Call For Paper - Upcoming Conferences
Research Article | Open Access | Download PDF
Volume 13 | Issue 4 | Year 2026 | Article Id. IJECE-V13I4P127 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I4P127

EdgeSchedAI: An Energy-Aware and Latency-Aware Task Scheduling Framework for IoT Applications in Edge-Cloud Environments

Nagendar Yamsani, Chenna Reddy P

Received Revised Accepted Published
23 Jan 2026 23 Feb 2026 24 Mar 2026 30 Apr 2026

Citation :

Nagendar Yamsani, Chenna Reddy P, "EdgeSchedAI: An Energy-Aware and Latency-Aware Task Scheduling Framework for IoT Applications in Edge-Cloud Environments," International Journal of Electronics and Communication Engineering, vol. 13, no. 4, pp. 317-347, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I4P127

Abstract

Given the explosive growth of IoT applications, there has been a growing need to schedule tasks effectively in edge–cloud computing settings, which are usually characterized by stringent latency requirements, constrained energy resources, a dynamic workload mix, and device mobility. Existing cloud-centric approaches suffer from high communication latency, while heuristic and meta-heuristic Schedulers are unable to adapt quickly to shifting system conditions. To tackle these issues, this paper offers an energy- and latency-aware DRL-based task system for scheduling IoT applications in Cloud-edge settings. Firstly, to express the task-scheduling issue as a sequential decision-making problem and then propose a DQN-based dynamic resource allocation model that adaptively schedules tasks across edge and cloud resources determined by changing system conditions throughout time, workload profiles, and SLA requirements. By balancing and adapting speed across a wide range of operational conditions, the proposed scheduler jointly optimizes energy consumption and task latency within a unified learning framework. The proposed approach outperformed two baselines—representative heuristic and learning-based—by 22–35% and 18–30%, respectively, in average task-latency and task energy consumption, with SLA satisfaction and task success rates above 95% under moderate workloads. Provided evidence of dependable learning behavior through conjunctions of ascent and/or descent and via convergence and stability analyses. Finally, scalability and stress testing observations confirm graceful performance degradation under overload conditions. The proposed framework provides a feasible and scalable solution that ensures sustainability, responsiveness, and service resilience for real-time IoT deployments. This enables deployment on any edge device, with adaptive load balancing and multi-objective optimization, allowing the application of this framework across numerous domains, such as real-time, industrial IoT, and smart cities monitoring systems, in dynamic edge–cloud environments.

Keywords

Deep Reinforcement Learning, Edge–Cloud Computing, Energy-Aware Scheduling, IoT Task Scheduling, Latency-Aware Optimization.

References

  1. Mohammad Sadegh Aslanpour et al., “Energy-Aware Resource Scheduling for Serverless Edge Computing,” 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Taormina, Italy, pp. 190-199, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  2. Bassem Sellami et al., “Energy-aware Task Scheduling and Offloading using Deep Reinforcement Learning in SDN-enabled IoT Network,” Computer Networks, vol. 210, pp. 1-20, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. Meng Xun et al., “Deep Reinforcement Learning for Delay and Energy-Aware Task Scheduling in Edge Clouds,” 18th CCF Conference, ChineseCSCW 2023 Computer Supported Cooperative Work and Social Computing, Harbin, China, pp. 436-450, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. QIN Zhiwei et al., “Energy-aware Workflow Real-time Scheduling Strategy for Device-edge-cloud Collaborative Computing,” Computer Integrated Manufacturing System, vol. 28, no. 10, pp. 3122-3130, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Abhijeet Mahapatra et al., “An Energy-Aware Task Offloading and Load Balancing for Latency-Sensitive IoT Applications in the Fog-Cloud Continuum,” IEEE Access, vol. 12, pp. 14334-14349, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Mekala Ratna Raju, and Sai Krishna Mothku, “Delay and Energy Aware Task Scheduling Mechanism for Fog-enabled IoT Applications: A Reinforcement Learning Approach,” Computer Networks, vol. 224, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. Mohammad Sadegh Aslanpour et al., “Faashouse: Sustainable Serverless Edge Computing Through Energy-Aware Resource Scheduling,” IEEE Transactions on Services Computing, vol. 17, no. 4, pp. 1533-1547, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Carmen Delgado, and Jeroen Famaey, “Optimal Energy-Aware Task Scheduling for Batteryless IoT Devices,” IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 3, pp. 1374-1387, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Amanda Jayanetti, Saman Halgamuge, and Rajkumar Buyya, “Deep Reinforcement Learning for Energy and Time Optimized Scheduling of Precedence-Constrained Tasks in Edge–Cloud Computing Environments,” Future Generation Computer Systems, vol. 137, pp. 14-30, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Yuqing Wang, and Xiao Yang, “Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning,” 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE), Shanghai, China, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Keqin Li, “Design and Analysis of Heuristic Algorithms for Energy-Constrained Task Scheduling With Device-Edge-Cloud Fusion,” IEEE Transactions on Sustainable Computing, vol. 8, no. 2, pp. 208-221, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. Wenhao Fan et al., “Collaborative Service Placement, Task Scheduling, and Resource Allocation for Task Offloading with Edge-Cloud Cooperation,” IEEE Transactions on Mobile Computing, vol. 23, no. 1, pp. 238-256, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. Jiangjiang Zhang et al., “A Many-Objective Ensemble Optimization Algorithm for the Edge Cloud Resource Scheduling Problem,” IEEE Transactions on Mobile Computing, vol. 23, no. 2, pp. 1330-1346, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link] 
  14. Kaige Zhu et al., “Learning to Optimize Workflow Scheduling for an Edge–Cloud Computing Environment,” IEEE Transactions on Cloud Computing, vol. 12, no. 3, pp. 897-912, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  15. K. Malathi, Dr.R. Anandan, and Dr.J. Frank Vijay, “Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm,” Journal of Internet Services and Information Security, vol. 13, no. 1, pp. 34-43, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Qiqi Zhang, Shaojin Geng, and Xingjuan Cai, “Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment,” CMES - Computer Modeling in Engineering and Sciences, vol. 135, no. 3, pp. 1863-1900, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Mehrnoosh Toghyani, Reihaneh Khorsand, and Hamidreza Khaksar, “QoS-SLA-aware Optimization Framework for IoT-Service Placement in Integrated Fog-Cloud Computing,” Journal of Grid Computing, vol. 23, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. A. S. Abohamama, Amir El-Ghamry, and Eslam Hamouda, “Real-Time Task Scheduling Algorithm for IoT-Based Applications in the Cloud–Fog Environment,” Journal of Network and Systems Management, vol. 30, pp. 1-35, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Ramamoorthy Karthikeyan, and Venkatachalam Balamurugan, “Energy‐Aware and SLA‐Guaranteed Optimal Virtual Machine Swap and Migrate System in Cloud‐Internet of Things,” Concurrency and Computation: Practice and Experience, vol. 33, no. 10, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. Asan Baker Kanbar, and Kamaran Faraj, “Region Aware Dynamic Task Scheduling and Resource Virtualization for Load Balancing in IoT–fog Multi-cloud Environment,” Future Generation Computer Systems, vol. 137, pp. 70-86, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Aroosa Mubeen et al., “Alts: An Adaptive Load Balanced Task Scheduling Approach for Cloud Computing,” Processes, vol. 9, no. 9, pp. 1-15, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. 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]
  23. Soghra Mousavi et al., “Directed Search: A New Operator in NSGA-II for Task Scheduling in IoT Based on Cloud-Fog Computing,” IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 2144-2157, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Meeniga Sriraghavendra et al., DoSP: A Deadline-Aware Dynamic Service Placement Algorithm for Workflow-Oriented IoT Applications in Fog-Cloud Computing Environments, Energy Conservation Solutions for Fog-Edge Computing Paradigms, Springer, pp. 21-47, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Jingyao Li et al., “Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing,” 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia, pp. 611-616, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Jianhang Tang et al., “Latency-Aware Task Scheduling in Software-Defined Edge and Cloud Computing with Erasure-Coded Storage Systems,” IEEE Transactions on Cloud Computing, vol. 11, no. 2, pp. 1575-1590, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  27. Xunzheng Zhang et al., “Energy Minimization Task Offloading Mechanism with Edge-Cloud Collaboration in IoT Networks,” 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Helsinki, Finland, pp. 1-7, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  28. Mengyu Sun et al., “Latency-aware Scheduling for Data-oriented Service Requests in Collaborative IoT-edge-cloud Networks,” Future Generation Computer Systems, vol. 163, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  29. Sadoon Azizi et al., “Deadline-aware and Energy-Efficient IoT Task Scheduling in Fog Computing Systems: A Semi-greedy Approach,” Journal of Network and Computer Applications, vol. 201, pp. 1-13, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  30. Sai Wang, Xiaoyang Li, and Yi Gong, “Energy-Efficient Task Offloading and Resource Allocation for Delay-Constrained Edge-Cloud Computing Networks,” IEEE Transactions on Green Communications and Networking, vol. 8, no. 1, pp. 514-524, 2024.
  31. [CrossRef] [Google Scholar] [Publisher Link]
  32. Yaswanth Chowdary Thotakura et al., “An Efficient Task Scheduling for Latency Sensitive Tasks in Edge - Cloud Computing Environment,” 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), Kamand, India, pp. 1-9, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  33. Qi Zhang et al., “Task Offloading and Resource Scheduling in Hybrid Edge-Cloud Networks,” IEEE Access, vol. 9, pp. 85350-85366, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  34. JongBeom Lim, “Latency-Aware Task Scheduling for IoT Applications Based on Artificial Intelligence with Partitioning in Small-Scale Fog Computing Environments,” Sensors, vol. 22, no. 19, pp. 1-12, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  35. Muhammad Bukhsh, Saima Abdullah, and Imran Sarwar Bajwa, “A Decentralized Edge Computing Latency-Aware Task Management Method with High Availability for IoT Applications,” IEEE Access, vol. 9, pp. 138994-139008, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  36. Zhenyu Wen et al., “Janus: Latency-Aware Traffic Scheduling for IoT Data Streaming in Edge Environments,” IEEE Transactions on Services Computing, vol. 16, no. 6, pp. 4302-4316, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  37. Abhijeet Mahapatra et al., “Latency-aware Internet of Things Scheduling in Heterogeneous Fog-Cloud Paradigm,” 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India, pp. 1-7, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  38. Syed Rizwan Hassan et al., “Design of Latency-Aware IoT Modules in Heterogeneous Fog-Cloud Computing Networks,” Computers, Materials & Continua, vol. 70, no. 3, pp. 6057-6072, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  39. Rabeea Basir et al., “Cloudlet Selection in Cache-Enabled Fog Networks for Latency Sensitive IoT Applications,” IEEE Access, vol. 9, pp. 93224-93236, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]
  40. Arash Deldari, and Alireza Holghinezhad, “An IoT-based Bag-of-tasks Scheduling Framework for Deadline-Sensitive Applications in Fog-cloud Environment,” Computing, vol. 107, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  41. Upma Arora, and Nipur Singh, “IoT Application Modules Placement in Heterogeneous Fog–Cloud Infrastructure,” International Journal of Information Technology, vol. 13, pp. 1975-1982, 2021.
    [    CrossRef] [Google Scholar] [Publisher Link]