CSQF-based Time-Sensitive Flow Scheduling in Long-distance Industrial IoT Networks

TL;DR

This paper introduces a novel integer programming model and heuristic algorithms for global flow scheduling in long-distance industrial IoT networks using CSQF, significantly improving flow scheduling efficiency.

Contribution

It proposes the first integer programming model for CSQF-based flow scheduling and develops heuristic algorithms to optimize global flow management in long-distance industrial IoT networks.

Findings

FO-CS increases scheduled flows by 31.2%

Tabu FO-CS schedules 94.45% of flows at 2000 flows

The approach effectively aligns cycles for long-distance links

Abstract

Booming time-critical services, such as automated manufacturing and remote operations, stipulate increasing demands for facilitating large-scale Industrial Internet of Things (IoT). Recently, a cycle specified queuing and forwarding (CSQF) scheme has been advocated to enhance the Ethernet. However, CSQF only outlines a foundational equipment-level primitive, while how to attain network-wide flow scheduling is not yet determined. Prior endeavors primarily focus on the range of a local area, rendering them unsuitable for long-distance factory interconnection. This paper devises the cycle tags planning (CTP) mechanism, the first integer programming model for the CSQF, which makes the CSQF practical for efficient global flow scheduling. In the CTP model, the per-hop cycle alignment problem is solved by decoupling the long-distance link delay from cyclic queuing time. To avoid queue overflows, we discretize the underlying network resources into cycle-related queue resource blocks and detail the core constraints within multiple periods. Then, two heuristic algorithms named flow offset and cycle shift (FO-CS) and Tabu FO-CS are designed to calculate the flows' cycle tags and maximize the number of schedulable flows, respectively. Evaluation results show that FO-CS increases the number of scheduled flows by 31.2%. The Tabu FO-CS algorithm can schedule 94.45% of flows at the level of 2000 flows.