, “Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness,” in 2022 7th International Conference on Fog and Mobile Edge Computing, FMEC 2022, 2022. doi: 10.1109/FMEC57183.2022.10062523.
[24] G. K. Walia, M. Kumar, and S. S. Gill, “AI-Empowered Fog/Edge Resource Management for IoT Applications: A Comprehensive Review, Research Challenges, and Future Perspectives,” IEEE Commun. Surv. Tutorials, 2024, doi: 10.1109/COMST.2023.3338015.
[25] J. B. Mehta, “Autonomous Workload Right-Sizing for Multi-Cloud Cost Optimization,” in 2026 International Conference on Artificial Intelligence, Systems, and Emerging Technologies (ICAISET), Cairo, Egypt: IEEE, 2026, pp. 1–11, June. doi: 10.1109/ICAISET66439.2026.11541899.
[26] N. R. Barot, “Transparency-Driven Operational Intelligence: A New Data Governance Model for High-Risk Industrial Automation,” J. Inf. Syst. Eng. Manag., vol. 10, no. 63s, pp. 1019–1028, Dec. 2025, doi: 10.52783/jisem.v10i63s.13975.
[27] N. D. Bhandarwar, “A Mathematical Framework For Explainable And Adversarially Robust Ids Using Ml For Large-Scale Enterprise And Cloud Systems,” Int. J. Appl. Math., vol. 39, no. 1s, pp. 1200–1212, Jan. 2026, doi: 10.12732/ijam.v39i1s.1910.
[28] R. rao Thallada and N. Alapati, “Privacy and Cybersecurity Convergence: GRC Controls for Data Protection,” J. Bus. Manag. Stud., vol. 8, no. 5, pp. 42–48, March, 2026, doi: 10.32996/jbms.
[29] H. F. Shahid, B. Akdemir, J. Islam, I. Ahmad, and E. Harjula, “IoT Service Orchestration in Edge–Cloud Continuum With 6G: A Review,” in IEEE Internet of Things Journal, IEEE, 2026, pp. 20339–20358, Februrary. doi: 10.1109/JIOT.2026.3663918.
[30] B. U. Kazi, M. K. Islam, M. M. H. Siddiqui, and M. Jaseemuddin, “A Survey on Software Defined Network-Enabled Edge Cloud Networks: Challenges and Future Research Directions,” Network, vol. 5, no. 2, p. 16, May 2025, doi: 10.3390/network5020016.
[31] M. S. Aslanpour, A. N. Toosi, M. A. Cheema, M. B. Chhetri, and M. A. Salehi, “Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach,” Futur. Gener. Comput. Syst., 2024, doi: 10.1016/j.future.2024.01.020.
[32] M. N. Jamil, M. S. Hossain, R. U. Islam, and K. Andersson, “Workload Orchestration in Multi-Access Edge Computing Using Belief Rule-Based Approach,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3326244.
[33] M. Raeisi-Varzaneh, O. Dakkak, A. Habbal, and B. S. Kim, “Resource Scheduling in Edge Computing: Architecture, Taxonomy, Open Issues and Future Research Directions,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3256522.
[34] A. Paszkiewicz et al., “Network Load Balancing for Edge-Cloud Continuum Ecosystems,” in Lecture Notes in Electrical Engineering, 2022. doi: 10.1007/978-981-19-1677-9_56.