{"title":"An Optimization of Computational Resources Allocation for Multi-MEC and Cloud Networks","authors":"A. Y. Du","doi":"10.1109/ISEC49744.2020.9397859","DOIUrl":null,"url":null,"abstract":"Mobile edge computing (MEC) technology has been studied for several years [1] and is still attracting more attentions. The optimization of MEC with cloud computing targets on improving efficiency of resources. In this work we consider a practical MEC application case in mobile video surveillance and analytics. By investigating some real application systems and using a simple model to analyze an optimization issue, we obtained some numerical results using MATLAB. The results shows that it is possible to improve the system performance in terms of energy saving and efficiency improving by balancing the workload between MEC and the cloud.","PeriodicalId":355861,"journal":{"name":"2020 IEEE Integrated STEM Education Conference (ISEC)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Integrated STEM Education Conference (ISEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISEC49744.2020.9397859","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Mobile edge computing (MEC) technology has been studied for several years [1] and is still attracting more attentions. The optimization of MEC with cloud computing targets on improving efficiency of resources. In this work we consider a practical MEC application case in mobile video surveillance and analytics. By investigating some real application systems and using a simple model to analyze an optimization issue, we obtained some numerical results using MATLAB. The results shows that it is possible to improve the system performance in terms of energy saving and efficiency improving by balancing the workload between MEC and the cloud.
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