[1]胡 恒,金凤林 *,谢 钧,等.设备间任务依赖的最佳卸载决策和资源分配[J].计算机技术与发展,2022,32(08):82-88.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 014]
　HU Heng,JIN Feng-lin*,XIE Jun,et al.Optimal Offloading Decision and Resource Allocation of Inter-devices Task Dependency[J].,2022,32(08):82-88.[doi:10. 3969 / j. issn. 1673-629X. 2022. 08. 014]
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设备间任务依赖的最佳卸载决策和资源分配()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年08期
页码:
82-88
栏目:
系统工程
出版日期:
2022-08-10

文章信息/Info

Title:
Optimal Offloading Decision and Resource Allocation of Inter-devices Task Dependency
文章编号:
1673-629X(2022)08-0082-07
作者:
胡 恒1 ; 金凤林1 * ; 谢 钧1 ; 俞 璐2 ; 黄科瑾3 ; 孟繁伦4 ; 杨 涛1
1. 陆军工程大学 指挥控制工程学院,江苏 南京 210007;
2. 陆军工程大学 通信工程学院,江苏 南京 210007;
3. 31121 部队,江苏 南京 210018;
4. 61096 部队,江苏 南京 210007
Author(s):
HU Heng1 ; JIN Feng-lin1* ; XIE Jun1 ; YU Lu2 ; HUANG Ke-jin3 ; MENG Fan-lun4 ; YANG Tao1
1. School of Command & Control Engineering,Army Engineering University of PLA,Nanjing 210007,China;
2. School of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China;
3. 31121 Troops,Nanjing 210018,China;
4. 61096 Troops,Nanjing 210007,China
关键词:
移动边缘计算; D2D 技术; 计算卸载技术; 卸载决策; 资源分配
Keywords:
mobile edge computing; D2D technology; computation offloading technology; offloading decision-making; resource alloca鄄tion
分类号:
TP393
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 08. 014
摘要:
对于不同设备之间具有任务依赖性的问题,考虑了两个设备的移动边缘计算( Mobile Edge Computing,MEC) 与端对端(Device-to-Device,D2D) 技术协作网络,其中一个无线设备的最终输出作为另一个设备上某个任务的输入。 在此任务依赖模型下,为了最小化无线设备的能耗和任务完成时间的加权和,研究了最佳的资源分配( 卸载发射功率和本地 CPU频率)和任务卸载决策问题。 为了解决该问题,将原问题分解为给定任务卸载决策的资源分配问题和优化与资源分配问题相对应的任务卸载问题。 首先给定卸载决策,推导出卸载发射功率和本地 CPU 频率的闭合表达式,运用凸优化方法求出该问题的解。 然后证明最优卸载决策遵循一次爬升策略,在此基础上提出了一种降低复杂度的在线任务卸载算法,该算法可以在多项式时间内获得最优卸载决策。 数值结果表明,该策略的性能明显优于其他有代表性的基准测试,同时MEC 与 D2D 协作可以显着提高系统的性能。
Abstract:
For the problem of task dependence between different devices,the mobile edge computing ( MEC) and device - to - device( D2D) technology cooperation network of two devices are considered. The final output of one wireless device is the input of a task onanother device. In this task-dependent model,in order to minimize the weighted sum of energy consumption of wireless devices and taskcompletion time,the optimal resource allocation ( offloading transmitting power and local CPU frequency) and task offloading decision-making are studied. In order? to solve this problem,the original problem is decomposed into a resource allocation problem for a given taskoffloading decision and a task offloading problem corresponding to the optimization and resource allocation problem. Firstly,given the offloading decision,the closed- form expressions of the offloading transmission power and the local CPU frequency are derived,and theconvex optimization method is used to solve the problem. Then it is proved that the optimal offloading decision follows a climbingstrategy,and on this basis, an online task offloading algorithm with reduced complexity is proposed, which can obtain the optimaloffloading decision in polynomial time. The numerical results show that the performance of this strategy is significantly better than that ofother representative benchmark tests,and the collaboration between MEC and D2D can significantly improve the performance of the system.

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更新日期/Last Update: 2022-08-10