Simplifying Wireless Social Caching

TL;DR

This paper explores a social caching strategy where users share cached data opportunistically to reduce bandwidth, proposing a heuristic solution that performs near-optimally based on real-world mobility data.

Contribution

It introduces a polynomial-time heuristic for social caching optimization, inspired by bipartite set cover, with proven worst-case bounds and validated on real mobility traces.

Findings

Heuristic closely matches optimal solutions in most scenarios.

Performance surpasses alternative caching strategies.

Validated on real-world and synthetic mobility datasets.

Abstract

Social groups give the opportunity for a new form of caching. In this paper, we investigate how a social group of users can jointly optimize bandwidth usage, by each caching parts of the data demand, and then opportunistically share these parts among themselves upon meeting. We formulate this problem as a Linear Program (LP) with exponential complexity. Based on the optimal solution, we propose a simple heuristic inspired by the bipartite set-cover problem that operates in polynomial time. Furthermore, we prove a worst case gap between the heuristic and the LP solutions. Finally, we assess the performance of our algorithm using real-world mobility traces from the MIT Reality Mining project dataset and two mobility traces that were synthesized using the SWIM model. Our heuristic performs closely to the optimal in most cases, showing a better performance with respect to alternative solutions.