Asymptotically-Optimal Incentive-Based En-Route Caching Scheme

TL;DR

This paper introduces a distributed, online en-route caching algorithm that considers incentives and quality of service, achieving asymptotically optimal performance in large networks.

Contribution

It proposes a low complexity, incentive-aware caching algorithm with proven asymptotic optimality and competitive ratio analysis, suitable for integration with existing TCP/IP networks.

Findings

The algorithm performs near-optimally in large networks.

No online algorithm can surpass an $ ext{O}( ext{log} n)$ competitive ratio.

Simulations confirm the effectiveness of the extended algorithm.

Abstract

Content caching at intermediate nodes is a very effective way to optimize the operations of Computer networks, so that future requests can be served without going back to the origin of the content. Several caching techniques have been proposed since the emergence of the concept, including techniques that require major changes to the Internet architecture such as Content Centric Networking. Few of these techniques consider providing caching incentives for the nodes or quality of service guarantees for content owners. In this work, we present a low complexity, distributed, and online algorithm for making caching decisions based on content popularity, while taking into account the aforementioned issues. Our algorithm performs en-route caching. Therefore, it can be integrated with the current TCP/IP model. In order to measure the performance of any online caching algorithm, we define the competitive ratio as the ratio of the performance of the online algorithm in terms of traffic savings to the performance of the optimal offline algorithm that has a complete knowledge of the future. We show that under our settings, no online algorithm can achieve a better competitive ratio than $\Omega(\log n)$, where $n$ is the number of nodes in the network. Furthermore, we show that under realistic scenarios, our algorithm has an asymptotically optimal competitive ratio in terms of the number of nodes in the network. We also study an extension to the basic algorithm and show its effectiveness through extensive simulations.