Janus: Latency-Aware Traffic Scheduling for IoT Data Streaming in Edge Environments

Abstract

This paper focuses on a simple, yet fundamental question of distributed edge computing: "how to handle IoT traffic with different levels of sensitivity and criticality by satisfying the application-specific latency constraints?" This question arises in the practical deployment of edge computing, where user data can arrive at a much faster rate than that they can be processed by an edge node. Addressing this question is critical for meeting the latency requirement for latency-sensitive applications, but existing approaches are inadequate to the problem. We present JANUS, a multi-level traffic scheduling system for managing multiple data streams with various degrees of latency constraints. At the edge node level, JANUS uses multi-level queues to manage data streams with different latency constraints. It then allocates the output bandwidth of the edge node according to the requirements of applications in different priority queues, aiming to reduce the queuing and processing delay of latency-sensitive streams while maximizing the edge-node throughput. At the network level, JANUS actively redirects incoming data streams to the less-loaded ones to achieve better network-wide load balance and improve the overall throughput. Experiments show that JANUS reduces the latency to only 16.6% of a non-priority based solution and improves the throughput by 1.7x of a state-of-the-art priority-aware data stream scheduling approach.

PHIR: A Platform Solution of Data-Driven Health Monitoring for Industrial Robots

“…Platforms. With the improvement of edge and cloud infrastructure [9][10][11][12][13][14], a large amount of work [15][16][17] has been conducted to manage industrial robots using the technology of IoT. In [18], the authors summarize the latest software technologies required to collect and manage all data through IoT devices that are deployed on production lines.…”