Various performance benefits such as low latency and high bandwidth have turned fog computing into a well-accepted extension of the cloud computing paradigm. Many fog computing systems have been proposed so far, consisting of distributed compute nodes which are often organized hierarchically in layers. To achieve low latency, these systems commonly rely on the assumption that the nodes of adjacent layers reside close to each other. However, this assumption may not hold in fog computing systems that span over large geographical areas, due to the wide distribution of the nodes.

To avoid relying on this assumption, in this paper we design distributed algorithms whereby the compute nodes measure the network proximity to each other, and self-organize into a hierarchical or a flat structure accordingly. Moreover, we implement these algorithms on geographically distributed compute nodes, and we experiment with image processing and smart city use cases. Our results show that compared to alternative methods, the proposed algorithms decrease the communication latency of latency-sensitive processes by 27%–43%, and increase the available network bandwidth by 36%–86%. Furthermore, we analyze the scalability of our algorithms, and we show that a flat structure (i.e., without layers) scales better than the commonly used layered hierarchy due to generating less overhead when the size of the system grows.

低延迟和高带宽等各种性能优势已使雾计算成为云计算范例的公认扩展。到目前为止，已经提出了许多雾计算系统，它们由通常按层次结构分层组织的分布式计算节点组成。为了实现低延迟，这些系统通常依赖于以下假设：相邻层的节点彼此靠近。然而，由于节点的广泛分布，该假设在跨越较大地理区域的雾计算系统中可能不成立。

为避免依赖此假设，在本文中，我们设计了分布式算法，计算节点可借此测量网络之间的邻近程度，并据此自组织为分层或平面结构。此外，我们在地理上分散的计算节点上实现了这些算法，并尝试了图像处理和智慧城市用例。我们的结果表明，与替代方法相比，所提出的算法将对延迟敏感的进程的通信延迟减少了27％至43％，并将可用网络带宽增加了36％至86％。此外，我们分析了算法的可伸缩性，并且显示出扁平结构（即无层）的伸缩性比常用的分层体系更好，这是因为随着系统规模的增长，生成的开销减少了。