2 research outputs found
Towards Communication-Aware Robust Topologies
We currently witness the emergence of interesting new network topologies
optimized towards the traffic matrices they serve, such as demand-aware
datacenter interconnects (e.g., ProjecToR) and demand-aware overlay networks
(e.g., SplayNets). This paper introduces a formal framework and approach to
reason about and design such topologies. We leverage a connection between the
communication frequency of two nodes and the path length between them in the
network, which depends on the entropy of the communication matrix. Our main
contribution is a novel robust, yet sparse, family of network topologies which
guarantee an expected path length that is proportional to the entropy of the
communication patterns
Self-Adjusting Linear Networks
Emerging networked systems become increasingly flexible and reconfigurable.
This introduces an opportunity to adjust networked systems in a demand-aware
manner, leveraging spatial and temporal locality in the workload for online
optimizations. However, it also introduces a trade-off: while more frequent
adjustments can improve performance, they also entail higher reconfiguration
costs.
This paper initiates the formal study of linear networks which self-adjust to
the demand in an online manner, striking a balance between the benefits and
costs of reconfigurations. We show that the underlying algorithmic problem can
be seen as a distributed generalization of the classic dynamic list update
problem known from self-adjusting datastructures: in a network, requests can
occur between node pairs. This distributed version turns out to be
significantly harder than the classical problem in generalizes. Our main
results are a lower bound on the competitive ratio, and a
(distributed) online algorithm that is -competitive if the
communication requests are issued according to a linear order