*Result*: IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on learning.
Title:
IntelliScheduler: an edge-cloud computing environment hybrid deep learning framework for task scheduling based on learning.
Authors:
Raju LR; Dept. of Computer Science and Engineering, Matrusri Engineering College, Hyderabad, India. lraghavendarraju@matrusri.edu.in., Reddy MVK; Department of Computer Science and Engineering, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, Telangana, 500075, India., Surukanti SR; Dept.of Computer Science and Engineering, Sreyas Institute of Engineering and Technology, Hyderabad, Telangana, India., Sudhakar G; Associate Professor, Department of Computer Science and Engineering, , Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500043, India., Subrahmanya Sarma M VV; Dept. Of Computer Applications, School of Engineering, Aditya University Surampalem, Andhra Pradesh, India., Adepu A; Polytechnic Darbhanga, Bihar, Maulana Azad National Urdu University, Gachibowli, Hyderabad, India.
Source:
Scientific reports [Sci Rep] 2026 Feb 27. Date of Electronic Publication: 2026 Feb 27.
Publication Model:
Ahead of Print
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
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Chen, Z. et al. Computation offloading and task scheduling for DNN-based applications in cloud-edge computing. IEEE Access 8, 115537–115547. https://doi.org/10.1109/ACCESS.2020.3004509 (2020).
Oo, Thanda and Ko, Young-Bae (2019). International Conference on Information and Communication Technology Convergence (ICTC) - Application-aware Task Scheduling in Heterogeneous Edge Cloud. , 1316–1320. http://https://doi.org/10.1109/ICTC46691.2019.8939927.
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Xu Zhao; Guangqiu Huang; Ling Gao; Maozhen Li and Quanli Gao; (2021). Low load DIDS task scheduling based on Q-learning in edge computing environment . Journal of Network and Computer Applications. http://https://doi.org/10.1016/j.jnca.2021.103095.
BOONHATAI KRUEKAEW AND WARANGKHANA KIMPAN. (2022). Multi-Objective Task Scheduling Optimization for Load Balancing in Cloud Computing Environment Using Hybrid Artificial Bee Colony Algorithm With Reinforcement Learning. IEEE. 10, pp.17803 - 17818. https://doi.org/10.1109/ACCESS.2022.3149955.
Miao, Y. et al. Intelligent task prediction and computation offloading based on mobile-edge cloud computing. Future Gener. Comput. Syst. 102, 925–931. https://doi.org/10.1016/j.future.2019.09.035 (2020).
Jauro, F. et al. Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend. Appl. Soft Comput. https://doi.org/10.1016/j.asoc.2020.106582 (2020).
Khaledian, N., Voelp, M., Azizi, S. & Hosseini Shirvani, M. AI-based & heuristic workflow scheduling in cloud and fog computing: A systematic review. Cluster Comput. 27, 10265–10298. https://doi.org/10.1007/s10586-024-04442-2 (2024).
Ghorbani, M., Khaledian, N. & Moazzami, S. ALBLA: an adaptive load balancing approach in edge-cloud networks utilizing learning automata. Computing 107, 34. https://doi.org/10.1007/s00607-024-01380-0 (2025).
Shahmirzadi, D., Khaledian, N. & Rahmani, A. M. Analyzing the impact of various parameters on job scheduling in the Google cluster dataset. Cluster Comput. 27, 7673–7687. https://doi.org/10.1007/s10586-024-04377-8 (2024).
Wu, H., Li, X. & Deng, Y. Deep learning-driven wireless communication for edge-cloud computing: Opportunities and challenges. J. Cloud Comput. https://doi.org/10.1186/s13677-020-00168-9 (2020).
Cao, Zhi; Zhang, Honggang; Cao, Yu and Liu, Benyuan (2019). 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks (MSN) - A Deep Reinforcement Learning Approach to Multi-Component Job Scheduling in Edge Computing. 19–24. https://doi.org/10.1109/MSN48538.2019.00018.
Nandhakumar, A. R., Baranwal, A., Choudhary, P., Golec, M. & Gill, S. S. EdgeAISim: A toolkit for simulation and modelling of AI models in edge computing environments. Elsevier 31, 1–14. https://doi.org/10.1016/j.measen.2023.100939 (2023).
YanalAlahmad; Tariq Daradkeh and Anjali Agarwal; (2021). Proactive Failure-Aware Task Scheduling Framework for Cloud Computing . IEEE Access. https://doi.org/10.1109/access.2021.3101147.
Ali Shakarami; Ali Shahidinejad and Mostafa Ghobaei-Arani; (2021). An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach . Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2021.102974.
Hosseinzadeh, M. et al. Task Scheduling Mechanisms for Fog Computing: A Systematic Survey. IEEE. 11, 50994–51017. https://doi.org/10.1109/ACCESS.2023.3277826 (2023).
Shakarami, A., Ghobaei-Arani, M. & Shahidinejad, A. A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective. Comput. Netw. 182, 107496 (2020).
Chen, Z. et al. Computation offloading and task scheduling for DNN-based applications in cloud-edge computing. IEEE Access 8, 115537–115547 (2020).
Jiangjiang Zhang, Zhenhu Ning, Muhammad Waqas, HishamAlasmary, ShanshanTu and Sheng Chen. (2023). Hybrid Edge-cloud Collaborator Resource Scheduling Approach Based on Deep Reinforcement Learning and Multi-Objective Optimization. IEEE. , pp.1 - 14. https://doi.org/10.1109/TC.2023.3326977.
HojjatBaghban, Amir Rezapour, Ching-Hsien Hsu, SirapopNuannimnoi and Ching-Yao Huang. (2022). Edge-AI: IoT Request Service Provisioning in Federated Edge Computing Using Actor-Critic Reinforcement Learning. IEEE., pp.1–10. https://doi.org/10.1109/TEM.2022.3166769.
Sellami, B., Hakiri, A. & Yahia, S. B. Deep reinforcement learning for energy-aware task offloading in join SDN-Blockchain 5G massive IoT edge network. Future Gener. Comput. Syst. 137, 363–379. https://doi.org/10.1016/j.future.2022.07.024 (2022).
Jain, V., Kumar, B. & Gupta, A. Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment. J. King Saud Univ. - Comput. Inf. Sci. 34(8), 5708–5720. https://doi.org/10.1016/j.jksuci.2022.02.005 (2022).
Khayyat, M. et al. Advanced deep learning-based computational offloading for multilevel vehicular edge-cloud computing networks. IEEE Access https://doi.org/10.1109/ACCESS.2020.3011705 (2020).
Hu, L., Sun, G. & Ren, Y. CoEdge: Exploiting the edge-cloud collaboration for faster deep learning. IEEE Access 8, 100533–100541. https://doi.org/10.1109/ACCESS.2020.2995583 (2020).
Wu, H., Zhang, Z., Guan, C., Wolter, K. & Xu, M. Collaborate edge and cloud computing with distributed deep learning for smart city Internet of Things. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2020.2996784 (2020).
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Contributed Indexing:
Keywords: Cloud computing; Deep learning; Edge computing; Internet of things; Reinforcement learning; Task scheduling
Entry Date(s):
Date Created: 20260227 Latest Revision: 20260227
Update Code:
20260228
DOI:
10.1038/s41598-026-41330-8
PMID:
41760833
Database:
MEDLINE
*Further Information*
*Edge-cloud computing has emerged as an important paradigm for modern Internet of Things (IoT) workflow applications, enabling low latency and on-demand resource allocation. In scenarios with heterogeneous deadlines and varying workloads, SLA compliance requires efficient coordination between edge and cloud resources. However, cloud-centric scheduling and heuristic approaches tend to lack adaptability to rapidly changing system conditions and, as a result, experience long waiting times (the same applies to QoS). To tackle these issues, we present IntelliScheduler, a hybrid actor-critic deep reinforcement learning framework for adaptive task scheduling in an edge-cloud system. Our framework presents a runtime-aware state representation combined with a learning-based decision mechanism, backed by a multi-buffer experience replay architecture. Second, a learning-based optimal task scheduling (LbOTS) algorithm is developed to minimise total task execution delay by discovering optimal deployment decisions across edge and cloud computational resources using latency-aware reward modelling. We assess the proposed approach by conducting extensive simulation experiments under different workloads. We evaluate LbOTS across various experimental scenarios and report up to 13% higher normalised reward, 67% lower training loss, 52-66% lower operational cost, and 80-90% lower rejection rate compared to PSO, MBO, and MOPSObaselines, achieving approximately 15-75% better QoE. Though the current assessment is simulation-based, the adaptive learning formulation is highly relevant for application in dynamic edge-cloud scheduling scenarios.
(© 2026. The Author(s).)*
*Declarations. Competing interests: The authors declare no competing interests. Ethical approval: This research does not involve humans or animals, so no ethical approval is required. Consent for publication: The authors give consent for their publication.*