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Towards Communication-Efficient Peer-to-Peer Networks

Khalid Hourani, William K. Moses, Gopal Pandurangan

TL;DR

An efficient, decentralized protocol is presented that transforms a random graph topology embedded in an underlying Euclidean space into a topology that also respects the underlying metric.

Abstract

We focus on designing Peer-to-Peer (P2P) networks that enable efficient communication. Over the last two decades, there has been substantial algorithmic research on distributed protocols for building P2P networks with various desirable properties such as high expansion, low diameter, and robustness to a large number of deletions. A key underlying theme in all of these works is to distributively build a \emph{random graph} topology that guarantees the above properties. Moreover, the random connectivity topology is widely deployed in many P2P systems today, including those that implement blockchains and cryptocurrencies. However, a major drawback of using a random graph topology for a P2P network is that the random topology does not respect the \emph{underlying} (Internet) communication topology. This creates a large \emph{propagation delay}, which is a major communication bottleneck in modern P2P networks. In this paper, we work towards designing P2P networks that are communication-efficient (having small propagation delay) with provable guarantees. Our main contribution is an efficient, decentralized protocol, $\textsc{Close-Weaver}$, that transforms a random graph topology embedded in an underlying Euclidean space into a topology that also respects the underlying metric. We then present efficient point-to-point routing and broadcast protocols that achieve essentially optimal performance with respect to the underlying space.

Towards Communication-Efficient Peer-to-Peer Networks

TL;DR

An efficient, decentralized protocol is presented that transforms a random graph topology embedded in an underlying Euclidean space into a topology that also respects the underlying metric.

Abstract

We focus on designing Peer-to-Peer (P2P) networks that enable efficient communication. Over the last two decades, there has been substantial algorithmic research on distributed protocols for building P2P networks with various desirable properties such as high expansion, low diameter, and robustness to a large number of deletions. A key underlying theme in all of these works is to distributively build a \emph{random graph} topology that guarantees the above properties. Moreover, the random connectivity topology is widely deployed in many P2P systems today, including those that implement blockchains and cryptocurrencies. However, a major drawback of using a random graph topology for a P2P network is that the random topology does not respect the \emph{underlying} (Internet) communication topology. This creates a large \emph{propagation delay}, which is a major communication bottleneck in modern P2P networks. In this paper, we work towards designing P2P networks that are communication-efficient (having small propagation delay) with provable guarantees. Our main contribution is an efficient, decentralized protocol, , that transforms a random graph topology embedded in an underlying Euclidean space into a topology that also respects the underlying metric. We then present efficient point-to-point routing and broadcast protocols that achieve essentially optimal performance with respect to the underlying space.

Paper Structure

This paper contains 14 sections, 8 theorems, 2 algorithms.

Key Result

Theorem 3

Let $G$ be a $d$-regular random graph embedded in the unit square. Then, there exists a pair of nodes $u$ and $v$ in $G$ such that $d_G(P)/d(u, v)$, the ratio of the propagation cost of the shortest path $P$ between $u$ and $v$ to the Euclidean distance between them is $\Omega\left(\sqrt{n}\right)$

Theorems & Definitions (15)

  • Definition 1
  • Definition 2
  • Theorem 3
  • Definition 4
  • Theorem 5
  • Theorem 6
  • Definition 7
  • Definition 8: Conductance
  • Definition 9: Expander Graph
  • Definition 10: Random Geometric Graph
  • ...and 5 more