最后期限约束下移动边缘计算的节能部分卸载

Jinxin Zhao, Longxin Deng, Yong Liu, J. Sun
{"title":"最后期限约束下移动边缘计算的节能部分卸载","authors":"Jinxin Zhao, Longxin Deng, Yong Liu, J. Sun","doi":"10.1109/ICITES53477.2021.9637065","DOIUrl":null,"url":null,"abstract":"Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.","PeriodicalId":370828,"journal":{"name":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Energy-Efficient Partial Offloading in Mobile Edge Computing Under a Deadline Constraint\",\"authors\":\"Jinxin Zhao, Longxin Deng, Yong Liu, J. Sun\",\"doi\":\"10.1109/ICITES53477.2021.9637065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.\",\"PeriodicalId\":370828,\"journal\":{\"name\":\"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)\",\"volume\":\"35 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICITES53477.2021.9637065\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Intelligent Technology and Embedded Systems (ICITES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICITES53477.2021.9637065","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

任务卸载和调度是移动边缘计算(MEC)中的重要问题。一个好的卸载决策可以充分利用边缘服务器的计算能力来提供高质量的计算服务。本文考虑到任务处理延迟和移动设备能耗的问题,将任务卸载问题表述为限期约束下的能量最小化整数规划。提出了一种基于鲸鱼优化算法的部分卸载和调度方法来解决公式化的优化问题。该方法采用基于概率模型的映射算子,将单个鲸鱼转换为由任务序列表示的有效卸载解。在生成高质量的任务序列方面,这种映射方案比基于排序的规则更有优势。我们开发了一种有效的启发式策略来决定每个任务应该在移动设备上本地处理或卸载到边缘服务器执行。在对序列中的所有任务进行调度后,可以计算出移动设备执行任务和传输数据所消耗的能量。因此,我们可以在种群中确定最佳个体鲸鱼,从而以最低的能量消耗找到解决方案。我们迭代地应用种群更新规则来探索更好的任务序列和卸载方案。我们进行了大量的实验来验证,与基线算法相比,所提出的算法在满足最后期限约束的同时实现了更节能的卸载解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy-Efficient Partial Offloading in Mobile Edge Computing Under a Deadline Constraint
Task offloading and scheduling is an important issue in mobile edge computing (MEC). A good offloading decision can fully utilize the computing capabilities of edge servers to deliver high-quality computing services. This paper takes into account the concerns about task processing latency and mobile device's energy consumption, and formulates the task offloading problem as a deadline-constrained energy-minimization integer program. We propose a partial offloading and scheduling method based on the whale optimization algorithm to solve the formulated optimization problem. This method employs a probability model-based mapping operator to convert an individual whale into a valid offloading solution represented by a task sequence. This mapping scheme is advantageous over sorting-based rules in producing high-quality task sequence. We develop an efficient heuristic strategy to decide each task should be processed locally on the mobile device or offloaded to the edge server for execution. With all tasks in the sequence having been scheduled, the mobile device's energy consumption for task execution and data transmission can be calculated. Accordingly, we can identify the best individual whale in the population leading to the solution with lowest energy consumption. We iteratively apply the population updating rule to explore better task sequence and offloading solution. We perform extensive experiments to verify that the proposed algorithm achieves more energy-efficient offloading solutions as compared to baseline algorithms while satisfying the deadline constraint.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信