Emerging optical and virtualization technologies enable the design of more flexible and demand-aware networked systems, in which resources can be optimized toward the actual workload they serve. For example, in a demand-aware datacenter network, frequently communicating nodes (e.g., two virtual machines or a pair of racks in a datacenter) can be placed topologically closer, reducing communication costs and hence improving the overall network performance. This paper revisits the bounded-degree network design problem underlying such demand-aware networks. Namely, given a distribution over communicating server pairs, we want to design a network with bounded maximum degree that minimizes expected communication distance. In addition to this known problem, we introduce and study a variant where we allow Steiner nodes (i.e., additional routers) to be added to augment the network. We improve the understanding of this problem domain in several ways. First, we shed light on the complexity and hardness of the aforementioned problems, and study a connection between them and the virtual networking embedding problem. We then provide a constant-factor approximation algorithm for the Steiner node version of the problem, and use it to improve over prior state-of-the-art algorithms for the original version of the problem with sparse communication distributions. Finally, we investigate various heuristic approaches to bounded-degree network design problem, in particular providing a reliable heuristic algorithm with good experimental performance. We report on an extensive empirical evaluation, using several real-world traffic traces from datacenters, and find that our approach results in improved demand-aware network designs.

翻译：新兴的光学与虚拟化技术使得设计更灵活、更需求感知的网络化系统成为可能，此类系统的资源可针对实际负载进行优化。例如，在需求感知数据中心网络中，频繁通信的节点（如两个虚拟机或数据中心的一对机架）可通过拓扑邻近部署，从而降低通信开销，进而提升整体网络性能。本文重新审视了此类需求感知网络所依赖的有界度网络设计问题：给定通信服务器对的分布，需设计一个具有最大度约束的网络，使期望通信距离最小化。除这一已知问题外，我们引入并研究了一种允许添加施泰纳节点（即额外路由器）增强网络的变体。我们通过以下方式深化对该问题领域的理解。首先，揭示了上述问题的复杂性与困难性，并探索了其与虚拟网络嵌入问题之间的关联。随后，针对含施泰纳节点的版本提出了恒定近似比算法，并利用该算法改进稀疏通信分布场景下原始问题的最优算法。最后，我们探索了有界度网络设计问题的多种启发式方法，特别提出了一种实验性能良好的可靠启发式算法。通过使用多个数据中心真实流量轨迹进行广泛实证评估，结果表明我们的方法能够实现更优的需求感知网络设计。