ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2020, Vol. 57 ›› Issue (9): 1823-1838.doi: 10.7544/issn1000-1239.2020.20200184

所属专题: 2020边缘计算专题

• 网络技术 • 上一篇    下一篇

绿色能源驱动的移动边缘计算动态任务卸载

马惠荣,陈旭,周知,于帅   

  1. (中山大学数据科学与计算机学院 广州 510006) (mahr3@mail2.sysu.edu.cn)
  • 出版日期: 2020-09-01
  • 基金资助: 
    国家自然科学基金项目(61972432,U1711265)

Dynamic Task Offloading for Mobile Edge Computing with Green Energy

Ma Huirong, Chen Xu, Zhou Zhi, Yu Shuai   

  1. (School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006)
  • Online: 2020-09-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (61972432, U1711265).

摘要: 移动边缘计算(mobile edge computing, MEC)是近年来出现的一种崭新技术,它能满足更多应用程序所需的计算资源,能使移动网络边缘资源受限的物联网(IoT)设备获得更好的性能.然而,众所周知,边缘基础设施在提高电力使用效率和整合可再生能源方面的能力较差.此外,由于物联网设备的电池容量是有限的,当电池电量耗尽时,所执行任务会被中断.因此,利用绿色能源来延长电池的使用寿命是至关重要的.此外,物联网设备间可以动态、有益地共享计算资源和通信资源.因此,为了提高边缘服务器的能效(power usage efficiency, PUE),实现绿色计算,设计了一种高效的任务卸载策略,提出了一种利用能量收集(energy harvesting, EH)技术和设备间通信(device-to-device communication, D2D)技术的绿色任务卸载框架.该框架旨在最小化任务执行所造成的边缘服务器端电网电力能源成本及云服务器端云资源租用成本.与此同时,引入激励约束,能够有效促进IoT设备间的协作,并防止IoT设备资源被其他设备过度使用.考虑到系统未来信息的不确定性,例如绿色能源的可获得性,提出了一种基于李雅普诺夫优化技术的在线任务卸载算法,该算法仅依赖于系统的当前状态信息.该算法的实现只需要在每个时间片内求解一个确定性问题,其核心思想是将每个时间片的任务卸载问题转化为图匹配问题,并通过调用爱德蒙带花树算法求得近似最优解.对所提出算法的性能进行了严格的理论分析,并通过实验验证了所提出框架的优越性能.

关键词: 移动边缘计算, 任务卸载, 能量收集, 设备间协作, 激励约束

Abstract: Mobile edge computing (MEC) has recently emerged to fulfill the computation demands of richer applications, and provide better experience for resource-hungry Internet-of-Things (IoT) devices at the edge of mobile networks. It is readily acknowledged that edge infrastructures are less capable of improving power usage efficiency (PUE) and integrating renewable energy. Besides, due to the limited battery capacities of IoT devices, the task execution would be interrupted when the battery runs out. Therefore, it is crucial to use green energy to prolong the battery life-time. Moreover, IoT devices can share computation and communication resources dynamically and beneficially among each other. Therefore, we develop an efficient task offloading strategy in order to improve PUE of edge server as well as achieving green computing. We also propose a green task offloading framework which leverages energy harvesting (EH) and device-to-device communication (D2D). Our framework aims at minimizing the long-term grid power energy consumption of edge server and cloud resource rental costs for task executions of all EH IoT devices. Meanwhile, the incentive constraints of preventing the over-exploiting behaviors should be considered, since they harm devices’ motivation for collaboration. To address the uncertain future system information, such as the availability of renewable energy, we resort to Lyapunov optimization technique to propose an online task offloading algorithm, in which the decisions only depend on system current state information. The implementation of this algorithm only requires to solve a deterministic problem in each time slot, for which the core idea is to transform the task offloading problem of each time slot into a graph matching problem and get the approximate optimal solution by calling Edmonds’s Blossom algorithm. Rigorous theoretical analysis and extensive evaluations demonstrate the superior performance of the proposed scheme.

Key words: mobile edge computing (MEC), task offloading, energy harvesting, D2D collaboration, incentive-awareness

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