Bounded Memory in Distributed Networks

TL;DR

This paper introduces the CONGEST model with memory limits, providing algorithms and lower bounds for clique listing and streaming simulations, enabling efficient distributed network analysis under realistic memory constraints.

Contribution

It presents the CONGEST model, establishes tight bounds for clique listing with memory limits, and demonstrates how streaming algorithms can be adapted for distributed network analysis.

Findings

Established lower bounds for clique listing in CONGEST.

Developed algorithms that match these bounds within memory constraints.

Showed how streaming algorithms can be simulated efficiently in distributed networks.

Abstract

The recent advent of programmable switches makes distributed algorithms readily deployable in real-world datacenter networks. However, there are still gaps between theory and practice that prevent the smooth adaptation of CONGEST algorithms to these environments. In this paper, we focus on the memory restrictions that arise in real-world deployments. We introduce the $\mu$-CONGEST model where on top of the bandwidth restriction, the memory of nodes is also limited to $\mu$ words, in line with real-world systems. We provide fast algorithms of two main flavors. First, we observe that many algorithms in the CONGEST model are memory-intensive and do not work in $\mu$-CONGEST. A prime example of a family of algorithms that use large memory is clique-listing algorithms. We show that the memory issue that arises here cannot be resolved without incurring a cost in the round complexity, by establishing a lower bound on the round complexity of listing cliques in $\mu$-CONGEST. We introduce novel techniques to overcome these issues and generalize the algorithms to work within a given memory bound. Combined with our lower bound, these provide tight tradeoffs between the running time and memory of nodes. Second, we show that it is possible to efficiently simulate various families of streaming algorithms in $\mu$-CONGEST. These include fast simulations of $p$-pass algorithms, random order streams, and various types of mergeable streaming algorithms. Combining our contributions, we show that we can use streaming algorithms to efficiently generate statistics regarding combinatorial structures in the network. An example of an end result of this type is that we can efficiently identify and provide the per-color frequencies of the frequent monochromatic triangles in $\mu$-CONGEST.