{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:49:58Z","timestamp":1777682998617,"version":"3.51.4"},"reference-count":33,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHS"],"published-print":{"date-parts":[[2024,10,15]]},"abstract":"<jats:p>The machine learning technique has been used to increase cloud management\u2019s intelligence. Effective resource provisioning also preserves the environment. Manual cloud management has some difficult problems, such as complexity in cloud systems and scale issues. Hence, this paper introduces a new task for managing the resources in the cloud using deep learning. The aim is to predict the overall workload and server status prediction to the cloud resource management. Initially, performance monitoring is performed to keep aware of the performance of the application and guarantee the cloud application\u2019s performance. In the suggested work, the required data is collected for the resource utilization on multiple Virtual Machine (VM) metrics. The VM provisioning is performed next to rectify the issues of resource provisioning. After that, the workload and server status prediction is conducted, where the Weighted Recurrent Neural Network (W-RNN) is adopted. After attaining the predicted workload, the VM placement module is carried out. Here, the virtual resource\u2019s quantity is attained. Moreover, the multi-objective functions like resource utilization; cost, energy, time, and Quality of Service (QoS) are derived in this phase with the help of the Improved Rain Optimization Algorithm (IROA). Subsequently, the VM recycling is performed in the suggested work. Here, a resource collector is given for the virtual resources recycling task. It scans the applications of the cloud in the data centre and processes the VM recycling for every application. While considering the statistical analysis of the IROA-W-RNN-based resource management system achieved a mean of 56.27% than JAYA-W-RNN, 21.09% than SCO-W-RNN, 60.2% than MFOA-W-RNN, and 16.74% than DA-W-RNN for configuration 4. Finally, the numerical analysis is conducted to validate the presented resource management task with the aid of various conventional tasks.<\/jats:p>","DOI":"10.3233\/jhs-230212","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T13:03:47Z","timestamp":1718111027000},"page":"583-606","source":"Crossref","is-referenced-by-count":5,"title":["Improved optimization algorithm for resource management in cloud applications with performance monitor of VM provisioning, placement and recycling"],"prefix":"10.1177","volume":"30","author":[{"given":"Kapil","family":"Vhatkar","sequence":"first","affiliation":[{"name":"Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, Pimpri-Chinchwad, Maharashtra 411018, India"}]},{"given":"Atul B.","family":"Kathole","sequence":"additional","affiliation":[{"name":"Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, Pimpri-Chinchwad, Maharashtra 411018, India"}]},{"given":"Aniruddha P","family":"Kshirsagar","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Department, K.B.P College of Engineering, Satara, Pirwadi, Maharashtra 415001, India"}]},{"given":"Jayashree","family":"Katti","sequence":"additional","affiliation":[{"name":"IT Department, Pimpri Chinchwad College of Engineering, Pune-411044, India"}]}],"member":"179","reference":[{"key":"10.3233\/JHS-230212_ref1","doi-asserted-by":"publisher","first-page":"122838","DOI":"10.1109\/ACCESS.2020.3007469","article-title":"Dynamic resource management for cloud spot markets","volume":"8","author":"Alzhouri","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230212_ref2","doi-asserted-by":"publisher","first-page":"164815","DOI":"10.1109\/ACCESS.2020.3021948","article-title":"Novel cloud-RRH architecture with radio resource management and QoS strategies for 5G HetNets","volume":"8","author":"Chabbouh","year":"2020","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/JHS-230212_ref3","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1109\/TETCI.2017.2755691","article-title":"Entropy4Cloud: Using entropy-based complexity to optimize cloud service resource management","volume":"2","author":"Chen","year":"2018","journal-title":"IEEE Transactions on Emerging Topics in Computational Intelligence"},{"key":"10.3233\/JHS-230212_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-37108-0_13"},{"key":"10.3233\/JHS-230212_ref5","doi-asserted-by":"publisher","first-page":"157052","DOI":"10.1109\/ACCESS.2021.3127521","article-title":"UniDRM: Unified data and resource management for federated vehicular cloud computing","volume":"9","author":"Danquah","year":"2021","journal-title":"IEEE Access"},{"issue":"1","key":"10.3233\/JHS-230212_ref6","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/TSC.2017.2679738","article-title":"A hybrid bio-inspired algorithm for scheduling and resource management in cloud environment","volume":"13","author":"Domanal","year":"2020","journal-title":"IEEE Transactions on Services Computing"},{"key":"10.3233\/JHS-230212_ref7","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/ACCESS.2015.2422266","article-title":"Cooperative radio resource management in heterogeneous cloud radio access networks","volume":"3","author":"Gerasimenko","year":"2015","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230212_ref8","doi-asserted-by":"crossref","unstructured":"N.\u00a0Gholipour, E.\u00a0Arianyan and R.\u00a0Buyya, A novel energy-aware resource management technique using joint VM and container consolidation approach for green computing in cloud data centers, Simulation Modelling Practice and Theory 104 (2020), 102127.","DOI":"10.1016\/j.simpat.2020.102127"},{"key":"10.3233\/JHS-230212_ref9","doi-asserted-by":"crossref","unstructured":"S.S.\u00a0Gill, S.\u00a0Tuli, A.N.\u00a0Toosi, F.\u00a0Cuadrado, P.\u00a0Garraghan, R.\u00a0Bahsoon, H.\u00a0Lutfiyya, R.\u00a0Sakellariou, O.\u00a0Rana, S.\u00a0Dustdar and R.\u00a0Buyya, ThermoSim: Deep learning based framework for modeling and simulation of thermal-aware resource management for cloud computing environments, Journal of Systems and Software 166 (2020), 110596.","DOI":"10.1016\/j.jss.2020.110596"},{"issue":"1","key":"10.3233\/JHS-230212_ref10","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TR.2022.3161359","article-title":"An intelligent resource management solution for hospital information system based on cloud computing platform","volume":"72","author":"Gong","year":"2023","journal-title":"IEEE Transactions on Reliability"},{"issue":"2","key":"10.3233\/JHS-230212_ref11","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1109\/TCC.2015.2474369","article-title":"Hierarchical SLA-driven resource management for peak power-aware and energy-efficient operation of a cloud datacenter","volume":"4","author":"Goudarzi","year":"2016","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"10.3233\/JHS-230212_ref12","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Guo, Y.\u00a0Jin and S.\u00a0Hu, Risk evolution analysis of ship pilotage operation by an integrated model of FRAM and DBN, Reliability Engineering & System Safety 229 (2023).","DOI":"10.1016\/j.ress.2022.108850"},{"key":"10.3233\/JHS-230212_ref13","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.neucom.2022.11.089","article-title":"Stable and efficient resource management using deep neural network on cloud computing","volume":"521","author":"Jeong","year":"2023","journal-title":"Neurocomputing"},{"issue":"4","key":"10.3233\/JHS-230212_ref14","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1109\/TCC.2017.2720665","article-title":"Cloud resource management for analyzing big real-time visual data from network cameras","volume":"7","author":"Kaseb","year":"2019","journal-title":"IEEE Transactions on Cloud Computing"},{"issue":"3","key":"10.3233\/JHS-230212_ref15","doi-asserted-by":"crossref","first-page":"1848","DOI":"10.1109\/TCC.2020.2998017","article-title":"Forecasting cloud application workloads with CloudInsight for predictive resource management","volume":"10","author":"Kim","year":"2022","journal-title":"IEEE Transactions on Cloud Computing"},{"issue":"2","key":"10.3233\/JHS-230212_ref16","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1109\/TCC.2017.2652698","article-title":"Proactive thermal-aware resource management in virtualized HPC cloud datacenters","volume":"5","author":"Lee","year":"2017","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"10.3233\/JHS-230212_ref17","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.ins.2019.12.049","article-title":"Resource and replica management strategy for optimizing financial cost and user experience in edge cloud computing system","volume":"516","author":"Li","year":"2020","journal-title":"Information Sciences"},{"key":"10.3233\/JHS-230212_ref18","doi-asserted-by":"publisher","first-page":"76700","DOI":"10.1109\/ACCESS.2018.2884130","article-title":"A double evolutionary learning moth-flame optimization for real-parameter global optimization problems","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230212_ref19","doi-asserted-by":"publisher","first-page":"89989","DOI":"10.1109\/ACCESS.2022.3201147","article-title":"Sand cat swarm optimization based on stochastic variation with elite collaboration","volume":"10","author":"Li","year":"2022","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230212_ref20","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1016\/j.future.2020.07.026","article-title":"Memory-aware resource management algorithm for low-energy cloud data centers","volume":"113","author":"Liang","year":"2020","journal-title":"Future Generation Computer Systems"},{"key":"10.3233\/JHS-230212_ref21","doi-asserted-by":"publisher","first-page":"68778","DOI":"10.1109\/ACCESS.2022.3185987","article-title":"Scalable management of heterogeneous cloud resources based on evolution strategies algorithm","volume":"10","author":"Loncar","year":"2022","journal-title":"IEEE Access"},{"issue":"3","key":"10.3233\/JHS-230212_ref22","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/TCC.2014.2369419","article-title":"Physical machine resource management in clouds: A mechanism design approach","volume":"3","author":"Mashayekhy","year":"2015","journal-title":"IEEE Transactions on Cloud Computing"},{"issue":"4","key":"10.3233\/JHS-230212_ref23","doi-asserted-by":"publisher","first-page":"4979","DOI":"10.1109\/JESTPE.2020.3036405","article-title":"JAYA algorithm based on L\u00e9vy flight for global MPPT under partial shading in photovoltaic system","volume":"9","author":"Motamarri","year":"2021","journal-title":"IEEE Journal of Emerging and Selected Topics in Power Electronics"},{"key":"10.3233\/JHS-230212_ref24","doi-asserted-by":"publisher","first-page":"35392","DOI":"10.1109\/ACCESS.2020.2974856","article-title":"Dragonfly-based joint delay\/energy LTE downlink scheduling algorithm","volume":"8","author":"Nashaat","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230212_ref25","doi-asserted-by":"publisher","DOI":"10.3390\/atmos10110668"},{"key":"10.3233\/JHS-230212_ref26","doi-asserted-by":"crossref","unstructured":"K.\u00a0Raghavendar, I.\u00a0Batra and A.\u00a0Malik, A robust resource allocation model for optimizing data skew and consumption rate in cloud-based IoT environments, Decision Analytics Journal 7 (2023), 100200.","DOI":"10.1016\/j.dajour.2023.100200"},{"key":"10.3233\/JHS-230212_ref27","doi-asserted-by":"crossref","unstructured":"A.\u00a0Reza Moazzeni and E.\u00a0Khamehchi, Rain optimization algorithm (ROA): A new metaheuristic method for drilling optimization solutions, Journal of Petroleum Science and Engineering 195 (2020).","DOI":"10.1016\/j.petrol.2020.107512"},{"issue":"4","key":"10.3233\/JHS-230212_ref28","doi-asserted-by":"crossref","first-page":"2804","DOI":"10.1109\/TCC.2021.3059096","article-title":"OP-MLB: An online VM prediction-based multi-objective load balancing framework for resource management at cloud data center","volume":"10","author":"Saxena","year":"2022","journal-title":"IEEE Transactions on Cloud Computing"},{"key":"10.3233\/JHS-230212_ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.parco.2023.103025"},{"issue":"4","key":"10.3233\/JHS-230212_ref30","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1109\/TCC.2017.2648788","article-title":"STAR: SLA-aware autonomic management of cloud resources","volume":"8","author":"Singh","year":"2020","journal-title":"IEEE Transactions on Cloud Computing"},{"issue":"2","key":"10.3233\/JHS-230212_ref31","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1109\/TNSM.2012.031512.110176","article-title":"A gossip protocol for dynamic resource management in large cloud environments","volume":"9","author":"Wuhib","year":"2012","journal-title":"IEEE Transactions on Network and Service Management"},{"issue":"12","key":"10.3233\/JHS-230212_ref32","doi-asserted-by":"publisher","first-page":"7938","DOI":"10.1109\/TIE.2015.2481792","article-title":"Cooperative resource management cloud-enabled vehicular networks","volume":"62","author":"Yu","year":"2015","journal-title":"IEEE Transactions on industrial electronics"},{"key":"10.3233\/JHS-230212_ref33","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.future.2015.12.011","article-title":"Optimization of virtual resource management for cloud applications to cope with traffic burst","volume":"58","author":"Zhang","year":"2016","journal-title":"Future Generation Computer Systems"}],"container-title":["Journal of High Speed Networks"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JHS-230212","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:44:41Z","timestamp":1777452281000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.medra.org\/servlet\/aliasResolver?alias=iospress&doi=10.3233\/JHS-230212"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,15]]},"references-count":33,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jhs-230212","relation":{},"ISSN":["1875-8940","0926-6801"],"issn-type":[{"value":"1875-8940","type":"electronic"},{"value":"0926-6801","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10,15]]}}}