D. Gupta, Aditi Moudgil, Shivani Wadhwa, Vikas Solanki
{"title":"边缘网络的高效数据缓存和计算卸载策略","authors":"D. Gupta, Aditi Moudgil, Shivani Wadhwa, Vikas Solanki","doi":"10.1109/ESCI53509.2022.9758379","DOIUrl":null,"url":null,"abstract":"We live in a world where huge end devices execute computing on a daily basis. With the growing number of sophisticated apps (e.g., augmented reality and face recognition) that require considerably more computational capacity, they are shifting to mobile cloud computing (MCC), or offloading computation to the cloud. Unfortunately, because the cloud is typically located far away from end devices, latency and quality of experience (QoE) for delay-sensitive applications suffer. Mobile edge computing (MEC) is considered to be a viable solution for meeting the requirement for low latency. Prior works on edge computing mostly focused on computation offloading to support low latency. This paper Jointly considered data caching and computation offloading to support better QoE for end device users. With caching of completed tasks data and offloading of computations at edge cloud using an efficient approach termed as data caching and computation offloading at edge (DCCO-E), the simulation results proved outstanding performance of the DCCO-E against other schemes in terms of low energy consumption and reduced latency.","PeriodicalId":436539,"journal":{"name":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Efficient Data Caching and Computation Offloading Strategy for Edge Network\",\"authors\":\"D. Gupta, Aditi Moudgil, Shivani Wadhwa, Vikas Solanki\",\"doi\":\"10.1109/ESCI53509.2022.9758379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We live in a world where huge end devices execute computing on a daily basis. With the growing number of sophisticated apps (e.g., augmented reality and face recognition) that require considerably more computational capacity, they are shifting to mobile cloud computing (MCC), or offloading computation to the cloud. Unfortunately, because the cloud is typically located far away from end devices, latency and quality of experience (QoE) for delay-sensitive applications suffer. Mobile edge computing (MEC) is considered to be a viable solution for meeting the requirement for low latency. Prior works on edge computing mostly focused on computation offloading to support low latency. This paper Jointly considered data caching and computation offloading to support better QoE for end device users. With caching of completed tasks data and offloading of computations at edge cloud using an efficient approach termed as data caching and computation offloading at edge (DCCO-E), the simulation results proved outstanding performance of the DCCO-E against other schemes in terms of low energy consumption and reduced latency.\",\"PeriodicalId\":436539,\"journal\":{\"name\":\"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-03-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ESCI53509.2022.9758379\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Emerging Smart Computing and Informatics (ESCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESCI53509.2022.9758379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient Data Caching and Computation Offloading Strategy for Edge Network
We live in a world where huge end devices execute computing on a daily basis. With the growing number of sophisticated apps (e.g., augmented reality and face recognition) that require considerably more computational capacity, they are shifting to mobile cloud computing (MCC), or offloading computation to the cloud. Unfortunately, because the cloud is typically located far away from end devices, latency and quality of experience (QoE) for delay-sensitive applications suffer. Mobile edge computing (MEC) is considered to be a viable solution for meeting the requirement for low latency. Prior works on edge computing mostly focused on computation offloading to support low latency. This paper Jointly considered data caching and computation offloading to support better QoE for end device users. With caching of completed tasks data and offloading of computations at edge cloud using an efficient approach termed as data caching and computation offloading at edge (DCCO-E), the simulation results proved outstanding performance of the DCCO-E against other schemes in terms of low energy consumption and reduced latency.