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While prior work has explored many proposed datacenter designs, only two designs, Clos-based and expander-based, are generally considered practical because they can scale using commodity switching chips. Prior work has used two diferent metrics, bisection band- width and throughput, for evaluating these topologies at scale. Little is known, theoretically or practically, how these metrics relate to each other. Exploiting characteristics of these topologies, we prove an upper bound on their throughput, then show that this upper bound better estimates worst-case throughput than all previously proposed throughput estimators and scales better than most of them. Using this upper bound, we show that for expander-based topologies, unlike Clos, beyond a certain size of the network, no topology can have full throughput, even if it has full bisection band- width; in fact, even relatively small expander-based topologies fail to achieve full throughput. We conclude by showing that using throughput to evaluate datacenter performance instead of bisection bandwidth can alter conclusions in prior work about datacenter cost, manageability, and reliability. 

Most recent datacenter topology designs have focused on performance properties such as latency and throughput. In this paper, we explore a new dimension, life cycle management complexity, which attempts to understand the complexity of deploying a topology and expanding it. By analyzing current practice in lifecycle management, we devise complexity metrics for lifecycle management, and show that existing topology classes have low lifecycle management complexity by some measures, but not by others. Motivated by this, we design a new class of topologies, FatClique, that, while being performance-equivalent to existing topologies, is comparable to, or better than them by all our lifecycle management complexity metrics.