{"title":"基于帕累托的高效方法,用于在雾云环境中卸载物联网任务","authors":"","doi":"10.1016/j.iot.2024.101311","DOIUrl":null,"url":null,"abstract":"<div><p>In recent times, a new paradigm has emerged in the field of Cloud computing, namely Fog computing. This paradigm has proven to be highly useful in a wide range of domains where both delay and cost were important metrics. Notably, the Internet of Things (IoT) strongly benefits from this, as small devices can gain access to strong computation power quickly and at a low cost. To achieve this, task offloading is used to decide which task should be executed on which node. The development of an efficient algorithm to address this problem could significantly enhance the sustainability of systems in various industrial, agricultural, autonomous vehicle, and other domains. This paper proposes a new variant of the Niche Pareto Genetic Algorithm (NPGA) called Local search Drafting-NPGA (LD-NPGA) to optimize resource allocation in a Cloud/Fog environment, with the objective of minimizing makespan and cost simultaneously. It generates Pareto solutions allowing the user to make choices closer to its intentions. Thus, it addresses various shortcomings identified in the state of the art, including scalability and aggregation formula. A drafting step is implemented to maintain diversity in the population of solutions, resulting in a more varied Pareto set than basic NPGA. LD-NPGA significantly outperforms state-of-the-art metaheuristics in makespan and cost by 15%. Finally, the scalability of our approach and the variety of solutions generated are confirmed in the different experiments.</p></div>","PeriodicalId":29968,"journal":{"name":"Internet of Things","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Efficient Pareto based approach for IoT task offloading on Fog–Cloud environments\",\"authors\":\"\",\"doi\":\"10.1016/j.iot.2024.101311\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In recent times, a new paradigm has emerged in the field of Cloud computing, namely Fog computing. This paradigm has proven to be highly useful in a wide range of domains where both delay and cost were important metrics. Notably, the Internet of Things (IoT) strongly benefits from this, as small devices can gain access to strong computation power quickly and at a low cost. To achieve this, task offloading is used to decide which task should be executed on which node. The development of an efficient algorithm to address this problem could significantly enhance the sustainability of systems in various industrial, agricultural, autonomous vehicle, and other domains. This paper proposes a new variant of the Niche Pareto Genetic Algorithm (NPGA) called Local search Drafting-NPGA (LD-NPGA) to optimize resource allocation in a Cloud/Fog environment, with the objective of minimizing makespan and cost simultaneously. It generates Pareto solutions allowing the user to make choices closer to its intentions. Thus, it addresses various shortcomings identified in the state of the art, including scalability and aggregation formula. A drafting step is implemented to maintain diversity in the population of solutions, resulting in a more varied Pareto set than basic NPGA. LD-NPGA significantly outperforms state-of-the-art metaheuristics in makespan and cost by 15%. Finally, the scalability of our approach and the variety of solutions generated are confirmed in the different experiments.</p></div>\",\"PeriodicalId\":29968,\"journal\":{\"name\":\"Internet of Things\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Internet of Things\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S254266052400252X\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet of Things","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S254266052400252X","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Efficient Pareto based approach for IoT task offloading on Fog–Cloud environments
In recent times, a new paradigm has emerged in the field of Cloud computing, namely Fog computing. This paradigm has proven to be highly useful in a wide range of domains where both delay and cost were important metrics. Notably, the Internet of Things (IoT) strongly benefits from this, as small devices can gain access to strong computation power quickly and at a low cost. To achieve this, task offloading is used to decide which task should be executed on which node. The development of an efficient algorithm to address this problem could significantly enhance the sustainability of systems in various industrial, agricultural, autonomous vehicle, and other domains. This paper proposes a new variant of the Niche Pareto Genetic Algorithm (NPGA) called Local search Drafting-NPGA (LD-NPGA) to optimize resource allocation in a Cloud/Fog environment, with the objective of minimizing makespan and cost simultaneously. It generates Pareto solutions allowing the user to make choices closer to its intentions. Thus, it addresses various shortcomings identified in the state of the art, including scalability and aggregation formula. A drafting step is implemented to maintain diversity in the population of solutions, resulting in a more varied Pareto set than basic NPGA. LD-NPGA significantly outperforms state-of-the-art metaheuristics in makespan and cost by 15%. Finally, the scalability of our approach and the variety of solutions generated are confirmed in the different experiments.
期刊介绍:
Internet of Things; Engineering Cyber Physical Human Systems is a comprehensive journal encouraging cross collaboration between researchers, engineers and practitioners in the field of IoT & Cyber Physical Human Systems. The journal offers a unique platform to exchange scientific information on the entire breadth of technology, science, and societal applications of the IoT.
The journal will place a high priority on timely publication, and provide a home for high quality.
Furthermore, IOT is interested in publishing topical Special Issues on any aspect of IOT.