18 research outputs found
A Kernel-space POF virtual switch
Protocol Oblivious Forwarding (POF) aims at providing a standard southbound interface for sustainable Software Defined Networking (SDN) evolvement. It overcomes the limitations of popular Open Flow protocols (an existing widely-adopted southbound interface), through the enhancement of SDN forwarding plane. This paper pioneers the design and implementation of a Kernel-space POF Virtual Switch (K_POFVS) on Linux platform. K_POFVS can improve the packet processing speed, through fast packet forwarding and the capability of adding/deleting/modifying protocol fields in kernel space. In addition, it is able to enhance flow table matching speed, by separating the mask table (consisting of flow entry masks used to figure out the matching field) and the flow table under a caching mechanism. Furthermore, K_POFVS can achieve efficient communication between the kernel space and the user space, via extending the Netlink communication between them. Experimental results show that K_POFVS can provide much better performance than existing user-space POF virtual switches, in terms of packet forwarding delay, packet processing delay and packet transmission rateThis work is partially supported by the National Program on Key Basic Research Project of China (973
Program) under Grant No. 2012CB315803, the Strategic Priority Research Program of the Chinese Academy of
Sciences under grant No. XDA06010306, the National Natural Science Foundation of China under Grant No.
61303241, and the University of Exeter’s Innovation Platform – Link Fund under Award No. LF207
P4言語を用いたパケット分類アルゴリズムに関する研究
パケット・クラシファイアとは、コンピュータネットワークにおいてネットワーク機器に到着したパケットをグループに分類するメカリズムである。特定の処理のためにパケットを区別して分離する必要があるサービス、例えば、ファイアウォールやサービス品質などのカスタマイズネットワークサービスなどを提供するためにルータでのパケットを分類するのは極めて重要である。パケット分類に関するアルゴリズムがいくつかの研究で提案されている。分類の性能を向上するため、決定木、ヒューリスティックなどを利用した提案がある。しかし、その性能評価は主にハードウェア実装に基づいていたので、アルゴリズムの設計方法、データ構造などソフトウェルーターに適用できない恐れがある。近年、ネットワークプロトコル、ターゲット非依存という特徴をあるP4言語が開発された。P4言語は幅広いのデータプレーンをプログラミングできるように、ネットワークの基本機能に関する表現力豊かな文法設計されています。仮想ネットワーク機能(VNF)に対する研究が流行っている背景のなか、P4言語用いてソフトウェアにおけるパケット分類の実装を研究する必要がある。本研究では、今までネットワークのパケット分類に関するアルゴリズムがP4言語文法による実装を検討する。P4抽象転送モデル中で利用可能なプログラミングフローを議論し、パケット分類の改善に適しているデータ構造を示した。また、異なるアルゴリズムとデータ構造を用いて、P4ソースコードからコンパイルされたソフトウェアルーターの性能評価を行った。電気通信大学201
Software-Defined Networking: A Comprehensive Survey
peer reviewedThe Internet has led to the creation of a digital society, where (almost) everything is connected and is accessible from anywhere. However, despite their widespread adoption, traditional IP networks are complex and very hard to manage. It is both difficult to configure the network according to predefined policies, and to reconfigure it to respond to faults, load, and changes. To make matters even more difficult, current networks are also vertically integrated: the control and data planes are bundled together. Software-defined networking (SDN) is an emerging paradigm that promises to change this state of affairs, by breaking vertical integration, separating the network's control logic from the underlying routers and switches, promoting (logical) centralization of network control, and introducing the ability to program the network. The separation of concerns, introduced between the definition of network policies, their implementation in switching hardware, and the forwarding of traffic, is key to the desired flexibility: by breaking the network control problem into tractable pieces, SDN makes it easier to create and introduce new abstractions in networking, simplifying network management and facilitating network evolution. In this paper, we present a comprehensive survey on SDN. We start by introducing the motivation for SDN, explain its main concepts and how it differs from traditional networking, its roots, and the standardization activities regarding this novel paradigm. Next, we present the key building blocks of an SDN infrastructure using a bottom-up, layered approach. We provide an in-depth analysis of the hardware infrastructure, southbound and northbound application programming interfaces (APIs), network virtualization layers, network operating systems (SDN controllers), network programming languages, and network applications. We also look at cross-layer problems such as debugging and troubleshooting. In an effort to anticipate the future evolution of this - ew paradigm, we discuss the main ongoing research efforts and challenges of SDN. In particular, we address the design of switches and control platforms—with a focus on aspects such as resiliency, scalability, performance, security, and dependability—as well as new opportunities for carrier transport networks and cloud providers. Last but not least, we analyze the position of SDN as a key enabler of a software-defined environment
A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory
This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordNetwork slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.Engineering and Physical Sciences Research Council (EPSRC)Singapore University of Technology and Design (SUTD)Hong Kong RGC Research Impact Fund (RIF)National Natural Science Foundation of ChinaShenzhen Science and Technology Innovation Commissio
5G Network Slicing using SDN and NFV: A Survey of Taxonomy, Architectures and Future Challenges
In this paper, we provide a comprehensive review and updated solutions
related to 5G network slicing using SDN and NFV. Firstly, we present 5G service
quality and business requirements followed by a description of 5G network
softwarization and slicing paradigms including essential concepts, history and
different use cases. Secondly, we provide a tutorial of 5G network slicing
technology enablers including SDN, NFV, MEC, cloud/Fog computing, network
hypervisors, virtual machines & containers. Thidly, we comprehensively survey
different industrial initiatives and projects that are pushing forward the
adoption of SDN and NFV in accelerating 5G network slicing. A comparison of
various 5G architectural approaches in terms of practical implementations,
technology adoptions and deployment strategies is presented. Moreover, we
provide a discussion on various open source orchestrators and proof of concepts
representing industrial contribution. The work also investigates the
standardization efforts in 5G networks regarding network slicing and
softwarization. Additionally, the article presents the management and
orchestration of network slices in a single domain followed by a comprehensive
survey of management and orchestration approaches in 5G network slicing across
multiple domains while supporting multiple tenants. Furthermore, we highlight
the future challenges and research directions regarding network softwarization
and slicing using SDN and NFV in 5G networks.Comment: 40 Pages, 22 figures, published in computer networks (Open Access
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
Networks, Communication, and Computing Vol. 2
Networks, communications, and computing have become ubiquitous and inseparable parts of everyday life. This book is based on a Special Issue of the Algorithms journal, and it is devoted to the exploration of the many-faceted relationship of networks, communications, and computing. The included papers explore the current state-of-the-art research in these areas, with a particular interest in the interactions among the fields