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Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization

APPLIED SCIENCES-BASEL 2024年第14期14卷 5967页

作者： Tache (Ungureanu), Maria Daniela Pascutoiu, Ovidiu Borcoci, Eugen Natl Univ Sci & Technol Polytech Bucharest Doctoral Sch Elect Telecommun & Informat Technol Bd Iuliu Maniu 1-3 Bucharest 061071 Romania Transilvania Univ Brasov Dept Automat & Informat Technol Bd Eroilor 29 Brasov 500036 Romania

Software-Defined Networking is today a mature technology, which is developed in many networks and also embedded in novel architectures like 5G and 6G. The SDN control centralization concept brings significant advantages for management and control in SDN together with the programmability of the data plane. SDN represents a paradigm shift towards agile, efficient, and secure network infrastructures, moving away from traditional, hardware-centric models to embrace dynamic, software-driven paradigms. SDN is compliant also with the virtualization architecture defined in the Network Function Virtualization framework. However, SDN should cooperate seamlessly for some years with the distributed TCP/ip control developed during the years all over the world. Among others, the traditional tasks of routing, forwarding, load balancing, QoS assurance, security, and privacy should be solved. The SDN native centralization brings also some new challenges and problems which are different from the traditional distributed control ip networks. The algorithms and protocols usable in SDN should meet requirements like scalability, convergence, redundancy assurance, sustainability, and good real-time response, and allow orchestrated automation in enhancing network resilience and adaptability. This work presents a theoretical review of state-of-the-art SDN optimization techniques, offering a critical and comparative discussion of various algorithms having tasks such as routing (including dynamic ones), forwarding, load balancing and traffic optimization, and forwarding delay minimization. Attention is pointed to general algorithms which can offer pragmatic solutions for large systems or multiple metric routing.

关键词： algorithm 5G networks connectivity SDN controller data rate latency SDN

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Flexible Design on Deterministic ip Networking for Mixed traffic Transmission

Flexible Design on Deterministic IP Networking for Mixed Tra...

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IEEE International Conference on Communications (ICC)

作者： Wu, Binwei Wang, Shuo Wang, Jiasen Tan, Weiqian Liu, Yunjie Purple Mt Labs Nanjing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Southeast Univ Nanjing Peoples R China

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Deterministic ip (Dip) networking is a promising technique that can provide delay-bounded transmission in large-scale networks. Nevertheless, Dip faces several challenges in the mixed traffic scenarios, including (i) the capability of ultralow latency communications, (ii) the simultaneous satisfaction of diverse QoS requirements, and (iii) the network efficiency. The problems are more formidable in the dynamic surroundings without prior knowledge of traffic demands. To address the above-mentioned issues, this paper designs a flexible Dip (FDip) network. In the proposed network, we classify the queues at the output port into multiple groups. Each group operates with different cycle lengths. FDip can assign the time-sensitive flows with different groups, hence delivering diverse QoS requirements, simultaneously. The ultra-low latency communication can be achieved by specific groups with short cycle lengths. Moreover, the flexible scheduling with diverse cycle lengths improves resource utilization, hence increasing the throughput (i.e., the number of acceptable time-sensitive flows). We formulate a throughput maximization problem that jointly considers the admission control, transmission path selection, and cycle length assignment A branch and bound (BnB)-based heuristic is developed. Simulation results show that the proposed FDip significantly outperforms the standard Dip in terms of both the throughput and the latency guarantees.

关键词： Integer programming Simulation Admission control Throughput Resource management ip networks Standards

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Artificial Intelligence Enabled Radio Propagation for Communications-Part I: Channel Characterization and Antenna-Channel Optimization

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2022年第6期70卷 3939-3954页

作者： Huang, Chen He, Ruisi Ai, Bo Molisch, Andreas F. Lau, Buon Kiong Haneda, Katsuyuki Liu, Bo Wang, Cheng-Xiang Yang, Mi Oestges, Claude Zhong, Zhangdui Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Beijing Jiaotong Univ Key Lab Railway Ind Broadband Mobile Informat Com Beijing 100044 Peoples R China Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90089 USA Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden Aalto Univ Dept Radio Sci & Engn Espoo 02150 Finland Univ Glasgow Sch Engn Glasgow G12 8QQ Lanark Scotland Southeast Univ Sch Informat Sci & Engn Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Catholic Univ Louvain Inst Informat & Commu nicat Technol Elect & Appl Math B-1348 Louvain Belgium

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence (AI) techniques. In this two-part article, we investigate the application of AI and, in particular, machine learning (ML) to the study of wireless propagation channels. It first provides a comprehensive overview of ML for channel characterization and ML-based antenna-channel optimization in this first part, and then, it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part ii. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next-generation networks of the topics covered in this part rounds off this article.

关键词： Artificial intelligence Optimization Channel estimation Antennas Wireless communication Receiving antennas Neural networks Artificial intelligence (AI) clustering and tracking machine learning (ML) parameter estimation propagation channel

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Dynamic Multicast Routing Management for Robust Wide Area Power Grid Communication

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2021年第6期57卷 5658-5666页

作者： Kushal, Tazim Ridwan Billah Illindala, Mahesh S. Wang, Jiankang Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA Calif ISO Folsom CA 95630 USA

High-volume synchrophasor data generated by the wide area measurement system (WAMS) present new challenges for the design of scalable and robust communication architectures suitable for the large-scale deployment in power grid monitoring and control applications. Features of information and communication technology (ICT) can be harnessed to devise a more flexible infrastructure than the conventional unicast-based hierarchical architecture. Multicast has been suggested as a potential solution for scalability issues in the current WAMS architecture. This article proposes a decentralized dynamic multicast routing management algorithm that manages network traffic efficiently, improves scalability, and makes the system resilient to component failures. The algorithm models the creation of alternate links as a stochastic process using the random-graph theory and manages connections between end hosts at the session layer. This protocol can be implemented using existing ICT protocols at a minimal cost. Numerical simulations and study of frequency control in an IEEE 39-bus system testbed show that the proposed algorithm improves the topological efficiency and resilience of the network.

关键词： Routing Phasor measurement units ip networks Protocols Wide area networks Heuristic algorithms Multicast algorithms Multicast communication routing protocols supervisory control and data acquisition (SCADA) smart grid wide area networks (WANs)

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[Invited Paper] Time Gating control Approaches for Time Critical Applications from Perspective of Wireless LANs and All Optical networks

[Invited Paper] Time Gating Control Approaches for Time Crit...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Hideki Tode Yosuke Tanigawa Ratha Yeu Akio Hasegawa Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Sakai Osaka Japan Advanced Telecommunications Research Institute International Seika-cho Soraku-gun Kyoto Japan

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

In recent years, with the proliferation of diverse time-critical applications, there is a growing need for technologies that can limit packet transmission delays within acceptable bounds while also maintaining low packet loss rates. This paper addresses this challenge by introducing the concept of Time-Gating control. In particular, it aims to maintain compatibility with existing information network infrastructures like TCP/ip and CSMA/CA, ensuring low cost and high scalability. Initially, the paper focuses on wireless access networks in smart factory environments, proposing a method that introduces a hierarchical time-unit structure for periodic traffic, such as sensor data and surveillance video. This method determines time widths for the overlap-free transmission of varied periodic communications, thereby facilitating temporal load distribution. We present results from functional verification tests conducted using a software prototype in this environment. Furthermore, as a solution for non-pediodic traffic with permissive delay constraints, an overview of a virtual slot-based CSMA/CA method is introduced. In addition, targeting completely different applications, we explore the implementation of Time-Gating control in All Optical Packet Switching networks within Beyond 5G access-metro and data center networks. Here, we design a new TCP transmission method incorporating this control mechanism and discuss its advantages and superior performance.

关键词： Wireless networks Surveillance Prototypes TCPip traffic control Software Delays

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High Capacity and Resilient Large-Scale Deterministic ip networks

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2022年第4期30卷 71-71页

作者： Angilella, Vincent Krasniqi, Filip Medagliani, Paolo Martin, Sebastien Leguay, Jeremie Shoushou, Ren Xuan, Liu Huawei Technol Boulogne France

With 5G networking, deterministic guarantees are emerging as a key enabler. In this context, we present a scalable architecture for Large-scale Deterministic ip networks (LDN) that meets end-to-end latency and jitter bounds. This work extends the original LDN (Liu et al. in IFip Networking, 2021) architecture, where flows are shaped at ingress gateways and scheduled for transmission at each link using an asynchronous and cyclic opening of gate-controlled queues. To further optimize the utilization of bandwidth and accept more traffic, our Advanced-LDN (A-LDN) architecture introduces a new shaping mechanism based on transmission patterns. To protect flows against failures, we also implement Frame Replication and Elimination for Reliability (FRER). To do so, we leverage on the A-LDN mechanism for traffic scheduling and activate it at core nodes, i.e., adding the possibility for additional shifts inside core nodes, so that replicas can arrive at destination at the same time. In any case, the network remains stateless (no per-flow states at core nodes), ensuring scalability over large-scale networks. For the control plane, we present variants of a column generation algorithm to quickly take admission control decisions and maximize traffic acceptance. For a set of flows, it determines acceptance and selects the best shaping parameters, routing policy, transmission patterns, and scheduling. Through numerical results and packet level simulations in OMNeT++, we demonstrate that our A-LDN architecture with transmission patterns improves traffic acceptance by up to 67% compared to the original LDN architecture. We also demonstrate that FRER can be efficiently supported at the cost of some extra bandwidth utilization.

关键词： Deterministic networks Large-scale ip networks Bounded delay Bounded jitter

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A deep learning technique to detect distributed denial of service attacks in software-defined networks

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COMPUTERS & SECURITY 2024年 137卷

作者： Gadallah, Waheed G. Ibrahim, Hosny M. Omar, Nagwa M. Assiut Univ Fac Comp & Informat Informat Technol Assiut 71515 Egypt Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China

Software-Defined Network (SDN) is an established networking paradigm that separates the control plane from the data plane. It has central network control, and programmability facilities, therefore SDN can improve network flexibility, management, performance, and scalability. The programmability and control centralization of SDN have improved network functions but also exposed it to security challenges such as Distributed Denial of Service (DDoS) attacks that target both control and data planes. This paper proposes an effective detection technique against DDoS attack in SDN control plane and data plane. For the control plane, the technique detects DDoS attacks through a Deep Learning (DL) model using new features extracted from traffic statistics. A DL method (AE-BGRU) for DDoS detection uses Autoencoder (AE) with Bidirectional Gated Recurrent Unit (BGRU). The proposed features for the control plane include unknown ip destination address, packets inter-arrival time, Transport layer protocol (TLP) header, and Type of service (ToS) header. For the data plane, the technique tracks the switch's average arrival bit rate with an unknown destination address in the data plane. Then, the technique detects DDoS attacks through a DL-based model which also uses AE with BGRU. The proposed features in the data plane include the switch's stored capacity, the average rate of packets with unknown destination addresses, the ip Options header, and the average number of flows. The dataset is generated from feature extraction and computations from normal and attack packets and used with the classifier. Also, additional Machine Learning (ML) methods are used to enhance the detection process. If the model detects an attack, the technique mitigates DDoS effects by updating the user's trust value and blocking suspicious senders based on the trust value. The experimental results proved that compared to related techniques, the suggested method had a higher accuracy and lower false alarm rate.

关键词： Software-defined networking Distributed denial of service Deep learning Autoencoder Bidirectional gated recurrent unit Trust value

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Hierarchy Beyond Top-Down control: The Architecture of Self-Organised Social Systems 4

Hierarchy Beyond Top-Down Control: The Architecture of Self-...

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4th IEEE International Conference on Automatic Computing and Self-Organizing Systems Companion, ACSOS-C 2023

作者： Di Felice, Louisa Jane Diaconescu, Ada Icta Universitat Autònoma de Barcelona Télécom Paris Ip Paris Ltci France Telecom Paris Ip Paris Ltci France

ISBN: (纸本)9798350337464

Diverse social system architectures are needed to address the inter-related social and environmental challenges that societies face across scales. The iron law of oligarchy states that all societies eventually become hierarchical, with top-down control, as they grow in size and complexity. We contribute to existing research arguing that this is not an inevitability. To do this, we analyse social systems that organise non-hierarchically (Self-Organised Social Systems (SOSS)). SOSS have a multi-scale architecture with respect to information abstraction, which is not tied to a hierarchical control structure, and allows for scalability. Through primary and secondary data collection, we analyse the structure and function of SOSS in the past and present, including neighbourhood collectives, activist and co-living groups (primary data, via interviews);and anarchist collectives in the Spanish Revolution, the Zapatistas, and the Occupy and 15M movements, among others (secondary data, from literature). We identify federations and networks as two initial types of SOSS that can scale in size and complexity. Key features include: (i) maintaining power at the lowest level;(ii) the quasi-autonomy of lower-level groups;(iii) sharing of skills, information and knowledge;(iv) adaptability across scales;(v) stability and uncertainty reduction;(vi) resilience. Future work will focus on questions of timing, scalar stress, and adaptability, to better understand how and why SOSS architectures succeed, fail, and can be implemented. © 2023 IEEE.

关键词： Feedback

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On the tradeoff between load balancing and energy-efficiency in hybrid ip/SDN networks 12

On the tradeoff between load balancing and energy-efficiency...

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12th International Conference on Network of the Future (NoF))

作者： Galan-Jimenez, Jaime Polverini, Marco Lavacca, Francesco G. Herrera, Juan Luis Berrocal, Javier Univ Extremadura Dpt Comp Syst & Telemat Engn Badajoz Spain Univ Rome Sapienza DIET Dept Rome Italy

ISBN: (纸本)9781665424349

The flexibility and programmability provided by the Software-Defined Networking (SDN) paradigm allow network operators to upgrade their legacy ip network infrastructures with the aim of improving their control over the network. However, the full migration from ip to SDN is not straightforward, at least, in the short term. Thus, transitory network infrastructures combining ip and SDN nodes (named hybrid ip/SDN networks) are required to coexist, while the correct coordination between paradigms is crucial to maintain the required Quality of Service. In this paper, two optimization problems that are normally solved separately due to their opposite nature: i) traffic load balancing and ii) the reduction of network power consumption, are jointly considered. In particular, an heuristic named Hybrid Spreading Load Algorithm (HSLA), is proposed to jointly minimize the Maximum Link Utilization (MLU) and the network power consumption during the transition from ip to SDN networks. Simulations over topologies of different size considering diverse selection methods for the replacement of the nodes reveal that HSLA outperforms other state-of-the-art approaches that specifically tackle only one objective, either the traffic load balancing or the reduction of the network power consumption.

关键词： hybrid ip/SDN load balancing energy efficiency traffic engineering heuristic

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Enhancing Security in dynamic IoT networks through Incremental Meta-learning

Enhancing Security in dynamic IoT networks through Increment...

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International Symposium on Advanced networks and Telecommunication Systems (ANTS)

作者： Megha Gopakumar Sriram Sankaran Centre for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798350391725

ISBN: (纸本)9798350391732

The widespread adoption of Internet of Things (IoT) devices presents numerous challenges, including security vulnerabilities, scalability, data management, and reliability concerns. Device identification is of paramount importance for addressing these challenges as it serves as a foundation for implementing security measures, a monitoring system to set up access control and regulate device usage within networks. Commonly used device identifiers such as MAC and ip addresses, are vulnerable to spoofing, highlighting the need for techniques such as network traffic-based device behavior analysis. Developing a device identification system for IoT is challenging due to dynamic nature of IoT environments. Further, identifying devices is exacerbated with limited network activity traces. Previous works rely on batch learning models that require extensive labeled data and are unable to adapt to real-time data changes. In this work, we develop an incremental meta-learning framework that uses machine learning to analyze and generalize device behaviors from network traces. Integrating incremental learning algorithms into the framework allows continuous adaptation to evolving data while using meta-learner model ensures efficient model training with limited data. The proposed model outperformed recent works with an accuracy of $\mathbf{8 9 . 5 4 \%}$ and provided consistent performance across five stages of incremental training.

关键词： Metalearning Training Adaptation models Heuristic algorithms Data models Real-time systems Internet of Things Object recognition Reliability Random forests

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