Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments: A Review
Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments: A Review |
||
 |
 |
|
| © 2025 by IJETT Journal | ||
| Volume-73 Issue-11 |
||
| Year of Publication : 2025 | ||
| Author : Hasnae NOUHAS, Abdessamad BELANGOUR, Mahmoud NASSAR | ||
| DOI : 10.14445/22315381/IJETT-V73I11P104 | ||
How to Cite?
Hasnae NOUHAS, Abdessamad BELANGOUR, Mahmoud NASSAR,"Heuristic-Based Workload Scheduling Approaches in Edge-Cloud Environments: A Review", International Journal of Engineering Trends and Technology, vol. 73, no. 11, pp.39-50, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P104
Abstract
Edge-cloud computing architecture has become appropriate and promising to satisfy the performance requirements of resource-intensive and latency-critical applications. Effective scheduling of workload serves the purpose of exploiting the distributed, heterogeneous, and dynamic characteristics of these environments. Among the developed approaches, heuristic-based scheduling methods are important due to the low level of complexity and practical merit they present in real-time and resource-limited environments. Heuristic-based workload scheduling methods are the center of focus of this paper. Present methods are classified into simple heuristics, metaheuristics, and hybrid schemes, and surveyed on prominent examples of HEFT, ACO, PSO, Min-Min/Max-Min, and Greedy Resource-Aware algorithms. Each of these is put through the prism of the scheduling objectives, benefits, and their tradeoff on various executed metrics like latency, energy efficiency, and flexibility. Though these strategies are beneficial, the issues associated with their usability for dynamic applications and multi-objective scenarios are prominent. Important research gaps are listed along with proposed future works, including adaptations, energy-oriented, and lightweight scheduling models. Of even higher value, it is notable to recognize the growing interest in the application of AI-based schemes, which have the potential to enhance heuristic-based scheduling once integrated into hybrid systems. This survey aspires to present the convenient go-to thesis of the researcher tackling the challenge of creating a productive workload scheduling design in the edge-cloud infrastructures.
Keywords
Cloud computing, Edge computing, Workload scheduling, Heuristic Algorithms, Metaheuristic Algorithms.
References
[1] Kanagarla Krishna Prasanth Bra, “Edge Computing and Analytics for IoT Devices: Enhancing Real-Time Decision Making in Smart Environments,” International Journal for Multidisciplinary Research (IJFMR), vol. 6, no. 5, pp. 1-9, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[2] Nasir Abbas et al., “Mobile Edge Computing: A Survey,” IEEE Internet of Things Journal, vol. 5, no. 1, pp. 450-465, 2018.
[CrossRef] [Google Scholar] [Publisher link]
[3] Mahsa Paknejad et al., “A Reliable and Efficient 5G Vehicular MEC: Guaranteed Task Completion with Minimal Latency,” 2025 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, pp. 566-571, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[4] Hossein Ahmadvand, and Fouzhan Foroutan, “Latency and Privacy-Aware Resource Allocation in Vehicular Edge Computing,” arXiv Preprint, pp. 1-6, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[5] Alexandru Rancea, Ionut Anghel, and Tudor Cioara, “Edge Computing in Healthcare: Innovations, Opportunities, and Challenges,” Future Internet, vol. 16, no. 9, no. 1-28, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[6] Yazeed Yasin Ghadi et al., “Enhancing Patient Healthcare with Mobile Edge Computing and 5G: Challenges and Solutions for Secure Online Health Tools,” Journal of Cloud Computing, vol. 13, no. 1, pp. 1-13, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[7] Dinesh Sahu et al., “Optimizing Energy and Latency in Edge Computing Through a Boltzmann Driven Bayesian Framework for Adaptive Resource Scheduling,” Scientific Reports, vol. 15, no. 1, pp. 1-26, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[8] Nasim Soltani, Behzad Soleimani, and Behrang Barekatain, “Heuristic Algorithms for Task Scheduling in Cloud Computing: A Survey,” International Journal of Computer Network and Information Security (IJCNIS), vol. 9, no. 8, pp. 16-22, 2017.
[CrossRef] [Google Scholar] [Publisher link]
[9] Harshala Shingne, and R. Shriram, “Heuristic Deep Learning Scheduling in Cloud for Resource-Intensive Internet of Things Systems,” Computers and Electrical Engineering, vol. 108, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[10] Zhiyu Wang et al., “Deep Reinforcement Learning-based Scheduling for Optimizing System Load and Response Time in Edge and Fog Computing Environments,” Future Generation Computer Systems, vol. 152, pp. 55-69, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[11] Hassan Asghar, and Eun-Sung Jung, “A Survey on Scheduling Techniques in the Edge Cloud: Issues, Challenges and Future Directions,” arXiv Preprint, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[12] Quyuan Luo et al., “Resource Scheduling in Edge Computing: A Survey,” IEEE Communications Surveys and Tutorials, vol. 23, no. 4, pp. 2131-2165, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[13] Deafallah Alsadie, “Advancements in Heuristic Task Scheduling for IoT Applications in Fog-Cloud Computing: Challenges and Prospects,” PeerJ Computer Science, vol. 10, pp. 1-58, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[14] Olha Boiko et al., “Edge-Cloud Architectures for Hybrid Energy Management Systems: A Comprehensive Review,” IEEE Sensors Journal, vol. 24, no. 10, pp. 15748-15772, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[15] Ahmed A. Ismail et al., “A Survey on Resource Scheduling Approaches in Multi-Access Edge Computing Environment: A Deep Reinforcement Learning Study,” Cluster Computing, vol. 28, no. 3, pp. 1-45, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[16] Zaiwar Ali et al., “A Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing,” Computers, Materials and Continua, vol. 66, no. 2, pp. 1461-1477, 2021.
[CrossRef] [Google Scholar] [Publisher link]
[17] Amin Avan, Akramul Azim, and Qusay H. Mahmoud, “A State-of-the-Art Review of Task Scheduling for Edge Computing: A Delay-Sensitive Application Perspective,” Electronics, vol. 12, no. 12, pp. 1-27, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[18] Nisha Devi et al., “A Systematic Literature Review for Load Balancing and Task Scheduling Techniques in Cloud Computing,” Artificial Intelligence Review, vol. 57, no. 10, pp. 1-63, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[19] Saydul Akbar Murad et al., “SG-PBFS: Shortest Gap-Priority Based Fair Scheduling Technique for Job Scheduling in Cloud Environment,” Future Generation Computer Systems, vol. 150, pp. 232-242, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[20] Hemant Kumar Apat et al., “A Hybrid Meta-Heuristic Algorithm for Multi-Objective IoT Service Placement in Fog Computing Environments,” Decision Analytics Journal, vol. 10, pp. 1-17, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[21] Mustafa Ibrahim Khaleel et al., “Combinatorial Metaheuristic Methods to Optimize the Scheduling of Scientific Workflows in Green DVFS-Enabled Edge-Cloud Computing,” Alexandria Engineering Journal, vol. 86, pp. 458-470, 2024.
[CrossRef] [Google Scholar] [Publisher link]
[22] Nebojsa Bacanin et al., “Modified Firefly Algorithm for Workflow Scheduling in Cloud-Edge Environment,” Neural Computing and Applications, vol. 34, no. 11, pp. 9043-9068, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[23] Ashutosh Shankar, and Astha Kumari, “QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs,” arXiv Preprint, pp. 1-9, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[24] Dinesh Sahu et al., “Beyond Boundaries A Hybrid Cellular Potts and Particle Swarm Optimization Model for Energy and Latency Optimization in Edge Computing,” Scientific Reports, vol. 15, no. 1, pp. 1-22, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[25] Rafał Skinderowicz, “Improving Ant Colony Optimization Efficiency for Solving Large TSP Instances,” Applied Soft Computing, vol. 120, pp. 1-28, 2022.
[CrossRef] [Google Scholar] [Publisher link]
[26] Renjbar Sh. Othman, and Ibrahim Mahmood Ibrahim, “A Review of Exploring Recent Advances in Ant Colony Optimization: Applications and Improvements,” International Journal of Scientific World, vol. 11, no. 1, pp. 114-122, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[27] Shabariram C. Palaniappan, and Priya P. Ponnuswamy, “Task Offloading in Edge Computing Using Integrated Particle Swarm Optimization and Genetic Algorithm,” Advances in Science and Technology Research Journal, vol. 19, no. 1, pp. 371-380, 2025.
[CrossRef] [Google Scholar] [Publisher link]
[28] Farida Siddiqi Prity, Md. Hasan Gazi, and K.M. Aslam Uddin, “A Review of Task Scheduling in Cloud Computing Based on Nature-Inspired Optimization Algorithm,” Cluster Computing, vol. 26, no. 5, pp. 3037-3067, 2023.
[CrossRef] [Google Scholar] [Publisher link]
[29] Kaushik Sathupadi, “Comparative Analysis of Heuristic and Ai-Based Task Scheduling Algorithms in Fog Computing: Evaluating Latency, Energy Efficiency, and Scalability in Dynamic, Heterogeneous Environments,” Quarterly Journal of Emerging Technologies and Innovations, vol. 5, no. 1, pp. 23-40, 2020.
[Google Scholar] [Publisher link]
[30] Sheikh Umar Mushtaq, Sophiya Sheikh, and Sheikh Mohammad Idrees, “Enhanced Priority Based Task Scheduling with Integrated Fault Tolerance in Distributed Systems,” International Journal of Cognitive Computing in Engineering, vol. 6, pp. 152-169, 2025.
[CrossRef] [Google Scholar] [Publisher link]