Seek Common While Shelving Differences: Orchestrating Deep Neural Networks for Edge Service Provisioning
Seek Common While Shelving Differences: Orchestrating Deep Neural Networks for Edge Service Provisioning
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DOI：
10.1109/jsac.2020.3036953
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发表时间：
2021-01
影响因子：
16.4
通讯作者：
Lixing Chen;Jie Xu
中科院分区：
文献类型：
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作者：
Lixing Chen;Jie Xu
Edge Computing（EC）平台使应用程序服务提供商（ASP）与用户密切相关的应用程序，以提供超低的潜伏期和位置意识，因为货币成本是在Edge Servers上租用的，以便将服务提供范围，以便在范围内提供额外的效力，以供纳入货币。与EC系统中的多种因素相关性，从用户行为到计算，这些因素很难被数学建模完全捕获，并且由于诱导高维状态空间的最新成功（DL）的新工具是在我们的详细信息中，因此在我们的问题上启用了dl dl dl dl dl dl dl。增强学习（DRL）和多代理DL，在EC系统中，这些技术不能仅处理EC系统的分布和异质性，我们提出了一个基于多代理DRL的新型框架，分布式的神经元网络策划（N2O），并从中启用了互联网。到达EC系统的异质性。替代方案。
Edge computing (EC) platforms, which enable Application Service Providers (ASPs) to deploy applications in close proximity to users, are providing ultra-low latency and location-awareness to a rich portfolio of services. As monetary costs are incurred for renting computing resources on edge servers to enable service provisioning, ASP has to cautiously decide where to deploy the application and how much resources would be needed to deliver satisfactory performance. However, the service provisioning problem exhibits complex correlations with multifarious factors in EC systems, ranging from user behavior to computation offloading, which are difficult to be fully captured by mathematical modeling and also put off traditional machine learning techniques due to the induction of high-dimension state space. The recent success of deep learning (DL) underpins new tools for addressing our problem. While previous works provide valuable insights on applying DL techniques, e.g., distributed DL, deep reinforcement learning (DRL), and multi-agent DL, in EC systems, these techniques cannot solely handle the distributed and heterogeneous nature of EC systems. To address these limitations, we propose a novel framework based on multi-agent DRL, distributed neural network orchestration (N2O), and knowledge distilling. The multi-agent DRL enables edge servers to learn deep neural networks that shelve distinct features learned from local edge sites and hence caters to the heterogeneity of EC systems. N2O coordinates edge servers in a fully distributed manner toward a common goal of maximizing ASP’s reward. It requires only local communications during execution and provides provable performance guarantees. The knowledge distilling is further utilized to distill the N2O policy for reducing the communication overhead and stabilizing the decision-making. We also carry out systematic experiments to show the advantages of our method over state-of-the-art alternatives.