Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data. Moreover, due to time-varying demands, the code and data placement needs to be adjusted over time, which raises concerns of system stability and operation cost. In this paper, we address these issues by proposing a two-time-scale framework that jointly optimizes service (code and data) placement and request scheduling, while considering storage, communication, computation, and budget constraints. First, by analyzing the hardness of various cases, we completely characterize the complexity of our problem. Next, we develop a polynomial-time service placement algorithm by formulating our problem as a set function optimization, which attains a constant-factor approximation under certain conditions. Furthermore, we develop a polynomial-time request scheduling algorithm by computing the maximum flow in a carefully constructed auxiliary graph, which satisfies hard resource constraints and is provably optimal in the special case where requests have homogeneous resource demands. Extensive synthetic and trace-driven simulations show that the proposed algorithms achieve 90% of the optimal performance.

中文翻译：

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边缘云中数据密集型应用程序的服务放置和请求调度

移动边缘计算为无线用户提供了利用云计算功能的机会，而不会造成较大的通信延迟。为了从边缘服务数据密集型应用程序（例如，视频分析，机器学习任务），除了计算资源外，我们还需要用于存储服务器代码和数据的存储资源以及用于接收用户提供的数据的网络带宽。而且，由于时变的要求，代码和数据的放置需要随时间调整，这引起了对系统稳定性和操作成本的关注。在本文中，我们通过提出一个两阶段规模的框架来解决这些问题，该框架可以在考虑存储，通信，计算和预算约束的同时，共同优化服务（代码和数据）的放置和请求调度。首先，通过分析各种情况的难度，我们完全刻画了问题的复杂性。接下来，我们通过将问题公式化为集合函数优化来开发多项式时间服务放置算法，该算法在特定条件下可获得恒定因子近似值。此外，我们通过在精心构造的辅助图中计算最大流量来开发多项式时间请求调度算法，该算法可以满足硬资源约束，并且在请求具有均匀资源需求的特殊情况下可证明是最优的。大量的综合和跟踪驱动的仿真表明，所提出的算法可实现90％的最佳性能。在某些条件下达到常数因子近似值。此外，我们通过在精心构造的辅助图中计算最大流量来开发多项式时间请求调度算法，该算法可以满足硬资源约束，并且在请求具有均匀资源需求的特殊情况下可证明是最优的。大量的综合和跟踪驱动的仿真表明，所提出的算法可实现90％的最佳性能。在某些条件下达到常数因子近似值。此外，我们通过在精心构造的辅助图中计算最大流量来开发多项式时间请求调度算法，该算法可以满足硬资源约束，并且在请求具有均匀资源需求的特殊情况下可证明是最优的。大量的综合和跟踪驱动的仿真表明，所提出的算法可实现90％的最佳性能。