FlowVista: Low-bandwidth SDN monitoring driven by business application interaction

Siniarski, Bartlomiej; Murphy, John; Delaney, Declan

doi:10.23919/softcom.2017.8115539

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…In [544], an intent-based northbound interface with micro-services, and service-oriented design principles having a three-tier application architecture and domain-driven design is proposed to define business-like applications. A framework known as FlowVista, which identifies flows using the Northbound interface, where flows are matched directly by interacting with a business application, translates high-level business policies into low-level network flows [545]. Voice Over IP (VoIP) is a very good example for the realization of FlowVista, as VoIP is driven by high-level business policies.…”

Section: Business Applicationsmentioning

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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Section: Business Applicationsmentioning

confidence: 99%

A Comprehensive Survey on Knowledge-Defined Networking

Wijesekara

Gunawardena

2023

Telecom

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“…The authors in [83] proposed FlowVisa as a plugin module in the control plane. It is used to identify traffic flows that belong to the critical application.…”

Section: ) Active Vs Passivementioning

confidence: 99%

A Survey on Autonomic Provisioning and Management of QoS in SDN Networks

BinSahaq

Sheltami

2019

IEEE Access

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In today's Internet, killer network services and applications, such as video and audio streaming, network storage, and online video games, are pushing the network infrastructure resources to the edge. By design and for the most part, the Internet is the best offer delivery ecosystem with little or no endto-end quality-of-service (QoS) guarantees. Even, frameworks, such as IntServ and DiffServ that were designed and implemented to provide QoS guarantees, still fail to solve this problem at a wide scale. Software-defined networking (SDN) is a fast emerging networking paradigm that promises to provide endto-end QoS guaranteeing by offering greater network flexibility, abstraction, control, and programmability to network resources. In this paper, we review, survey, and discuss the current state of the art on QoS provisioning in the area of SDN, with respect to applying the concept of autonomic computing (AC) to automatically support, provision, monitor, and maintain QoS requirements. This paper includes in-depth classification, taxonomy, and comparative analysis for the autonomic-based QoS provisioning in accordance with the famous influential and widely adopted the monitor-analyze-plan-execute-knowledge (MAPE-K) IBM architectural model for autonomic computing.

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VoIP Traffic Management Using a Software-Defined Networking Approach

Vieira

Fiorese

2019

Advanced Information Networking and Applications

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FlowVista: Low-bandwidth SDN monitoring driven by business application interaction

Cited by 6 publications

References 4 publications

A Comprehensive Survey on Knowledge-Defined Networking

A Comprehensive Survey on Knowledge-Defined Networking

A Survey on Autonomic Provisioning and Management of QoS in SDN Networks

VoIP Traffic Management Using a Software-Defined Networking Approach

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