Comprehensive Performance and Robustness Analysis of Expander-Based Data Centers
Mohamad Al Adraa, Chadi M. Assi, Mohammed Almekhlafi, Maurice Khabbaz, Vladimir Pelekhaty, Michael Y. Frankel · IEEE Transactions on Network and Service Management · 2023
Data center networks have been gaining a lot of attention in recent years. These networks are scaling up quickly with the explosive nature of current applications. Lately, a lot of efforts have been exerted to improve the performance of these networks compared to the often performance-lagging standard Clos-based topologies. One of the approaches for performance improvement is to use alternative data center network topologies. Consequently, researchers explored topologies based on Expander Graphs (EGs), such as Jellyfish, Xpander, and STRAT, where they exploited the sparse and incremental nature of these new topologies. This paper investigates the STructured Re-Arranged Topology (STRAT) as a potentially robust and efficient design for next-generation data centers. Robustness and throughput metrics are adopted to benchmark the performance of STRAT against the well-known Expander architectures, which show better performance than that of present topologies, (e.g., Fat-Tree, BCube). This paper shows that STRAT has better structural properties than Jellyfish and Xpander, making it more robust to switch and link failures. Specifically, STRAT possesses lower average shortest path length and diameter, higher spectral gap, and higher algebraic connectivity. These exceptional properties allow STRAT to achieve better throughput. Such observations are validated through extensive flow and packet level simulations, demonstrating STRAT’s superior performance in terms of the flow completion time as compared to other Expanders.