{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T14:45:12Z","timestamp":1784645112647,"version":"3.55.0"},"reference-count":42,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Algorithms"],"abstract":"<jats:p>Managing resource use in cloud and edge environments is crucial for optimizing performance and efficiency. Traditionally, this process is performed with detailed knowledge of the available infrastructure while being application-specific. However, it is common that users cannot accurately specify their applications\u2019 low-level requirements, and they tend to overestimate them\u2014a problem further intensified by their lack of detailed knowledge on the infrastructure\u2019s characteristics. In this context, resource orchestration mechanisms perform allocations based on the provided worst-case assumptions, with a direct impact on the performance of the whole infrastructure. In this work, we propose a resource orchestration mechanism based on intents, in which users provide their high-level workload requirements by specifying their intended preferences for how the workload should be managed, such as prioritizing high capacity, low cost, or other criteria. Building on this, the proposed mechanism dynamically assigns resources to applications through a Reinforcement Learning method leveraging the feedback from the users and infrastructure providers\u2019 monitoring system. We formulate the respective problem as a discrete-time, finite horizon Markov decision process. Initially, we solve the problem using a tabular Q-learning method. However, due to the large state space inherent in real-world scenarios, we also employ Deep Reinforcement Learning, utilizing a neural network for the Q-value approximation. The presented mechanism is capable of continuously adapting the manner in which resources are allocated based on feedback from users and infrastructure providers. A series of simulation experiments were conducted to demonstrate the applicability of the proposed methodologies in intent-based resource allocation, examining various aspects and characteristics and performing comparative analysis.<\/jats:p>","DOI":"10.3390\/a18100627","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T13:33:41Z","timestamp":1759757621000},"page":"627","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Intent-Based Resource Allocation in Edge and Cloud Computing Using Reinforcement Learning"],"prefix":"10.3390","volume":"18","author":[{"given":"Dimitrios","family":"Konidaris","sequence":"first","affiliation":[{"name":"Department of Digital Systems, University of Peloponnese, 23100 Sparta, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Polyzois","family":"Soumplis","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Zografou, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Varvarigos","sequence":"additional","affiliation":[{"name":"Department of Informatics and Telematics, Harokopio University of Athens, Kallithea, 17676 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1823-3027","authenticated-orcid":false,"given":"Panagiotis","family":"Kokkinos","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Peloponnese, 23100 Sparta, Greece"},{"name":"Institute of Communication and Computer Systems (ICCS), National Technical University of Athens, Zografou, 15772 Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kretsis, A., Kokkinos, P., Soumplis, P., Olmos, J.J.V., Feh\u00e9r, M., Sipos, M., Lucani, D.E., Khabi, D., Masouros, D., and Siozios, K. (2021, January 7\u201310). Serrano: Transparent application deployment in a secure, accelerated and cognitive cloud continuum. Proceedings of the 2021 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), Athens, Greece.","DOI":"10.1109\/MeditCom49071.2021.9647689"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Kokkinos, P., Margaris, D., and Spiliotopoulos, D. (2022). A Quality of Experience Illustrator User Interface for Cloud Provider Recommendations. HCI International 2022 Posters, Proceedings of the 24th International Conference on Human-Computer Interaction, HCII 2022, Virtual, 26 June\u20131 July 2022, Springer.","DOI":"10.1007\/978-3-031-06417-3_42"},{"key":"ref_3","unstructured":"Clemm, A., Ciavaglia, L., Granville, L.Z., and Tantsura, J. (2025, September 30). RFC 9315: Intent-Based Networking\u2014Concepts and Definitions. RFC Editor. Available online: https:\/\/www.rfc-editor.org\/rfc\/rfc9315.html."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mardian, R.D., Suryanegara, M., and Ramli, K. (2019, January 28\u201330). Measuring Quality of Service (QoS) and Quality of Experience (QoE) on 5G Technology: A Review. Proceedings of the 2019 IEEE International Conference on Innovative Research and Development (ICIRD), Piscataway, NJ, USA.","DOI":"10.1109\/ICIRD47319.2019.9074681"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"22862","DOI":"10.1109\/ACCESS.2020.2969208","article-title":"A survey on intent-driven networks","volume":"8","author":"Pang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Leivadeas, A., and Falkner, M. (2021, January 1\u20134). VNF placement problem: A multi-tenant intent-based networking approach. Proceedings of the 2021 24th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Paris, France.","DOI":"10.1109\/ICIN51074.2021.9385553"},{"key":"ref_7","first-page":"1","article-title":"Resource management in fog\/edge computing: A survey on architectures, infrastructure, and algorithms","volume":"52","author":"Hong","year":"2019","journal-title":"Acm Comput. Surv."},{"key":"ref_8","unstructured":"Sutton, R.S. (2018). Reinforcement learning: An introduction. A Bradford Book, MIT Press."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kokkinos, P., Varvarigos, A., Konidaris, D., and Tserpes, K. (2023). Intent-Based Allocation of Cloud Computing Resources Using Q-Learning. International Symposium on Algorithmic Aspects of Cloud Computing, Springer.","DOI":"10.1007\/978-3-031-49361-4_10"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1016\/j.automatica.2006.09.019","article-title":"Model-free Q-learning designs for linear discrete-time zero-sum games with application to H-infinity control","volume":"43","author":"Lewis","year":"2007","journal-title":"Automatica"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"113820","DOI":"10.1016\/j.eswa.2020.113820","article-title":"Multi-DQN: An ensemble of Deep Q-learning agents for stock market forecasting","volume":"164","author":"Carta","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1729881419853185","DOI":"10.1177\/1729881419853185","article-title":"Decision-making method for vehicle longitudinal automatic driving based on reinforcement Q-learning","volume":"16","author":"Gao","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152126","DOI":"10.1109\/ACCESS.2019.2948111","article-title":"Q-learning aided resource allocation and environment recognition in LoRaWAN with CSMA\/CA","volume":"7","author":"Aihara","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"41468","DOI":"10.1109\/ACCESS.2021.3065314","article-title":"Priority-based joint resource allocation with deep q-learning for heterogeneous NOMA systems","volume":"9","author":"Rezwan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Dab, B., Aitsaadi, N., and Langar, R. (2019, January 8\u201312). Q-learning algorithm for joint computation offloading and resource allocation in edge cloud. Proceedings of the 2019 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), Washington, DC, USA.","DOI":"10.1109\/WCNC.2019.8885537"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"3058","DOI":"10.1109\/TII.2019.2892767","article-title":"Joint computation offloading, power allocation, and channel assignment for 5G-enabled traffic management systems","volume":"15","author":"Ning","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1109\/LWC.2019.2958121","article-title":"Q-learning based two-timescale power allocation for multi-homing hybrid RF\/VLC networks","volume":"9","author":"Kong","year":"2019","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5871","DOI":"10.1109\/TVT.2019.2907682","article-title":"Deep Q-learning aided networking, caching, and computing resources allocation in software-defined satellite-terrestrial networks","volume":"68","author":"Qiu","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Valkanis, A., Beletsioti, G.A., Nicopolitidis, P., Papadimitriou, G., and Varvarigos, E. (2020, January 3\u20135). Reinforcement learning in traffic prediction of core optical networks using learning automata. Proceedings of the 2020 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), Sharjah, United Arab Emirates.","DOI":"10.1109\/CCCI49893.2020.9256655"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1007\/s00607-023-01220-7","article-title":"An autonomous architecture based on reinforcement deep neural network for resource allocation in cloud computing","volume":"106","author":"Ghaemi","year":"2024","journal-title":"Computing"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3074","DOI":"10.1109\/TNSE.2020.3015689","article-title":"Enhanced online Q-learning scheme for resource allocation with maximum utility and fairness in edge-IoT networks","volume":"7","author":"AlQerm","year":"2020","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Eshratifar, A.E., and Pedram, M. (2018, January 23\u201325). Energy and performance efficient computation offloading for deep neural networks in a mobile cloud computing environment. Proceedings of the 2018 on Great Lakes Symposium on VLSI, Chicago, IL, USA.","DOI":"10.1145\/3194554.3194565"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s13677-021-00276-0","article-title":"Deep reinforcement learning-based workload scheduling for edge computing","volume":"11","author":"Zheng","year":"2022","journal-title":"J. Cloud Comput."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MNET.2019.1800386","article-title":"Resource management at the network edge: A deep reinforcement learning approach","volume":"33","author":"Zeng","year":"2019","journal-title":"IEEE Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s00607-022-01147-5","article-title":"Task scheduling in edge-fog-cloud architecture: A multi-objective load balancing approach using reinforcement learning algorithm","volume":"105","author":"Ghasemi","year":"2023","journal-title":"Computing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1007\/s00778-022-00775-9","article-title":"Data collection and quality challenges in deep learning: A data-centric AI perspective","volume":"32","author":"Whang","year":"2023","journal-title":"VLDB J."},{"key":"ref_27","unstructured":"Eimer, T., Lindauer, M., and Raileanu, R. (2023). Hyperparameters in reinforcement learning and how to tune them. International Conference on Machine Learning, PMLR."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abbas, K., Afaq, M., Ahmed Khan, T., Rafiq, A., and Song, W.C. (2020). Slicing the core network and radio access network domains through intent-based networking for 5G networks. Electronics, 9.","DOI":"10.3390\/electronics9101710"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.future.2022.12.033","article-title":"Intent-based network slicing for SDN vertical services with assurance: Context, design and preliminary experiments","volume":"142","author":"Martini","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1109\/MCOM.101.2100141","article-title":"End-to-end intent-based networking","volume":"59","author":"Velasco","year":"2021","journal-title":"IEEE Commun. Mag."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MNET.2024.3420120","article-title":"Intent-Based Management of Next-Generation Networks: An LLM-centric Approach","volume":"38","author":"Mekrache","year":"2024","journal-title":"IEEE Netw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akbari, N., Grundy, J., Cheema, A., and Toosi, A.N. (2025). IntentContinuum: Using LLMs to Support Intent-Based Computing Across the Compute Continuum. arXiv.","DOI":"10.1109\/ICWS67624.2025.00079"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Chao, W., and Horiuchi, S. (2018, January 20\u201322). Intent-based cloud service management. Proceedings of the 2018 21st Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Paris, France.","DOI":"10.1109\/ICIN.2018.8401600"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Kang, J.M., Lee, J., Nagendra, V., and Banerjee, S. (2017, January 8\u201312). LMS: Label management service for intent-driven cloud management. Proceedings of the 2017 IFIP\/IEEE Symposium on Integrated Network and Service Management (IM), Lisbon, Portugal.","DOI":"10.23919\/INM.2017.7987278"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5127","DOI":"10.1109\/TITS.2020.3027437","article-title":"Learning-based intent-aware task offloading for air-ground integrated vehicular edge computing","volume":"22","author":"Liao","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13677-021-00242-w","article-title":"Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system","volume":"10","author":"Wu","year":"2021","journal-title":"J. Cloud Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/MNET.2023.3326239","article-title":"Siats: A service intent-aware task scheduling framework for computing power networks","volume":"38","author":"Tang","year":"2023","journal-title":"IEEE Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1007\/s42979-023-01698-0","article-title":"Intent-driven orchestration: Enforcing service level objectives for cloud native deployments","volume":"4","author":"Metsch","year":"2023","journal-title":"SN Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2020.05.045","article-title":"Intent-based resource matching strategy in cloud","volume":"538","author":"He","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1007\/s10586-024-04893-7","article-title":"A survey on resource scheduling approaches in multi-access edge computing environment: A deep reinforcement learning study","volume":"28","author":"Ismail","year":"2025","journal-title":"Clust. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1007\/s10462-024-10756-9","article-title":"Deep reinforcement learning-based methods for resource scheduling in cloud computing: A review and future directions","volume":"57","author":"Zhou","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_42","unstructured":"Amazon, E. (2025, September 30). Amazon EC2 Instance Types. Available online: https:\/\/aws.amazon.com\/ec2\/instance-types."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/627\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T13:49:41Z","timestamp":1759758581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/10\/627"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":42,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["a18100627"],"URL":"https:\/\/doi.org\/10.3390\/a18100627","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]}}}