Scaling Laws of the Throughput Capacity and Latency in Information-Centric Networks

TL;DR

This paper analyzes how caching impacts throughput and latency in wireless information-centric networks, showing that caching significantly improves performance as network size grows, with specific scaling laws derived for different network types.

Contribution

It derives the asymptotic scaling laws for throughput and latency in cache-enabled networks with limited data lifetime, comparing them to non-caching scenarios.

Findings

Caching improves throughput inversely proportional to the square root and logarithm of network size.

Data access time remains constant with network growth under fixed request and cache expiration rates.

Simple path search mechanisms retain most of the caching benefits.

Abstract

Wireless information-centric networks consider storage as one of the network primitives, and propose to cache data within the network in order to improve latency and reduce bandwidth consumption. We study the throughput capacity and delay in an information-centric network when the data cached in each node has a limited lifetime. The results show that with some fixed request and cache expiration rates, the order of the data access time does not change with network growth, and the maximum throughput order is inversely proportional to the square root and logarithm of the network size $n$ in cases of grid and random networks, respectively. Comparing these values with the corresponding throughput and latency with no cache capability (throughput inversely proportional to the network size, and latency of order $\sqrt{n}$ and $\sqrt{\frac{n}{\log n}}$ in grid and random networks, respectively), we can actually quantify the asymptotic advantage of caching. Moreover, we compare these scaling laws for different content discovery mechanisms and illustrate that not much gain is lost when a simple path search is used.