{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:25:44Z","timestamp":1760149544041,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T00:00:00Z","timestamp":1690934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>Cloud computing delivers robust computational services by processing tasks on its virtual machines (VMs) using resource-scheduling algorithms. The cloud\u2019s existing algorithms provide limited results due to inappropriate resource scheduling. Additionally, these algorithms cannot process tasks generating faults while being computed. The primary reason for this is that these existing algorithms need an intelligence mechanism to enhance their abilities. To provide an intelligence mechanism to improve the resource-scheduling process and provision the fault-tolerance mechanism, an algorithm named reinforcement learning-shortest job first (RL-SJF) has been implemented by integrating the RL technique with the existing SJF algorithm. An experiment was conducted in a simulation platform to compare the working of RL-SJF with SJF, and challenging tasks were computed in multiple scenarios. The experimental results convey that the RL-SJF algorithm enhances the resource-scheduling process by improving the aggregate cost by 14.88% compared to the SJF algorithm. Additionally, the RL-SJF algorithm provided a fault-tolerance mechanism by computing 55.52% of the total tasks compared to 11.11% of the SJF algorithm. Thus, the RL-SJF algorithm improves the overall cloud performance and provides the ideal quality of service (QoS).<\/jats:p>","DOI":"10.3390\/informatics10030064","type":"journal-article","created":{"date-parts":[[2023,8,2]],"date-time":"2023-08-02T11:17:17Z","timestamp":1690975037000},"page":"64","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0068-0988","authenticated-orcid":false,"given":"Prathamesh","family":"Lahande","sequence":"first","affiliation":[{"name":"Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune 411016, India"}]},{"given":"Parag","family":"Kaveri","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune 411016, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5205-5263","authenticated-orcid":false,"given":"Jatinderkumar","family":"Saini","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune 411016, India"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1145\/1721654.1721672","article-title":"A view of cloud computing","volume":"53","author":"Armbrust","year":"2010","journal-title":"Commun. ACM"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dillon, T.S., Wu, C., and Chang, E. (2010, January 20\u201323). Cloud Computing: Issues and Challenges. Proceedings of the 2010 24th IEEE International Conference on Advanced Information Networking and Applications, Perth, Australia.","DOI":"10.1109\/AINA.2010.187"},{"key":"ref_3","first-page":"134","article-title":"Cloud Computing: Research Issues and Implications","volume":"2","author":"Prasad","year":"2013","journal-title":"Int. J. Cloud Comput. Serv. Sci."},{"key":"ref_4","first-page":"1159","article-title":"A survey of fault tolerance in cloud computing","volume":"33","author":"Kumari","year":"2021","journal-title":"J. King Saud Univ.\u2014Comput. Inf. Sci."},{"key":"ref_5","unstructured":"Sutton, R.S., and Barto, A.G. (2015). Reinforcement Learning: An Introduction, MIT Press."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cui, T., Yang, R., Wang, X., and Yu, S. (2023). Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments. Symmetry, 15.","DOI":"10.3390\/sym15010217"},{"key":"ref_7","first-page":"42","article-title":"An Optimized Shortest job first Scheduling Algorithm for CPU Scheduling","volume":"5","author":"Akhtar","year":"2015","journal-title":"J. Appl. Environ. Biol. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"418","DOI":"10.3390\/make5020025","article-title":"A Reinforcement Learning Approach for Scheduling Problems with Improved Generalization through Order Swapping","volume":"5","author":"Vivekanandan","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, H., Ding, W., Min, Q., Dai, Z., Jiang, Q., and Gu, C. (2023). A Meta Reinforcement Learning-Based Task Offloading Strategy for IoT Devices in an Edge Cloud Computing Environment. Appl. Sci., 13.","DOI":"10.3390\/app13095412"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"131","DOI":"10.31449\/inf.v46i2.3603","article-title":"Deep Reinforcement Learning-based anomaly detection for Video Surveillance","volume":"46","author":"Aberkane","year":"2022","journal-title":"Informatica"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Sheng, S., Chen, P., Chen, Z., Wu, L., and Yao, Y. (2021). Deep Reinforcement Learning-Based Task Scheduling in IoT Edge Computing. Sensors, 21.","DOI":"10.3390\/s21051666"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shin, D., and Kim, J. (2021). Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems. Appl. Sci., 11.","DOI":"10.3390\/app11188727"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.1109\/TNNLS.2021.3105905","article-title":"Solving Dynamic Traveling Salesman Problems with Deep Reinforcement Learning","volume":"34","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_14","unstructured":"Afshar, R., Zhang, Y., Firat, M., and Kaymak, U. (2020, January 18\u201320). A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning. Proceedings of the Asian Conference on Machine Learning, Bangkok, Thailand."},{"key":"ref_15","first-page":"3677","article-title":"Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization","volume":"35","author":"Cappart","year":"2021","journal-title":"Proc. Conf. AAAI Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.future.2019.08.032","article-title":"Q-learning based collaborative cache allocation in mobile edge computing","volume":"102","author":"Chien","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, Y., Fadda, E., Manerba, D., Tadei, R., and Terzo, O. (2019, January 1\u20134). Reinforcement Learning Algorithms for Online Single-Machine Scheduling. Proceedings of the Computer Science and Information Systems (FedCSIS), Leipzig, Germany.","DOI":"10.15439\/2020F100"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"71752","DOI":"10.1109\/ACCESS.2020.2987820","article-title":"Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9441","DOI":"10.1109\/JIOT.2020.2986803","article-title":"Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching","volume":"7","author":"Wang","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_20","first-page":"1621","article-title":"Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning","volume":"33","author":"Zhang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ren, J., He, Y., Yu, G., and Li, G.Y. (2019, January 15\u201318). Joint Communication and Computation Resource Allocation for Cloud-Edge Collaborative System. Proceedings of the 2019 IEEE Wireless Communications and Networking Conference, Marrakesh, Morocco.","DOI":"10.1109\/WCNC.2019.8885877"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yuan, W., Yang, M., He, Y., Wang, C., and Wang, B. (2019, January 27\u201330). Multi-Reward Architecture based Reinforcement Learning for Highway Driving Policies. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917304"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, J., Hui, P., Lv, T., and Lu, Y. (2018, January 15\u201318). Deep reinforcement learning based computation offloading and resource allocation for MEC. Proceedings of the 2018 IEEE Wireless Communications and Networking Conference (WCNC), Barcelona, Spain.","DOI":"10.1109\/WCNC.2018.8377343"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1960","DOI":"10.1109\/JIOT.2018.2871020","article-title":"Deep Reinforcement Learning Based Mode Selection and Resource Management for Green Fog Radio Access Networks","volume":"6","author":"Sun","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1587\/transcom.2017CQP0014","article-title":"A Deep Reinforcement Learning Based Approach for Cost- and Energy-Aware Multi-Flow Mobile Data Offloading","volume":"101","author":"Zhang","year":"2018","journal-title":"IEICE Trans. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhong, C., Gursoy, M.C., and Velipasalar, S. (2018, January 21\u201323). A deep reinforcement learning-based framework for content caching. Proceedings of the 2018 52nd Annual Conference on Information Sciences and Systems (CISS), Princeton, NJ, USA.","DOI":"10.1109\/CISS.2018.8362276"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kolomvatsos, K., and Anagnostopoulos, C. (2017). Reinforcement Learning for Predictive Analytics in Smart Cities. Informatics, 4.","DOI":"10.3390\/informatics4030016"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.jpdc.2014.10.001","article-title":"Improving reliability in resource management through adaptive reinforcement learning for distributed systems","volume":"75","author":"Hussin","year":"2015","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1080\/00207543.2011.571443","article-title":"Distributed policy search reinforcement learning for job-shop scheduling tasks","volume":"50","author":"Gabel","year":"2011","journal-title":"Int. J. Prod. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.engappai.2006.06.019","article-title":"A reinforcement learning approach to dynamic resource allocation","volume":"20","author":"Vengerov","year":"2007","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chen, W., Liu, Y., Dai, Y., and Luo, Z. (2022, January 22\u201327). Optimal Qos-Aware Network Slicing for Service-Oriented Networks with Flexible Routing. Proceedings of the ICASSP 2022\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore.","DOI":"10.1109\/ICASSP43922.2022.9747910"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Poryazov, S., Saranova, E., and Andonov, V. (2019, January 23\u201325). Overall Model Normalization towards Adequate Prediction and Presentation of QoE in Overall Telecommunication Systems. Proceedings of the 2019 14th International Conference on Advanced Technologies, Systems and Services in Telecommunications (TELSIKS), Ni\u0161, Serbia.","DOI":"10.1109\/TELSIKS46999.2019.9002295"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.3390\/s18072247","article-title":"A Software-Defined Networking Framework to Provide Dynamic QoS Management in IEEE 802.11 Networks","volume":"18","year":"2018","journal-title":"Sensors"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, W., and Deelman, E. (2012, January 8\u201312). WorkflowSim: A toolkit for simulating scientific workflows in distributed environments. Proceedings of the 2012 IEEE 8th International Conference on E-Science, Chicago, IL, USA.","DOI":"10.1109\/eScience.2012.6404430"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/3\/64\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:24:33Z","timestamp":1760127873000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/3\/64"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,2]]},"references-count":34,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["informatics10030064"],"URL":"https:\/\/doi.org\/10.3390\/informatics10030064","relation":{},"ISSN":["2227-9709"],"issn-type":[{"type":"electronic","value":"2227-9709"}],"subject":[],"published":{"date-parts":[[2023,8,2]]}}}