Joint Multiuser DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
Joint Multiuser DNN Partitioning and Computational Resource Allocation for Collaborative Edge Intelligence
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DOI：
10.1109/jiot.2020.3010258
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发表时间：
2021-06-15
影响因子：
10.6
通讯作者：
Chen, Lin
中科院分区：
文献类型：
--
作者：
Tang, Xin;Chen, Xu;Chen, Lin
移动边缘计算(MEC)已经成为一种很有前途的支持架构，为网络边缘提供各种资源，从而成为边缘智能服务的使能器，为具有人工智能(AI)能力的大规模移动和物联网(IoT)设备提供支持。在边缘服务器的帮助下，用户设备(UE)能够运行基于深度神经网络(DNN)的人工智能应用程序，这些应用程序通常是资源密集型和计算密集型的，以至于单个UE很难实时地负担得起。然而，每个独立边缘服务器中的资源通常是有限的。因此，任何涉及边缘服务器的资源优化本质上都是一个资源受限的优化问题，需要在这样一个现实的背景下解决。受此启发，我们研究了DNN划分(一种新兴的DNN卸载方案)在现实的多用户资源约束条件下的优化问题，这在以前的工作中很少考虑。尽管解空间非常大，但我们揭示了这一特定的联合多UE DNN划分和计算资源分配优化问题的几个性质。提出了一种迭代交替优化算法(IAO)，该算法可以在多项式时间内获得最优解。此外，我们从时间复杂度和实际估计误差下的性能两个方面对该算法进行了严格的理论分析。此外，我们构建了一个原型，实现了我们的框架，并使用真实的DNN模型进行了广泛的实验，结果表明了该框架的有效性和高效性。
Mobile-edge computing (MEC) has emerged as a promising supporting architecture providing a variety of resources to the network edge, thus acting as an enabler for edge intelligence services empowering massive mobile and Internet-of-Things (IoT) devices with artificial intelligence (AI) capability. With the assistance of edge servers, user equipments (UEs) are able to run deep neural network (DNN)-based AI applications, which are generally resource hungry and computation intensive such that an individual UE can hardly afford by itself in real time. However, the resources in each individual edge server are typically limited. Therefore, any resource optimization involving edge servers is by nature a resource-constrained optimization problem and needs to be tackled in such a realistic context. Motivated by this observation, we investigate the optimization problem of DNN partitioning (an emerging DNN offloading scheme) in a realistic multiuser resource-constrained condition that rarely considered in previous works. Despite the extremely large solution space, we reveal several properties of this specific optimization problem of joint multi-UE DNN partitioning and computational resource allocation. We propose an algorithm called iterative alternating optimization (IAO) that can achieve the optimal solution in polynomial time. In addition, we present a rigorous theoretic analysis of our algorithm in terms of time complexity and performance under realistic estimation error. Moreover, we build a prototype that implements our framework and conducts extensive experiments using realistic DNN models, whose results demonstrate its effectiveness and efficiency.