Distributed Self-Adjusting Tree Networks

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 networ...

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Bibliographic Details
Published in:IEEE transactions on cloud computing 2023-01, Vol.11 (1), p.716-729
Main Authors: Peres, Bruna Soares, Souza, Otavio Augusto de Oliveira, Goussevskaia, Olga, Avin, Chen, Schmid, Stefan
Format: Article
Language:English
Subjects:
Algorithms
amortized analysis
Cloud computing
Concurrency
Costs
datacenters
decentralization
Network topologies
Network topology
Optical switches
Peer-to-peer computing
Performance evaluation
Routing
Self-adjusting networks
Topology
trace-driven simulations
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Summary: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.
ISSN:2168-7161
2372-0018
DOI:10.1109/TCC.2021.3112067