Shrewd Selection Speeds Surfing: Use Smart EXP3!

TL;DR

This paper introduces Smart EXP3, a novel multi-armed bandit algorithm tailored for distributed network selection, improving practical performance by reducing switches and convergence time, leading to faster and fairer network choices.

Contribution

Smart EXP3 extends EXP3 with switch bounds and practical enhancements, demonstrating improved real-world performance in network selection tasks.

Findings

Achieves 18% faster downloads in real-world tests

Stabilizes at optimal network states effectively

Ensures fairness among multiple devices

Abstract

In this paper, we explore the use of multi-armed bandit online learning techniques to solve distributed resource selection problems. As an example, we focus on the problem of network selection. Mobile devices often have several wireless networks at their disposal. While choosing the right network is vital for good performance, a decentralized solution remains a challenge. The impressive theoretical properties of multi-armed bandit algorithms, like EXP3, suggest that it should work well for this type of problem. Yet, its real-word performance lags far behind. The main reasons are the hidden cost of switching networks and its slow rate of convergence. We propose Smart EXP3, a novel bandit-style algorithm that (a) retains the good theoretical properties of EXP3, (b) bounds the number of switches, and (c) yields significantly better performance in practice. We evaluate Smart EXP3 using simulations, controlled experiments, and real-world experiments. Results show that it stabilizes at the optimal state, achieves fairness among devices and gracefully deals with transient behaviors. In real world experiments, it can achieve 18% faster download over alternate strategies. We conclude that multi-armed bandit algorithms can play an important role in distributed resource selection problems, when practical concerns, such as switching costs and convergence time, are addressed.