11 research outputs found
A Comprehensive Survey of In-Band Control in SDN: Challenges and Opportunities
Software-Defined Networking (SDN) is a thriving networking architecture that has gained popularity in recent years, particularly as an enabling technology to foster paradigms like edge computing. SDN separates the control and data planes, which are later on synchronised via a control protocol such as OpenFlow. In-band control is a type of SDN control plane deployment in which the control and data planes share the same physical network. It poses several challenges, such as security vulnerabilities, network congestion, or data loss. Nevertheless, despite these challenges, in-band control also presents significant opportunities, including improved network flexibility and programmability, reduced costs, and increased reliability. Benefiting from the previous advantages, diverse in-band control designs exist in the literature, with the objective of improving the operation of SDN networks. This paper surveys the different approaches that have been proposed so far towards the advance in in-band SDN control, based on four main categories: automatic routing, fast failure recovery, network bootstrapping, and distributed control. Across these categories, detailed summary tables and comparisons are presented, followed by a discussion on current trends a challenges in the field. Our conclusion is that the use of in-band control in SDN networks is expected to drive innovation and growth in the networking industry, but efforts for holistic and full-fledged proposals are still needed
Reliable Packet Streams with Multipath Network Coding
With increasing computational capabilities and advances in robotics, technology is at the verge of the next industrial revolution. An growing number of tasks can be performed by artificial intelligence and agile robots. This impacts almost every part of the economy, including agriculture, transportation, industrial manufacturing and even social interactions. In all applications of automated machines, communication is a critical component to enable cooperation between machines and exchange of sensor and control signals.
The mobility and scale at which these automated machines are deployed also challenges todays communication systems. These complex cyber-physical systems consisting of up to hundreds of mobile machines require highly reliable connectivity to operate safely and efficiently. Current automation systems use wired communication to guarantee low latency connectivity. But wired connections cannot be used to connect mobile robots and are also problematic to deploy at scale. Therefore, wireless connectivity is a necessity. On the other hand, it is subject to many external influences and cannot reach the same level of reliability as the wired communication systems.
This thesis aims to address this problem by proposing methods to combine multiple unreliable wireless connections to a stable channel. The foundation for this work is Caterpillar Random Linear Network Coding (CRLNC), a new variant of network code designed to achieve low latency. CRLNC performs similar to block codes in recovery of lost packets, but with a significantly decreased latency. CRLNC with Feedback (CRLNC-FB) integrates a Selective-Repeat ARQ (SR-ARQ) to optimize the tradeoff between delay and throughput of reliable communication. The proposed protocol allows to slightly increase the overhead to reduce the packet delay at the receiver. With CRLNC, delay can be reduced by more than 50 % with only a 10 % reduction in throughput. Finally, CRLNC is combined with a statistical multipath scheduler to optimize the reliability and service availability in wireless network with multiple unreliable paths. This multipath CRLNC scheme improves the reliability of a fixed-rate packet stream by 10 % in a system model based on real-world measurements of LTE and WiFi.
All the proposed protocols have been implemented in the software library NCKernel. With NCKernel, these protocols could be evaluated in simulated and emulated networks, and were also deployed in several real-world testbeds and demonstrators.:Abstract 2
Acknowledgements 6
1 Introduction 7
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2 Use Cases and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.3 Opportunities of Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 State of the Art of Multipath Communication 19
2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.4 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.5 Application Layer and Session Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.6 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
3 NCKernel: Network Coding Protocol Framework 27
3.1 Theory that matters! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.1 Socket Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
3.3.2 En-/Re-/Decoder API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.3.3 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.4 Timers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.3.5 Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.5 Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4 Low-Latency Network Coding 35
4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Random Linear Network Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
4.3 Low Latency Network Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.4 CRLNC: Caterpillar Random Linear Network Coding . . . . . . . . . . . . . . . . . . 38
4.4.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.4.2 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.5.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
4.5.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
4.5.3 Packet Loss Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.5.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
4.5.5 Window Size Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5 Delay-Throughput Tradeoff 55
5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.2 Network Coding with ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
5.3 CRLNC-FB: CRLNC with Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
5.3.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.2 Decoding and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
5.3.3 Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
5.4.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.4.3 Systematic Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
5.4.4 Coded Packet Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
5.4.5 Comparison with other Protocols . . . . . . . . . . . . . . . . . . . . . . . . 67
5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
6 Multipath for Reliable Low-Latency Packet Streams 73
6.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.3.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
6.3.3 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.3.4 Reliability Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.4 Multipath CRLNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
6.4.1 Window Size for Heterogeneous Paths . . . . . . . . . . . . . . . . . . . . . 77
6.4.2 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.5.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
6.5.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
6.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
7 Conclusion 94
7.1 Results and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
7.2 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
Acronyms 99
Publications 101
Bibliography 10
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
As communication protocols evolve, datacenter network utilization increases.
As a result, congestion is more frequent, causing higher latency and packet
loss. Combined with the increasing complexity of workloads, manual design of
congestion control (CC) algorithms becomes extremely difficult. This calls for
the development of AI approaches to replace the human effort. Unfortunately, it
is currently not possible to deploy AI models on network devices due to their
limited computational capabilities. Here, we offer a solution to this problem
by building a computationally-light solution based on a recent reinforcement
learning CC algorithm [arXiv:2207.02295]. We reduce the inference time of RL-CC
by x500 by distilling its complex neural network into decision trees. This
transformation enables real-time inference within the -sec decision-time
requirement, with a negligible effect on quality. We deploy the transformed
policy on NVIDIA NICs in a live cluster. Compared to popular CC algorithms used
in production, RL-CC is the only method that performs well on all benchmarks
tested over a large range of number of flows. It balances multiple metrics
simultaneously: bandwidth, latency, and packet drops. These results suggest
that data-driven methods for CC are feasible, challenging the prior belief that
handcrafted heuristics are necessary to achieve optimal performance
Livraison de contenus sur un réseau hybride satellite / terrestre
Lâaugmentation et le renforcement des usages dâInternet rend nĂ©cessaire lâĂ©volution des rĂ©seaux existants. Cependant, on constate de fortes inĂ©galitĂ©s entre les zones urbaines, bien desservies et qui concentrent lâessentiel des investissements, et les zones rurales, mal desservies etdĂ©laissĂ©es. Face Ă cette situation, les utilisateurs de ces zones se tournent vers dâautres moyensdâaccĂšs, et notamment vers les accĂšs Internet par satellite. Cependant, ces derniers souffrentdâune limitation qui est le dĂ©lai important induit par le temps de propagation du signal entre la terre et lâorbite gĂ©ostationnaire. Dans cette thĂšse, nous nous intĂ©ressons Ă lâutilisation simultanĂ©e dâun rĂ©seau dâaccĂšs terrestre, caractĂ©risĂ© par un faible dĂ©bit et un faible dĂ©lai, et dâun rĂ©seau dâaccĂšs satellite, caractĂ©risĂ© par une forte latence et un dĂ©bit plus important. Dâautre part, les rĂ©seaux dediffusion de contenus ou CDNs, constituĂ©s dâun grand nombre de serveurs de cache, apportentune rĂ©ponse Ă lâaugmentation du trafic et des besoins en termes de latence et de dĂ©bit.Cependant, localisĂ©s dans les rĂ©seaux de cĆur, les caches restent Ă©loignĂ©s des utilisateurs etnâatteignent pas les rĂ©seaux dâaccĂšs. Ainsi, les fournisseurs dâaccĂšs Internet (FAI) se sontintĂ©ressĂ©s au dĂ©ploiement de ces serveurs au sein de leur propre rĂ©seau, que lâon appelle alorsTelCo CDN. La diffusion des contenus nĂ©cessite idĂ©alement lâinterconnexion des opĂ©rateurs CDNavec les TelCo CDNs, permettant ainsi la dĂ©lĂ©gation de la diffusion Ă ces derniers. Ils sont alorsen mesure dâoptimiser la diffusion des contenus sur leur rĂ©seau dont ils ont une meilleureconnaissance. Ainsi, nous nous intĂ©resserons Ă lâoptimisation de la livraison de contenus sur unrĂ©seau hybride satellite / terrestre intĂ©grĂ© Ă une chaĂźne de livraison CDN. Nous nous attacheronsdans un premier temps Ă dĂ©crire une architecture permettant, grĂące Ă lâinterconnexion de CDNs,de prendre en charge la diffusion des contenus sur le rĂ©seau hybride. Dans un second temps,nous Ă©tudierons lâintĂ©rĂȘt de la connaissance des informations apportĂ©es par le contexte CDN pour le routage sur une telle architecture. Dans ce cadre, nous proposerons un mĂ©canisme de routage fondĂ© sur la taille des contenus. Finalement, nous montrerons la supĂ©rioritĂ© de notre approche sur lâutilisation du protocole de transport multichemin MP-TC
Swarm of UAVs for Network Management in 6G: A Technical Review
Fifth-generation (5G) cellular networks have led to the implementation of
beyond 5G (B5G) networks, which are capable of incorporating autonomous
services to swarm of unmanned aerial vehicles (UAVs). They provide capacity
expansion strategies to address massive connectivity issues and guarantee
ultra-high throughput and low latency, especially in extreme or emergency
situations where network density, bandwidth, and traffic patterns fluctuate. On
the one hand, 6G technology integrates AI/ML, IoT, and blockchain to establish
ultra-reliable, intelligent, secure, and ubiquitous UAV networks. 6G networks,
on the other hand, rely on new enabling technologies such as air interface and
transmission technologies, as well as a unique network design, posing new
challenges for the swarm of UAVs. Keeping these challenges in mind, this
article focuses on the security and privacy, intelligence, and
energy-efficiency issues faced by swarms of UAVs operating in 6G mobile
networks. In this state-of-the-art review, we integrated blockchain and AI/ML
with UAV networks utilizing the 6G ecosystem. The key findings are then
presented, and potential research challenges are identified. We conclude the
review by shedding light on future research in this emerging field of research.Comment: 19,
Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications
Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era
Situation-aware Edge Computing
Future wireless networks must cope with an increasing amount of data that needs to be transmitted to or from mobile devices. Furthermore, novel applications, e.g., augmented reality games or autonomous driving, require low latency and high bandwidth at the same time. To address these challenges, the paradigm of edge computing has been proposed. It brings computing closer to the users and takes advantage of the capabilities of telecommunication infrastructures, e.g., cellular base stations or wireless access points, but also of end user devices such as smartphones, wearables, and embedded systems. However, edge computing introduces its own challenges, e.g., economic and business-related questions or device mobility. Being aware of the current situation, i.e., the domain-specific interpretation of environmental information, makes it possible to develop approaches targeting these challenges.
In this thesis, the novel concept of situation-aware edge computing is presented. It is divided into three areas: situation-aware infrastructure edge computing, situation-aware device edge computing, and situation-aware embedded edge computing. Therefore, the concepts of situation and situation-awareness are introduced. Furthermore, challenges are identified for each area, and corresponding solutions are presented. In the area of situation-aware infrastructure edge computing, economic and business-related challenges are addressed, since companies offering services and infrastructure edge computing facilities have to find agreements regarding the prices for allowing others to use them. In the area of situation-aware device edge computing, the main challenge is to find suitable nodes that can execute a service and to predict a nodeâs connection in the near future. Finally, to enable situation-aware embedded edge computing, two novel programming and data analysis approaches are presented that allow programmers to develop situation-aware applications.
To show the feasibility, applicability, and importance of situation-aware edge computing, two case studies are presented. The first case study shows how situation-aware edge computing can provide services for emergency response applications, while the second case study presents an approach where network transitions can be implemented in a situation-aware manner
Deliverable D2.1 - Ecosystem analysis and 6G-SANDBOX facility design
This document provides a comprehensive overview of the core aspects of the 6G-SANDBOX project. It outlines the project's vision, objectives, and the Key Performance Indicators (KPIs) and Key Value Indicators (KVIs) targeted for achievement. The functional and non-functional requirements of the 6G-SANDBOX Facility are extensively presented, based on a proposed reference blueprint. A detailed description of the updated reference architecture of the facility is provided, considering the requirements outlined. The document explores the experimentation framework, including the lifecycle of experiments and the methodology for validating KPIs and KVIs. It presents the key technologies and use case enablers towards 6G that will be offered within the trial networks. Each of the platforms constituting the 6G-SANDBOX Facility is described, along with the necessary enhancements to align them with the project's vision in terms of hardware, software updates, and functional improvements