Optical Self-Adjusting Data Center Networks in the Scalable Matching Model

Abstract

Self-Adjusting Networks (SAN) optimize their physical topology toward the demand in an online manner. Their application in data center networks is motivated by emerging hardware technologies, such as 3D MEMS Optical Circuit Switches (OCS). The Matching Model (MM) has been introduced to study the hybrid architecture of such networks. It abstracts from the electrical switches and focuses on the added (reconfigurable) optical ones. MM defines any SAN topology as a union of matchings over a set of top-of-rack (ToR) nodes, and assumes that rearranging the edges of a single matching comes at a fixed cost. In this work, we propose and study the Scalable Matching Model (SMM), a generalization of the MM, and present OpticNet, a framework that maps a set of ToRs to a set of OCSs to form a SAN topology. We prove that OpticNet uses the minimum number of switches to realize any bounded-degree topology and allows existing SAN algorithms to run on top of it, while preserving amortized performance guarantees. Our experimental results based on real workloads show that OpticNet is a flexible and efficient framework for the implementation and evaluation of SAN algorithms in reconfigurable data center environments.