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

Yudong Huang, Tao Huang, Xinyuan Zhang, Shuo Wang, Hongyang Du, Dusit Niyato, Fei Richard Yu

TL;DR

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 and discretizes the underlying network resources into cycle-related queue resource blocks.

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.

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

TL;DR

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 and discretizes the underlying network resources into cycle-related queue resource blocks.

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.
Paper Structure (29 sections, 2 theorems, 24 equations, 10 figures, 2 tables, 2 algorithms)

This paper contains 29 sections, 2 theorems, 24 equations, 10 figures, 2 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Background
  3. Motivation
  4. How to Model the CSQF?
  5. How to Calculate the Cycle Tags?
  6. Cycle Tags Planning
  7. CTP Model
  8. Integer Programming Inputs and Variables
  9. Core Constraints
  10. Schedulability Analysis
  11. Algorithm Design
  12. Design Guidelines
  13. Path Selection
  14. Basic Flow Scheduling Algorithm
  15. Enhanced Flow Sequence Algorithm
  16. ...and 14 more sections

Key Result

Theorem 1

When the total network resources are constant (due to limited on-chip memory), the schedulability increases as the queue length $L$ becomes smaller, i.e.,

Figures (10)

  • Figure 1: The working mechanism of CSQF in the factory interconnection or cloud PLC scenarios, where the control plane has to compute the cycle tags (SID labels) for every packet along the path.
  • Figure 2: Comparison of mechanisms evolving from local CQF to CSQF under long-distance Industrial IoT networks.
  • Figure 3: An access-aware scenario with the queue length of three packets. The node A is the access gateway device of the CSQF-enabled network domain.
  • Figure 4: An example of the flow offset and cycle shift (FO-CS) method. The flow offset operation decides the packet's arrival time at the first hop. The cycle shift optimizes the selection of receiving queues at each node.
  • Figure 5: The CTP model from the perspective of the output port.
  • ...and 5 more figures

Theorems & Definitions (4)

  • Theorem 1
  • proof
  • Corollary 1
  • proof