Deterministic Service Function Chaining over Beyond 5G Edge Fabric

Yu, Hao; Taleb, Tarik; Zhang, Jiawei

doi:10.1109/globecom46510.2021.9685927

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Recently, the IETF DetNet working group has been studying deterministic data transmission by incorporating Segment Routing (SR) in Layer 3 to extend TSN technologies for queuing and scheduling, in order to support deterministic bounded latency and jitter for time-critical traffic [2]. In particular, regarding the strength of programmability of SR, the working group is currently specifying a Cycle Specified Queuing and Forwarding (CSQF) mechanism [3] to schedule the flows in a more flexible and scalable way, where the forwarding time slot can be specified for the packets, which will increase the bandwidth efficiency.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Deterministic Routing and Scheduling for Mixed-Criticality Flows

Taleb

Zhang

2023

IEEE Trans. Ind. Inf.

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Deterministic networking (DetNet) has recently drawn much attention by investigating deterministic flow scheduling. Combined with artificial intelligent (AI) technologies, it can be leveraged as a promising network technology for facilitating automated network configuration in the Industrial Internet of Things (IIoT). However, the stricter requirements of the IIoT have posed significant challenges, that is, deterministic and bounded latency for time-critical applications. This paper incorporates deep reinforcement learning (DRL) in Cycle Specified Queuing and Forwarding (CSQF) and proposes a DRLbased Deterministic Flow Scheduler (Deep-DFS) to solve the Deterministic Flow Routing and Scheduling (DFRS) problem. Novel delay aware network representations, action masking and criticality aware reward function design are proposed to make Deep-DFS more scalable and efficient. Simulation experiments are conducted to evaluate the performances of Deep-DFS, and the results show that Deep-DFS can schedule more flows than the other benchmark methods (heuristic-based and AI-based methods).

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Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Deterministic Routing and Scheduling for Mixed-Criticality Flows

Taleb

Zhang

2023

IEEE Trans. Ind. Inf.

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“…We extend our previous work in [9] to further address the problem of provisioning SFC requests with deterministic latency and jitter. First, we formulate it as a mathematical model and the objective is to maximize the overall profits for ISP over a time period.…”

Section: Introductionmentioning

confidence: 99%

Deterministic Latency/Jitter-Aware Service Function Chaining Over Beyond 5G Edge Fabric

Taleb

Zhang

2022

IEEE Trans. Netw. Serv. Manage.

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Deterministic network (DetNet) recently attracts much attention which aims at studying the deterministic bounded latency and low latency variation for time-sensitive applications (e.g., industrial automation). To improve the quality of service (QoS) guarantee and make the network management efficient, it is desirable for Internet Service Provider (ISP) to obtain an optimal service function chain (SFC) provision strategy while providing deterministic service performance for the time-sensitive applications. In this paper, we will study the deterministic SFC lifetime management problem in beyond 5G edge fabric with the objective of maximizing the overall profits and ensuring the deterministic latency and jitter of SFC requests. We first formulate this problem as a mathematical model with the maximal profits for ISP. Then, the novel Deterministic SFC Deployment algorithm (Det-SFCD) and SFC Adjustment algorithm (Det-SFCA) due to traffic load variation have been proposed to efficiently solve the SFC lifetime management problem. Extensive simulation results show that our proposed algorithms can achieve the higher performance in terms of SFC request acceptance rates, overall profits and latency variation compared with the benchmark algorithm.

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