Energy consumption

Dynamic bandwidth allocation

Algorithm

Chaotic

Engineering

Ecology

Analytics

Biology

Lyapunov optimization

Bandwidth (computing)

Real-time computing

Operating system

Joint (building)

Telecommunications

Data mining

Computer science

Cloud computing

Edge device

Distributed computing

Computer network

Physics

Bandwidth allocation

Latency (audio)

Lyapunov redesign

Optics

Lyapunov exponent

Enhanced Data Rates for GSM Evolution

Online algorithm

Data science

Artificial intelligence

Architectural engineering

Edge computing

Adaptation (eye)

Computer vision

Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to the nearby server, thus mitigating long latency incurred by data transmission via wide area networks. When offloading frames from the front-end device to the edge server, the application configuration (frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration adaption and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. Simulation results show that JCAB can effectively balance the analytics accuracy and energy consumption while keeping low system latency.

Joint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics