LAUSR

The performance of many data-centric cloud applications critically depends on the performance of the underlying datacenter network. Reconfigurable optical technologies have recently introduced a novel opportunity to improve datacenter network performance, by allowing to dynamically adjust the network topology according to the demand. However, the vision of self-adjusting networks raises the fundamental question how such networks can be efficiently operated in a scalable and distributed manner. This article presents $DiSplayNet$DiSplayNet, the first fully distributed self-adjusting network. $DiSplayNet$DiSplayNet relies on algorithms that perform decentralized and concurrent topological adjustments to account for changes in the demand. We propose two natural metrics to evaluate the performance of distributed self-adjusting networks, the amortized work (the cost of routing on and adjusting the network) and the makespan (the time it takes to serve a set of communication requests). We present a rigorous formal analysis of the work and makespan of $DiSplayNet$DiSplayNet, which can be seen as an interesting generalization of analyses known from sequential self-adjusting datastructures. We complement our theoretical contribution with an extensive trace-driven simulation study, shedding light on the opportunities and limitations of leveraging spatial and temporal locality and concurrency in self-adjusting networks.