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

Yu, Hao; Taleb, Tarik; Zhang, Jiawei

doi:10.1109/tnsm.2022.3151431

Cited by 22 publications

(3 citation statements)

References 33 publications

(43 reference statements)

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“…The authors in [3] also focus on the joint routing and scheduling problem in large-scale deterministic networks using CSQF to maximize traffic acceptance for network planning and online flow admission. Joint routing and network resource allocation for the deterministic service function chaining (SFC) problem was also investigated in [12], where a novel Deterministic SFC Deployment algorithm (Det-SFCD) and an SFC Adjustment algorithm (Det-SFCA) were proposed to ensure deterministic performance during the SFC lifetime.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…Subsequently, a delay-aware and load-balancing embedding algorithm was proposed in order to obtain a near-optimal solution. Yu et al [7] performed a study focused on deterministic latency-or jitter-aware service function chaining in beyond-5G edge fabric. The researchers conducted an investigation into the problem of managing the lifetime of deterministic SFCs in beyond-5G edge fabric.…”

Section: Related Workmentioning

confidence: 99%

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

Erbati,

Tajiki,

Schiele

2023

Electronics

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Network function virtualization (NFV) has the potential to fundamentally transform conventional network architecture through the decoupling of software from dedicated hardware. The convergence of virtualization and cloud computing technologies has revolutionized the networking landscape, offering a wide range of advantages, including improved flexibility, manageability, and scalability. The importance of network capability in enabling ultra-low latency applications has been greatly amplified in the current era due to the increased demand for emerging services such as autonomous driving, teleoperated driving, virtual reality, and remote surgery. This paper presents a novel and efficient methodology for service function chaining (SFC) in an NFV-enabled network that aims to minimize latency and optimize the utilization of physical network resources, with a specific focus on ultra-low latency applications. In our proposed methodology, we offer flow prioritization and an adjustable priority coefficient factor (µ) to reserve a portion of physical network resources exclusively for ultra-low latency applications in order to optimize the deployment paths of these applications further. We formulate the SFC deployment problem as an integer linear programming (ILP) optimization model. Furthermore, we propose a set of heuristic algorithms that yield near-optimal solutions with minimal optimality gaps and execution times, making them practical for large-scale network topologies. Performance evaluations demonstrate the effectiveness of our proposed methodology in enabling ultra-low latency applications in an NFV-enabled network. Compared to existing algorithms, our proposed methodology achieves notable enhancements in terms of the end-to-end delay (up to 22 percent), bandwidth utilization (up to 28 percent), and SFC acceptance rate (up to 13 percent).

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“…The introduction of fifth-generation 5G technology has further revolutionized service selection in mobile networks and played a crucial role in enabling wireless communication and connectivity for mobile devices, allowing people to stay connected and access a wide range of services on the go [9] [10].…”

Section: Introductionmentioning

confidence: 99%

A Review of Service Selection Strategies in Mobile IoT Networks

Mohamed Hadjkouider,

Kerrache,

Korichi

et al. 2024

IEEE Open J. Commun. Soc.

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With the proliferation of service-oriented architectures, the ubiquity of mobile networks, and an expanding spectrum of services available to users, the service selection process has become paramount in designing and implementing contemporary applications. Service selection strategies in mobile networks involve choosing the optimal services from many available options based on QoS (quality of service), bandwidth, and mobility, ultimately augmenting network efficiency and user experience. Nonetheless, particular challenges can impact the efficacy of these strategies. This paper presents a comprehensive survey of service selection strategies in mobile networks, transitioning from traditional approaches to emerging techniques, while also introducing a taxonomy classifying service selection methods based on criteria including mobile devices as service providers, architectural perspectives, computing devices allocation, evaluation metrics, and service selection methods. Additionally, We offer insights into the latest QoS-aware and energy-centric service selection methodologies, while addressing challenges related to battery life and energy efficiency, Quality of Service (QoS), security and privacy, as well as scalability and resource management. Additionally, we discuss prospective trajectories and future direction.

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

Cited by 22 publications

References 33 publications

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

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

Service Function Chaining to Support Ultra-Low Latency Communication in NFV

A Review of Service Selection Strategies in Mobile IoT Networks

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