Bandit Convex Optimization for Scalable and Dynamic IoT Management

TL;DR

This paper introduces bandit online saddle-point algorithms for scalable IoT management, enabling adaptive decision-making under changing conditions with limited feedback, and demonstrates their effectiveness in fog computing scenarios.

Contribution

It develops novel bandit online saddle-point schemes tailored for IoT management, handling time-varying loss functions and constraints with limited feedback.

Findings

Achieves sub-linear dynamic regret and constraint fit.

Performs competitively in fog computing offloading tasks.

Handles non-stationary environments with bandit feedback.

Abstract

The present paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (a.k.a. bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.