| Abstract |
Edge computing is emerging as a promising computing paradigm for supporting next-generation applications that rely on low-latency network connections in the Internet-ofThings (IoT) era. Many edge applications, such as multi-player augmented reality (AR) gaming and federated machine learning, require that distributed clients work collaboratively for a common goal through message exchanges. Given an edge network, it is an open problem how to deploy such collaborative edge applications to achieve the best overall system performance. This paper presents a formal study of this problem. We first provide a mix of cost models to capture the system. Based on a thorough formulation, we propose an iterative algorithm dubbed ITEM, where in each iteration, we construct a graph to encode all the costs and convert the cost optimization problem into a graph cut problem. By obtaining the minimum s − t cut via existing max-flow algorithms, we address the original problem via solving a series of graph cuts. We rigorously prove that ITEM has a parameterized constant approximation ratio. Inspired by the optimal stopping theory, we further design an online algorithm called OPTS, based on optimally alternating between partial and full placement updates. Our evaluations with real-world data traces demonstrate that ITEM performs close to the optimum (within 5%) and converges fast. OPTS achieves a bounded performance as expect |