{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:02:30Z","timestamp":1772755350793,"version":"3.50.1"},"reference-count":22,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":19,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Cloud computing offers on\u2010demand access to computing resources; however, minimizing response time while ensuring compliance with Service Level Agreements (SLAs) remains a critical challenge. The proposed CLOUD SMART framework aims to intelligently minimize response time, process delays, and propagation latency in cloud environments through adaptive, SLA\u2010aware dynamic scheduling. This study evaluates the model using the Cloud Workload Dataset for Scheduling Analysis, available on Kaggle. The dataset undergoes preprocessing, including median imputation for missing numerical values, and the derivation of key features such as response time, deadlines, and priority tiers for accurate workload profiling. Workload characterization through statistical profiling and clustering reveals patterns, arrival rates, and task categories that guide scheduling strategy selection. Baseline performance is established using discrete event simulation of standard policies such as FCFS, SJF\/Min\u2010Min, Max\u2010Min, and EDF. A novel Predictive Deadline\u2010Aware Hybrid Scheduling (PDHS) approach is integrated to predict completion times and dynamically switch scheduling strategies based on urgency. An execution and closed\u2010loop feedback mechanism enables real\u2010time adaptation. Experimental results show that CLOUD SMART significantly reduces response time, improves SLA compliance, and enhances resource utilization compared to static scheduling baselines. The PDHS model achieves an average response time of 6.72\u2009s, significantly lower than all baselines. Average waiting time is reduced to 2.87\u2009s, and Makespan improves to 138.4\u2009s. SLA compliance reaches 97%, with a deadline miss ratio of only 3%. System throughput is enhanced to 38.5 tasks per second, and resource utilization climbs to 92%. Prediction accuracy excels with an MAE of 0.94\u2009s and RMSE of 1.26\u2009s.<\/jats:p>","DOI":"10.1002\/cpe.70561","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T03:03:56Z","timestamp":1768964636000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>CLOUD SMART<\/scp>\n                    : A Dynamic Scheduling Framework for Minimizing Response Time in Cloud Environments"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2507-5136","authenticated-orcid":false,"given":"S.","family":"Anuradha","sequence":"first","affiliation":[{"name":"Department of Computer Science and Applications SRM Institute of Science and Technology (FSH)  Chennai Tamilnadu India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K.","family":"Unnikrishnan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering Rajiv Gandhi Institute of Technology, Government Engineering College  Kottayam Kerala India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S.","family":"Reshma","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering  Bangalore India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"O.","family":"Bhaskaru","sequence":"additional","affiliation":[{"name":"Department of CSE Vignan's Foundation for Science, Technology and Research  Guntur Andhra Pradesh India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richa","family":"Sharma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Lovely Professional University  Phagwara India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/EIECC64539.2024.10929270","volume-title":"2024 4th International Conference on Electronic Information Engineering and Computer Communication (EIECC)","author":"Wang X.","year":"2024"},{"key":"e_1_2_9_3_1","doi-asserted-by":"crossref","first-page":"438","DOI":"10.22247\/ijcna\/2022\/214505","article-title":"Multi Objective Fault Tolerance Model for Scientific Workflow Scheduling on Cloud Computing","volume":"4","author":"Anuradha S.","year":"2022","journal-title":"International Journal on Computer Networks an Applications (IJCNA)"},{"key":"e_1_2_9_4_1","volume-title":"ADS and AVS\u2010 Its Cyber Security and Privacy Legal Issues Autonomous Driving and Advanced Driver\u2010Assistance","author":"Ravishankar C. V.","year":"2021"},{"key":"e_1_2_9_5_1","first-page":"1","article-title":"Task Scheduling Using a Glowworm\u2010Based Optimal Heterogeneous Earliest Finish Time Algorithm for Mobile Grids","volume":"17","author":"Ashwitha A.","year":"2024","journal-title":"International Journal of Information Technology"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2021.3115262"},{"issue":"21","key":"e_1_2_9_7_1","doi-asserted-by":"crossref","first-page":"32305","DOI":"10.1007\/s11042-023-14565-0","article-title":"Task Scheduling for Improved Response Time of Latency Sensitive Applications in Fog Integrated Cloud Environment","volume":"82","author":"Mehta R.","year":"2023","journal-title":"Multimedia Tools and Applications"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2022.06.012"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3318553"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/CLOUD55607.2022.00056","volume-title":"2022 IEEE 15th International Conference on Cloud Computing (CLOUD)","author":"Tuli S.","year":"2022"},{"issue":"15","key":"e_1_2_9_11_1","doi-asserted-by":"crossref","DOI":"10.3390\/electronics11152464","article-title":"Deadline\u2010Aware Dynamic Task Scheduling in Edge\u2013Cloud Collaborative Computing","volume":"11","author":"Zhang Y.","year":"2022","journal-title":"Electronics"},{"key":"e_1_2_9_12_1","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1109\/ICSCCC51823.2021.9478160","volume-title":"2021 2nd International Conference on Secure Cyber Computing and Communications (ICSCCC)","author":"Pol S. 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S.","year":"2022","journal-title":"Journal of Network and Systems Management"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.10.012"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/math11153364"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5919"},{"issue":"1","key":"e_1_2_9_17_1","doi-asserted-by":"crossref","DOI":"10.1007\/s11227-024-06668-8","article-title":"Introducing an Improved Deep Reinforcement Learning Algorithm for Task Scheduling in Cloud Computing","volume":"81","author":"Salari\u2010Hamzehkhani B.","year":"2025","journal-title":"Journal of Supercomputing"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-022-03630-2"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2023.03.029"},{"key":"e_1_2_9_20_1","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1109\/INCIP64058.2025.11019301","volume-title":"2025 International Conference on Next Generation Communication & Information Processing (INCIP)","author":"Singh A. 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