Robust resource provisioning in time-varying edge networks
Robust resource provisioning in time-varying edge networks
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DOI：
10.1145/3397166.3409146
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Ruozhou Yu;G. Xue;Yinxin Wan;Jian Tang;Dejun Yang;Yusheng Ji
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文献类型：
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作者：
Ruozhou Yu;G. Xue;Yinxin Wan;Jian Tang;Dejun Yang;Yusheng Ji
边缘计算是实现高性能和低延迟现代应用的革命性技术之一，如智能城市、联网汽车等。然而，它的采用受到一些因素的限制，包括边缘资源的高成本、异构和波动的需求，以及缺乏可靠性。在本文中，我们研究了边缘计算中的资源配置，考虑了这些不同的因素。首先，基于对真实需求轨迹的观察，我们提出了一个时变随机模型来捕捉边缘网络中时变和不确定的需求和网络动态。然后，我们应用了一个新的鲁棒性模型，该模型同时考虑了服务的预期性能和最坏情况下的性能。在这些模型的基础上，我们将边缘分配问题表述为一个多阶段随机优化问题。即使在确定性的情况下，这个问题也是np困难的。利用多阶段结构，我们应用嵌套的bender分解来解决问题。我们还描述了几种提高效率的技术，包括一种快速解决大量分解子问题的新技术。最后，我们给出了基于真实数据集的仿真结果，证明了所提出的模型、算法和技术的优势。
Edge computing is one of the revolutionary technologies that enable high-performance and low-latency modern applications, such as smart cities, connected vehicles, etc. Yet its adoption has been limited by factors including high cost of edge resources, heterogeneous and fluctuating demands, and lack of reliability. In this paper, we study resource provisioning in edge computing, taking into account these different factors. First, based on observations from real demand traces, we propose a time-varying stochastic model to capture the time-dependent and uncertain demand and network dynamics in an edge network. We then apply a novel robustness model that accounts for both expected and worst-case performance of a service. Based on these models, we formulate edge provisioning as a multi-stage stochastic optimization problem. The problem is NP-hard even in the deterministic case. Leveraging the multi-stage structure, we apply nested Benders decomposition to solve the problem. We also describe several efficiency enhancement techniques, including a novel technique for quickly solving the large number of decomposed subproblems. Finally, we present results from real dataset-based simulations, which demonstrate the advantages of the proposed models, algorithm and techniques.