{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:27:15Z","timestamp":1780054035244,"version":"3.54.0"},"reference-count":25,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T00:00:00Z","timestamp":1705536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Communication"],"published-print":{"date-parts":[[2024,3,25]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Recently, there has been a growing emphasis on reducingenergy consumption in cloud networks and achieving green computing practices toaddress environmental concerns and optimize resource utilization. In thiscontext, efficient task scheduling minimizes energy usage and enhances overallsystem performance. To tackle the challenge ofenergy\u2010efficient task allocation, we propose a novel approach that harnessesthe power of Artificial Neural Networks (ANN). Our Artificial neural network Dynamic Balancing (ANNDB) method is designed toachieve green computing in cloud environments. ANNDB leverages the feed\u2010forwardnetwork architecture and a multi\u2010layer perceptron, effectively allocatingrequests to higher\u2010power and higher\u2010quality virtual machines, resulting inoptimized energy utilization. Through extensive simulations, wedemonstrate the superiority of ANNDB over existing methods, including WPEG,IRMBBC, and BEMEC, in terms of energy and power efficiency. Specifically, ourproposed ANNDB method exhibits substantial improvements of 13.81%, 8.62%, and9.74% in the Energy criterion compared to WPEG, IRMBBC, and BEMEC,respectively. Additionally, in the Power criterion, the method achievesperformance enhancements of 3.93%, 4.84%, and 4.19% over the mentioned methods.The findings from this research hold significant promise for organizations seekingto optimize their cloud computing environments while reducing energyconsumption and promoting sustainable computing practices. By adopting theANNDB approach for efficient task scheduling, businesses and institutions cancontribute to green computing efforts, reduce operational costs, and make moreenvironmentally friendly choices without compromising task allocationperformance.<\/jats:p>","DOI":"10.1002\/dac.5689","type":"journal-article","created":{"date-parts":[[2024,1,18]],"date-time":"2024-01-18T08:29:45Z","timestamp":1705566585000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Efficient task scheduling in cloud networks using ANN for green computing"],"prefix":"10.1002","volume":"37","author":[{"given":"Hadi","family":"Zavieh","sequence":"first","affiliation":[{"name":"School of Economics and Statistics Guangzhou University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Javadpour","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology (Cyberspace Security) Harbin Institute of Technology  Shenzhen China"},{"name":"ADiT\u2010Lab, Electrotechnics and Telecommunications Department Instituto Polit\u00e9cnico de Viana do Castelo  Porto Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arun Kumar","family":"Sangaiah","sequence":"additional","affiliation":[{"name":"International Graduate Institute of AI National Yunlin University of Science and Technology  Douliu Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,1,18]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"1","volume-title":"Reinforcement Learning Approach to Managing Distributed Data Processing Tasks in Wireless Sensing Networks","author":"Bratman 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