AI-Driven Packet Forwarding With Programmable Data Plane: A Survey

Quan, Wei; Xu, Ziheng; Liu, Mingyuan; Cheng, Nan; Liu, Gang; Gao, Deyun; Zhang, Hongke; Zhuang, Weihua

doi:10.1109/comst.2022.3217613

Cited by 14 publications

(5 citation statements)

References 154 publications

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“…The literature on TCP congestion control design with AI and ML is very rich [13]. Of particular importance to this work is PCC [2,3], where the sender observes the connection between its actions and its experienced performance, which allows it to consistently take actions and improve performance.…”

Section: Literaturementioning

confidence: 99%

DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

Pokhrel,

Kua,

Satish

et al. 2024

Future Internet

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We introduce a novel multipath data transport approach at the transport layer referred to as ‘Deep Deterministic Policy Gradient for Multipath Performance-oriented Congestion Control’ (DDPG-MPCC), which leverages deep reinforcement learning to enhance congestion management in multipath networks. Our method combines DDPG with online convex optimization to optimize fairness and performance in simultaneously challenging multipath internet congestion control scenarios. Through experiments by developing kernel implementation, we show how DDPG-MPCC performs compared to the state-of-the-art solutions.

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Section: Literaturementioning

confidence: 99%

DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

Pokhrel,

Kua,

Satish

et al. 2024

Future Internet

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“…The number of multi-constrained task sets [2][3][4][5][6][7][8][9][10][11][12][13][14] The number of edge servers [14][15][16] The virtual machine capacity of edge servers [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] The CPU frequency of edge servers [2000-2500] MHz…”

Section: Parameter Valuementioning

confidence: 99%

“…1,2 This paradigm integrates a vast number of network-embedded sensors and cutting-edge computer technologies into manufacturing and production, which dramatically increases the production efficiency of businesses and is pushing the development of more sustainable solutions. 3 Nevertheless, since the majority of IIoT devices and sensors are constrained by their computing resources and battery capacity, they struggle to support the growing number of compute-intensive and latency-sensitive applications. 4 As a solution to the resource-constraint issue, multi-access edge computing (MEC) has received extensive attention.…”

Section: Introductionmentioning

confidence: 99%

“…The Industrial Internet of Things (IIoT), an important application of the Internet of Things in industrial scenarios, is becoming a real‐time bridge connecting the digital and industrial worlds through semantic interoperability 1,2 . This paradigm integrates a vast number of network‐embedded sensors and cutting‐edge computer technologies into manufacturing and production, which dramatically increases the production efficiency of businesses and is pushing the development of more sustainable solutions 3 . Nevertheless, since the majority of IIoT devices and sensors are constrained by their computing resources and battery capacity, they struggle to support the growing number of compute‐intensive and latency‐sensitive applications 4 …”

Section: Introductionmentioning

confidence: 99%

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A fairness‐aware task offloading method in edge‐enabled IIoT with multi‐constraints using AGE‐MOEA and weighted MMF

Peng

Ling

Zhao

et al. 2023

Concurrency and Computation

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SummaryBy providing distributed and ultra‐low‐latency communication between industrial devices and resource components, the Industrial Internet of Things (IIoT) is at the forefront of a new trend. Such a distributed paradigm is viewed as a collection of autonomous computing resources utilized by multiple heterogeneous devices to achieve higher‐quality interconnection and data exchange. However, stringent requirements of exceptional service and fairness guarantees pose many formidable challenges. To this end, this study investigates the aforementioned concerns in an integrated manner and further proposes a fairness‐aware task offloading method, called FOIMAM. Specifically, the ‐norm is introduced to accommodate the Pareto plane under the non‐Euclidean geometry framework while the evaluation and elimination of low‐quality solutions are completed based on survival scores. Particularly, the fairness requirements are formulated as a multi‐constraint problem and resolved using weighted max‐min fairness. Eventually, numerical results indicate that the proposed method brings substantial improvement in both service efficiency and fairness guarantees.

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“…The advent of in-network Artificial Intelligence (AI) will enable future network devices to process data (i.e., packets, flows, aggregate traffic) at wirespeed with the support of builtin AI-enabled components [1], [2]. In-network function offloading is becoming popular thanks to the flexibility and maturity of programmable data plane languages such as P4, and has the potential to bring programmable and versatile networking operation at the data plane thus saving computational resources for applications at the edge and with limited energy consumption, since re-using the same network infrastructure devices [3].…”

Section: Introductionmentioning

confidence: 99%

Cascaded Look Up Table Distillation of P4 Deep Neural Network Switches

De Marinis,

Paolini,

Bakar

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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In-network function offloading represents a key enabler of the SDN-based data plane programmability to enhance network operation and awareness while speeding up applications and reducing the energy footprint. The offload of network functions exploiting machine learning and artificial intelligence has been recently considered with intermediate solutions such as feature extraction acceleration and mixed architectures including AI-specific platforms (e.g., GPU, FPGA).Indeed, the P4 language enables the programmability of deep neural networks inside the pipelines of both software and hardware switches and NICs. However, programmable hardware pipeline chipsets suffer from significant computing capability limitations (e.g., missing arithmetic logic units, limited and slow stateful registers) preventing the plain programmability of a deep neural network (DNN) operating at wirespeed. This paper proposes an innovative knowledge distillation technique that maps a DNN into a cascade of lookup tables (i.e., flow tables) with limited entry size. The proposed mapping avoids stateful elements and maths operators, whose requirement prevented the deployment of DNNs within hardware switches up to now. The evaluation is carried out considering a cyber security use case targeting a DDoS mitigator network function, showing negligible impact due to the lossless mapping reduction and feature quantization.

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AI-Driven Packet Forwarding With Programmable Data Plane: A Survey

Cited by 14 publications

References 154 publications

DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control

A fairness‐aware task offloading method in edge‐enabled IIoT with multi‐constraints using AGE‐MOEA and weighted MMF

Cascaded Look Up Table Distillation of P4 Deep Neural Network Switches

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