Energy-Optimal Sampling for Edge Computing Feedback Systems: Aperiodic Case

TL;DR

This paper introduces an energy-efficient aperiodic sampling policy for edge video analytics systems, balancing event detection delay and resource consumption, outperforming existing periodic sampling methods.

Contribution

It proposes a novel aperiodic sampling strategy that optimizes energy use while maintaining responsiveness, improving over current periodic sampling approaches.

Findings

Over 10% energy savings compared to state-of-the-art methods

Effective trade-off between sampling frequency and responsiveness

Improved system efficiency in edge computing scenarios

Abstract

We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that processes them and generates feedback for a user. Sampling the system with the maximum allowed frequency results in the timely detection of relevant events with minimum delay. However, it incurs high energy costs and causes unnecessary usage of network and compute resources via communication and processing of redundant samples. On the other hand, an infrequent sampling result in a higher delay in detecting the relevant event, thus increasing the idle energy usage and degrading the quality of experience in terms of responsiveness of the system. We quantify this sampling frequency trade-off as a weighted function between the number of samples and the responsiveness. We propose an energy-optimal aperiodic sampling policy that improves over the state-of-the-art optimal periodic sampling policy. Numerically, we show the proposed policy provides a consistent improvement of more than 10$\mathbf{\%}$ over the state-of-the-art.