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Relevant bibliographies by topics / Computer networks, SDN, network optimization, network resource

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Academic literature on the topic 'Computer networks, SDN, network optimization, network resource'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Computer networks, SDN, network optimization, network resource"

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MAJDOUB, MANEL, ALI EL KAMEL, and HABIB YOUSSEF. "DQR: An Efficient Deep Q-Based Routing Approach in Multi-Controller Software Defined WAN (SD-WAN)." Journal of Interconnection Networks 20, no. 04 (2020): 2150002. http://dx.doi.org/10.1142/s021926592150002x.

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Software Defined Networking (SDN) is a promising paradigm in the field of network technology. This paradigm suggests the separation between the control plane and the data plane which brings flexibility, efficiency and programmability to network resources. SDN deployment in large scale networks raises many issues which can be overcame using a collaborative multi-controller approaches. Such approaches can resolve problems of routing optimization and network scalability. In large scale networks, such as SD-WAN, routing optimization consists of achieving a trade-off between per-flow QoS, the load

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Guo, Aipeng, and Chunhui Yuan. "Network Intelligent Control and Traffic Optimization Based on SDN and Artificial Intelligence." Electronics 10, no. 6 (2021): 700. http://dx.doi.org/10.3390/electronics10060700.

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For telecom operators, it is of great significance to employ artificial intelligence (AI) and big data technology in a software-defined network (SDN) in order to achieve intelligent network control, traffic management and optimization. This paper proposes a solution for intelligent work control and traffic optimization. This paper is mainly focused on SDN-based network traffic algorithm optimization and experimental verification. In this paper, we design a network control mechanism for network intelligent control as well as solutions for traffic optimization based on SDN and artificial intelli

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Rowshanrad, Shiva, Mohamad Reza Parsaei, and Manijeh Keshtgari. "IMPLEMENTING NDN USING SDN: A REVIEW ON METHODS AND APPLICATIONS." IIUM Engineering Journal 17, no. 2 (2016): 11–20. http://dx.doi.org/10.31436/iiumej.v17i2.590.

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In recent years many claims about the limitations of todaysâ€™ network architecture, its lack of flexibility and ability to response to ongoing changes and increasing users demands. In this regard, new network architectures are proposed. Software Defined Networking (SDN) is one of these new architectures which centralizes the control of network by separating control plane from data plane. This separation leads to intelligence, flexibility and easier control in computer networks. One of the advantages of this framework is the ability to implement and test new protocols and architectures in actu

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Alomari, Amirah, Shamala K. Subramaniam, Normalia Samian, Rohaya Latip, and Zuriati Zukarnain. "Resource Management in SDN-Based Cloud and SDN-Based Fog Computing: Taxonomy Study." Symmetry 13, no. 5 (2021): 734. http://dx.doi.org/10.3390/sym13050734.

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Software-defined networks (SDN) is an evolution in networking field where the data plane is separated from the control plane and all the controlling and management tasks are deployed in a centralized controller. Due to its features regarding ease management, it is emerged in other fields such as cloud and fog computing in order to manage asymmetric communication across nodes, thus improving the performance and reducing the power consumption. This study focused on research that were conducted in SDN-based clouds and SDN-based fogs. It overviewed the important contributions in SDN clouds in term

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Peng, Fei, and Tianjie Cao. "Software-Defined Network Resource Optimization of the Data Center Based on P4 Programming Language." Mobile Information Systems 2021 (August 4, 2021): 1–7. http://dx.doi.org/10.1155/2021/3601104.

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This paper makes use of the new architecture software-defined network (SDN) in the cloud data center based on P4 language to realize the flexible management and configuration of the network equipment to achieve (a) data center virtualization management and (b) data center resource optimization based on the P4 programming language. Furthermore, error tolerance of dynamic network optimization depends on the virtual machine (VM) online migration technology, and the load balancing mechanism has a very good flexibility. At the same time, the paper proposed a multipath VM migration strategy based on

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Xue, Hai, Kyung Kim, and Hee Youn. "Dynamic Load Balancing of Software-Defined Networking Based on Genetic-Ant Colony Optimization." Sensors 19, no. 2 (2019): 311. http://dx.doi.org/10.3390/s19020311.

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Load Balancing (LB) is one of the most important tasks required to maximize network performance, scalability and robustness. Nowadays, with the emergence of Software-Defined Networking (SDN), LB for SDN has become a very important issue. SDN decouples the control plane from the data forwarding plane to implement centralized control of the whole network. LB assigns the network traffic to the resources in such a way that no one resource is overloaded and therefore the overall performance is maximized. The Ant Colony Optimization (ACO) algorithm has been recognized to be effective for LB of SDN a

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Ge, Mengmeng, Jin-Hee Cho, Dongseong Kim, Gaurav Dixit, and Ing-Ray Chen. "Proactive Defense for Internet-of-things: Moving Target Defense With Cyberdeception." ACM Transactions on Internet Technology 22, no. 1 (2022): 1–31. http://dx.doi.org/10.1145/3467021.

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Resource constrained Internet-of-Things (IoT) devices are highly likely to be compromised by attackers, because strong security protections may not be suitable to be deployed. This requires an alternative approach to protect vulnerable components in IoT networks. In this article, we propose an integrated defense technique to achieve intrusion prevention by leveraging cyberdeception (i.e., a decoy system) and moving target defense (i.e., network topology shuffling). We evaluate the effectiveness and efficiency of our proposed technique analytically based on a graphical security model in a softw

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Yeo, Sangho, Ye Naing, Taeha Kim, and Sangyoon Oh. "Achieving Balanced Load Distribution with Reinforcement Learning-Based Switch Migration in Distributed SDN Controllers." Electronics 10, no. 2 (2021): 162. http://dx.doi.org/10.3390/electronics10020162.

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Distributed controllers in software-defined networking (SDN) become a promising approach because of their scalable and reliable deployments in current SDN environments. Since the network traffic varies with time and space, a static mapping between switches and controllers causes uneven load distribution among controllers. Dynamic migration of switches methods can provide a balanced load distribution between SDN controllers. Recently, existing reinforcement learning (RL) methods for dynamic switch migration such as MARVEL are modeling the load balancing of each controller as linear optimization

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Abdelaal, Marwa A., Gamal A. Ebrahim, and Wagdy R. Anis. "Efficient Placement of Service Function Chains in Cloud Computing Environments." Electronics 10, no. 3 (2021): 323. http://dx.doi.org/10.3390/electronics10030323.

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The widespread adoption of network function virtualization (NFV) leads to providing network services through a chain of virtual network functions (VNFs). This architecture is called service function chain (SFC), which can be hosted on top of commodity servers and switches located at the cloud. Meanwhile, software-defined networking (SDN) can be utilized to manage VNFs to handle traffic flows through SFC. One of the most critical issues that needs to be addressed in NFV is VNF placement that optimizes physical link bandwidth consumption. Moreover, deploying SFCs enables service providers to con

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Safrianti, Ery, Linna Oktaviana Sari, and Rian Arighi Mahan. "Optimization of Universitas Riau Data Network Management Using Software Defined Network (SDN)." International Journal of Electrical, Energy and Power System Engineering 2, no. 3 (2019): 10–14. http://dx.doi.org/10.31258/ijeepse.2.3.10-14.

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Computer networks are one of the main parts in the telecommunications system. To support reliable network technology, a centralized network is needed so that network traffic can be managed more easily. Software-Defined Network (SDN) technology is a centralized network that provides a separation between control planes and data planes in different systems. This study discusses the optimization of network management at the University of Riau (UNRI) using SDN. Optimization is done by designing a UNRI computer network in the form of SDN then simulated using the Mininet. Quality of Service (QoS) ana

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Dissertations / Theses on the topic "Computer networks, SDN, network optimization, network resource"

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Hasan, Cengis. "Optimization of resource allocation in small cells networks : A green networking approach." Phd thesis, INSA de Lyon, 2013. http://tel.archives-ouvertes.fr/tel-01015735.

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The term "green networking" refers to energy-efficient networking technologies and products, while minimizing resource usage as possible. This thesis targets the problem of resource allocation in small cells networks in a green networking context. We develop algorithms for different paradigms. We exploit the framework of coalitional games theory and some stochastic geometric tools as well as the crowding game model. We first study the mobile assignment problem in broadcast transmission where minimal total power consumption is sought. A green-aware approach is followed in our algorithms. We exa

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Maldonado, López Ferney A. "Validation of availability and policy based management for programmable networks." Doctoral thesis, Universitat de Girona, 2017. http://hdl.handle.net/10803/666198.

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SDN is a network technology that separates control functions and the data plane. This separation allows flexibility in the management and use of network resources because the software is specialized in controlling the traffic and economic hardware oversees forwarding. Developers can build applications that control the detail of network and packet processing, from the autonomous configuration to complex operations which involve the context. However, the human factor represents between 50% and 80% of network failures due to errors and bugs in the programming of applications and the implementatio

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Li, Tao. "Conserve and Protect Resources in Software-Defined Networking via the Traffic Engineering Approach." 2020. https://tud.qucosa.de/id/qucosa%3A72444.

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Software Defined Networking (SDN) is revolutionizing the architecture and operation of computer networks and promises a more agile and cost-efficient network management. SDN centralizes the network control logic and separates the control plane from the data plane, thus enabling flexible management of networks. A network based on SDN consists of a data plane and a control plane. To assist management of devices and data flows, a network also has an independent monitoring plane. These coexisting network planes have various types of resources, such as bandwidth utilized to transmit monitoring data

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Jin, Jin. "On Improving Multi-channel Wireless Networks Through Network Coding and Dynamic Resource Allocation." Thesis, 2011. http://hdl.handle.net/1807/29766.

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Multi-channel wireless networks represent a direction that future state-of-the-art fourth generation (4G) wireless communication standards evolve towards. The IEEE 802.16 family of standards, or referred to as WiMAX, has emerged as one of the most important 4G networks to provide high speed data communication in metropolitan areas. There will be huge challenges in designing the networking protocols to allow WiMAX to provide high quality of services. How to effectively control the errors in the wireless channels and how to efficiently manage the scarce spectrum and power resources in different

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Book chapters on the topic "Computer networks, SDN, network optimization, network resource"

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Ali, Jehad, and Byeong-hee Roh. "Management of Software-Defined Networking Powered by Artificial Intelligence." In Computer-Mediated Communication [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.97197.

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Separating data and control planes by Software-Defined Networking (SDN) not only handles networks centrally and smartly. However, through implementing innovative protocols by centralized controllers, it also contributes flexibility to computer networks. The Internet-of-Things (IoT) and the implementation of 5G have increased the number of heterogeneous connected devices, creating a huge amount of data. Hence, the incorporation of Artificial Intelligence (AI) and Machine Learning is significant. Thanks to SDN controllers, which are programmable and versatile enough to incorporate machine learning algorithms to handle the underlying networks while keeping the network abstracted from controller applications. In this chapter, a software-defined networking management system powered by AI (SDNMS-PAI) is proposed for end-to-end (E2E) heterogeneous networks. By applying artificial intelligence to the controller, we will demonstrate this regarding E2E resource management. SDNMS-PAI provides an architecture with a global view of the underlying network and manages the E2E heterogeneous networks with AI learning.

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Raheel, Muhammad Salman, and Raad Raad. "Streaming Coded Video in P2P Networks." In Research Anthology on Recent Trends, Tools, and Implications of Computer Programming. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3016-0.ch060.

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This chapter discusses the state of the art in dealing with the resource optimization problem for smooth delivery of video across a peer to peer (P2P) network. It further discusses the properties of using different video coding techniques such as Scalable Video Coding (SVC) and Multiple Descriptive Coding (MDC) to overcome the playback latency in multimedia streaming and maintains an adequate quality of service (QoS) among the users. The problem can be summarized as follows; Given that a video is requested by a peer in the network, what properties of SVC and MDC can be exploited to deliver the video with the highest quality, least upload bandwidth and least delay from all participating peers. However, the solution to these problems is known to be NP hard. Hence, this chapter presents the state of the art in approximation algorithms or techniques that have been proposed to overcome these issues.

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Conference papers on the topic "Computer networks, SDN, network optimization, network resource"

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Hossen, Md Sazzad, and Abbas Jamalipour. "Network Resource Optimization in SDN-based Cellular Networks: A Traffic Steering Approach." In 2018 IEEE/CIC International Conference on Communications in China (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/iccchina.2018.8641251.

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Mahmood, Adnan, Bernard Butler, and Brendan Jennings. "Towards Efficient Network Resource Management in SDN-Based Heterogeneous Vehicular Networks." In 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2018. http://dx.doi.org/10.1109/compsac.2018.00133.

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Shi, Yi, Yalin E. Sagduyu, and Tugba Erpek. "Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing." In 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). IEEE, 2020. http://dx.doi.org/10.1109/camad50429.2020.9209299.

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