[论文解读] Multiple Access Computational Offloading: Communication Resource Allocation in the Two-User Case (Extended Version)

Mahsa Salmani, Timothy N. Davidson|arXiv (Cornell University)|May 14, 2018

IoT and Edge/Fog Computing参考文献 21被引用 12

一句话总结

本文提出了一种面向双用户移动边缘计算系统的最优通信资源分配框架，结合计算卸载，分析了在各种多址接入方案下的能量最小化问题。研究推导出不可分割任务和无限可分割任务的闭式解与准闭式解，结果表明，当信道增益不对称且时延约束严格时，充分利用多址接入信道的系统相比TDMA可显著降低能耗。

ABSTRACT

By offering shared computational facilities to which mobile devices can offload their computational tasks, the mobile edge computing framework is expanding the scope of applications that can be provided on resource-constrained devices. When multiple devices seek to use such a facility simultaneously, both the available computational resources and the available communication resources need to be appropriately allocated. In this manuscript, we seek insight into the impact of the choice of the multiple access scheme by developing solutions to the mobile energy minimization problem in the two-user case with plentiful shared computational resources. In that setting, the allocation of communication resources is constrained by the latency constraints of the applications, the computational capabilities and the transmission power constraints of the devices, and the achievable rate region of the chosen multiple access scheme. For both indivisible tasks and the limiting case of tasks that can be infinitesimally partitioned, we provide a closed-form and quasi-closed-form solution, respectively, for systems that can exploit the full capabilities of the multiple access channel, and for systems based on time-division multiple access (TDMA). For indivisible tasks, we also provide quasi-closed-form solutions for systems that employ sequential decoding without time sharing or independent decoding. Analyses of our results show that when the channel gains are equal and the transmission power budgets are larger than a threshold, TDMA (and the suboptimal multiple access schemes that we have considered) can achieve an optimal solution. However, when the channel gains of each user are significantly different and the latency constraints are tight, systems that take advantage of the full capabilities of the multiple access channel can substantially reduce the energy required to offload.

研究动机与目标

• 开发一种集中式资源分配策略，以最小化多用户计算卸载中移动设备的能量消耗。
• 分析多址接入方案（尤其是全信道能力利用与TDMA）对卸载中能量效率的影响。
• 推导在不可分割任务与可分割任务下能量最小化问题的闭式解与准闭式解。
• 评估在非对称信道条件下，先进多址接入技术相较于传统方案（如TDMA）的性能增益。
• 通过刻画时延、功率与信道利用率之间的权衡，为实际卸载系统提供设计洞见。

提出的方法

• 为两个移动用户向共享边缘服务器卸载计算任务，构建联合通信与计算资源分配问题。
• 针对不可分割任务，推导出在全信道能力利用与TDMA方案下的能量最小化闭式解。
• 采用坐标下降算法求解可分割任务及混合任务类型下的准闭式问题。
• 引入时延需求、发射功率限制以及不同多址接入方案的可实现速率区域约束。
• 通过顺序解调与独立解调模型，分析次优方案与最优全能力系统的性能对比。
• 利用最优解中活跃时延约束的等式约束，降低优化问题的维度。

实验结果

研究问题

• RQ1 多址接入方案的选择如何影响双用户系统中移动计算卸载的能量效率？
• RQ2 在何种信道与功率条件下，TDMA相较于全能力多址接入可实现最优能量性能？
• RQ3 在不可分割与可分割任务存在的情况下，能量最小化资源分配的闭式或准闭式解是什么？
• RQ4 非对称信道增益与严格时延约束如何影响先进多址接入与传统方案之间的性能差距？
• RQ5 能否利用活跃时延约束的等式约束来降低优化问题的维度？

主要发现

• 当信道增益相等且发射功率超过阈值时，TDMA可实现最优能量性能。
• 在信道增益非对称且时延约束严格的情况下，利用多址接入信道全能力的系统相比TDMA可显著降低能量消耗。
• 对于不可分割任务，推导出全能力与TDMA方案的闭式解，而顺序解调与独立解调方案则获得准闭式解。
• 通过利用活跃时延约束的等式约束，混合任务类型（不可分割与可分割）的优化问题维度得以降低。
• 降维后问题的目标准则函数在每个变量上保持准凸性，支持通过坐标下降算法实现收敛。
• 所推导的解表明，在非对称信道环境中，先进多址接入技术可显著降低移动设备的能量消耗。