Recursive SDN for Carrier Networks

McCauley, James; Liu, Zhi; Panda, Aurojit; Koponen, Teemu; Raghavan, Barath; Rexford, Jennifer; Shenker, Scott

doi:10.1145/3027947.3027948

Cited by 18 publications

(10 citation statements)

References 24 publications

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“…Although this algorithm was not applied in an SDN architecture, it provided a new reference direction for subsequent routing optimization algorithms. Lin et al [ 21 ] introduced multi-layer SDN QoS-aware adaptive routing, combining the work of Hassas [ 22 ] and McCauley [ 23 ], introducing a distributed control plane architecture, and achieving a reliable SDN infrastructure with minimal signal latency. Rischke et al [ 24 ] proposed the QR-SDN algorithm to create multiple paths between source and destination addresses, maintaining flow integrity using Q-Learning, and employing the softmax function as an exploration-exploitation strategy.…”

Section: Related Workmentioning

confidence: 99%

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

Gong,

Ren,

Wang

et al. 2024

Sensors

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Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

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Section: Related Workmentioning

confidence: 99%

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

Gong,

Ren,

Wang

et al. 2024

Sensors

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show abstract

“…A recursive building block known as a logical xBar, which is a programmable entity that can switch packets between ports, and a logical server, which manages forwarding tables and control plane computations, are used to create a centralized hierarchical control plane [326].…”

Section: Control Modelsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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Traditional networking is hardware-based, having the control plane coupled with the data plane. Software-Defined Networking (SDN), which has a logically centralized control plane, has been introduced to increase the programmability and flexibility of networks. Knowledge-Defined Networking (KDN) is an advanced version of SDN that takes one step forward by decoupling the management plane from control logic and introducing a new plane, called a knowledge plane, decoupled from control logic for generating knowledge based on data collected from the network. KDN is the next-generation architecture for self-learning, self-organizing, and self-evolving networks with high automation and intelligence. Even though KDN was introduced about two decades ago, it had not gained much attention among researchers until recently. The reasons for delayed recognition could be due to the technology gap and difficulty in direct transformation from traditional networks to KDN. Communication networks around the globe have already begun to transform from SDNs into KDNs. Machine learning models are typically used to generate knowledge using the data collected from network devices and sensors, where the generated knowledge may be further composed to create knowledge ontologies that can be used in generating rules, where rules and/or knowledge can be provided to the control, management, and application planes for use in decision-making processes, for network monitoring and configuration, and for dynamic adjustment of network policies, respectively. Among the numerous advantages that KDN brings compared to SDN, enhanced automation and intelligence, higher flexibility, and improved security stand tall. However, KDN also has a set of challenges, such as reliance on large quantities of high-quality data, difficulty in integration with legacy networks, the high cost of upgrading to KDN, etc. In this survey, we first present an overview of the KDN architecture and then discuss each plane of the KDN in detail, such as sub-planes and interfaces, functions of each plane, existing standards and protocols, different models of the planes, etc., with respect to examples from the existing literature. Existing works are qualitatively reviewed and assessed by grouping them into categories and assessing the individual performance of the literature where possible. We further compare and contrast traditional networks and SDN against KDN. Finally, we discuss the benefits, challenges, design guidelines, and ongoing research of KDNs. Design guidelines and recommendations are provided so that identified challenges can be mitigated. Therefore, this survey is a comprehensive review of architecture, operation, applications, and existing works of knowledge-defined networks.

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“…To simulate realistic SDNs, we use the Rocketfuel topologies [34], which are used in many recent publications on SDN such as [5], [6], [35], [36], [37], [38] as well as fat-tree topologies [39] that are typically used in data-center networks. We present the results based on the Rocketfuel topology AS1239 with 1944 links and 315 nodes, for a capacity of 12 for every link and results from an 8-ary fat-tree with 1280 links and 256 server nodes and a link capacity of 25.…”

Section: Evaluation a Test Environmentmentioning

confidence: 99%

Fast Network Configuration in Software Defined Networking

Achleitner

Bartolini

et al. 2018

IEEE Trans. Netw. Serv. Manage.

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Software Defined Networking (SDN) provides a framework to dynamically adjust and re-program the data plane with the use of flow rules. The realization of highly adaptive SDNs with the ability to respond to changing demands or recover after a network failure in a short period of time, hinges on efficient updates of flow rules. We model the time to deploy a set of flow rules by the update time at the bottleneck switch, and formulate the problem of selecting paths to minimize the deployment time under feasibility constraints as a mixed integer linear program (MILP). To reduce the computation time of determining flow rules, we propose efficient heuristics designed to approximate the minimum-deployment-time solution by relaxing the MILP or selecting the paths sequentially. Through extensive simulations we show that our algorithms outperform current, shortest path based solutions by reducing the total network configuration time up to 55% while having similar packet loss, in the considered scenarios. We also demonstrate that in a networked environment with a certain fraction of failed links, our algorithms are able to reduce the average time to reestablish disrupted flows by 40%.

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Recursive SDN for Carrier Networks

Cited by 18 publications

References 24 publications

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network

A Comprehensive Survey on Knowledge-Defined Networking

Fast Network Configuration in Software Defined Networking

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