Distributed Interactive Applications (DIAs) such as virtual reality and multiplayer online game usually require fast processing of tremendous data and timely exchange of delay-sensitive action data and metadata. This makes traditional mobile-based or cloud-based solutions no longer effective. Thanks to edge computing, DIA Service Providers (DSPs) can rent resources from Edge Infrastructure Providers (EIPs) to place service entities that store user states and run computation-intensive tasks. One fundamental problem for a DSP is to decide where to place service entities to achieve low-delay pairwise interactions between DIA users, under the constraint that the total placement cost is no more than a specified budget threshold. In this paper, we formally model the service entity placement problem and prove that it is NP-complete by a polynomial reduction from the set cover problem. We present GPA, an efficient algorithm for service entity placement, and theoretically analyze its performance. We evaluated GPA with both real-world data trace-driven simulations, and observed that GPA performs close to the optimal algorithm and generally outperforms the baseline algorithm. We also output a curve showing the trade-off between the weighted average interaction delay and the budget threshold, so that a DSP can choose the right balance.

中文翻译：

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边缘计算中面向交互的服务实体放置

虚拟现实和多人在线游戏等分布式交互应用程序 (DIA) 通常需要快速处理大量数据并及时交换延迟敏感的动作数据和元数据。这使得传统的基于移动或基于云的解决方案不再有效。由于边缘计算，DIA 服务提供商 (DSP) 可以从边缘基础设施提供商 (EIP) 租用资源来放置存储用户状态和运行计算密集型任务的服务实体。DSP 的一个基本问题是在总放置成本不超过指定预算阈值的约束下，决定将服务实体放置在何处以实现 DIA 用户之间的低延迟成对交互。在本文中，我们对服务实体放置问题进行了形式化建模，并通过对集合覆盖问题的多项式归约来证明它是 NP 完全的。我们提出了一种有效的服务实体放置算法 GPA，并对其性能进行了理论上的分析。我们使用真实数据跟踪驱动的模拟评估了 GPA，并观察到 ​​GPA 的性能接近最佳算法，并且通常优于基线算法。我们还输出了一条曲线，显示加权平均交互延迟和预算阈值之间的权衡，以便 DSP 可以选择正确的平衡。并观察到 ​​GPA 的性能接近最佳算法，并且通常优于基线算法。我们还输出了一条曲线，显示加权平均交互延迟和预算阈值之间的权衡，以便 DSP 可以选择正确的平衡。并观察到 ​​GPA 的性能接近最佳算法，并且通常优于基线算法。我们还输出了一条曲线，显示加权平均交互延迟和预算阈值之间的权衡，以便 DSP 可以选择正确的平衡。