计算机科学
调度(生产过程)
抖动
延迟(音频)
元启发式
分布式计算
迭代局部搜索
数学优化
算法
电信
数学
作者
Junhong Min,Woongsoo Kim,Jeongyeup Paek
出处
期刊:IEEE Internet of Things Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/jiot.2023.3331430
摘要
Time-sensitive networking (TSN) strives to provide an ultra-low-latency real-time deterministic network for time-critical traffic using the time-aware shaper (TAS) mechanism. For this purpose, methods for routing and scheduling the time-critical flows must be specified. However, this is an NP-hard problem. Although several prior studies have suggested constraint programming (CP)-based approaches, these methods fail to provide a reasonable runtime due to the complexity of the problem. Motivated by this, we propose a TAS co-optimization (TACO) framework that solves the TAS scheduling and routing problem in TSN with a reasonable runtime. As an alternative to CP-based approaches, TACO considers a metaheuristic approach to co-optimize routing, scheduling order, and transmission timing. However, joint optimization through a metaheuristic algorithm is challenging due to the heterogeneous search spaces of the sub-problems. Therefore, TACO carefully integrates the search spaces into a single domain and optimizes routing and TAS scheduling jointly with its heuristic algorithm. We evaluate TACO in various industrial networking scenarios to demonstrate that TACO achieves up to an 88% increase in the scheduling success rate with a good convergence rate and an overall low latency/jitter compared to other approaches.
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