Talbot: Improving Throughput for Traffic Dynamics in Reconfigurable Datacenters

Jingzhou Wang, Yu-E Sun, He Huang, Yang Du

Abstract: Current web applications like social networks and video streaming have been generating magnificent traffic volume, along with intensive traffic dynamics, raising challenges to the fundamental infrastructure of web, i.e., datacenters. However, the traditional electric-based architectures are behind the curve due to the fixed topology and the demand-oblivious nature, failing to guarantee the performance of web applications. Motivated by the new traffic pattern, the reconfigurable technologies, like optical circuit switches (OCSes), are a promising choice to further improve throughput by dynamically adjusting connections when facing traffic dynamics. Currently, static and dynamic updating are two main methods for reconfiguring OCSes. Though static methods can acquire a solution with approximation ratio, it takes long running time and recourse. As comparison, dynamic updating can efficiently acquire a solution, yet existing solutions may degrade along with updates. This paper presents Talbot to further improve throughput with both approximation guarantee and low updating time/recourse. We formulate the throughput maximization problem as a fully dynamic k-weight limited matching problem which is $\mathcal{N} \mathcal{P}$-hard, and we further propose an approximation algorithm based on level and lazy update scheme. To evaluate Talbot, simulations are conducted with both real-world and synthetic datasets. Compared with state-of-the-art works, we show the superior performance of Talbot.

External IDs:dblp:conf/iwqos/WangSHD25