Design and development of management functions for distributed monitoring based on SDN-based network

Adami, Davide; Donatini, Lisa; Foddis, Gianluca; Giordano, Stefano; Roma, Simone; Topazzi, Simone

doi:10.1109/emtc.2014.6996655

Cited by 3 publications

(2 citation statements)

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“…Last but not least, the management of network functions for distributed monitoring is an important factor for the correct operation of SDN-based networks. An extended analysis was already performed [62], with some proposals for proper data synchronization. However, despite the large amount of contributions in the autonomous management context for communication networks, the proposed models focus its use in centralized management systems.…”

Section: Distributed Autonomous Management In Sdnmentioning

confidence: 99%

ArchSDN: a reinforcement learning-based autonomous OpenFlow controller with distributed management properties

2020

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The scalling capability of network controllers is important for network operators to have the confidence to continue to expand the communication network. Today's networks can use the Software Defined Networks (SDN) concept to separate the control and the switching planes, and use autonomous self-management features to quickly react to any network event, while providing the network with necessary agility to support custom Service Level Agreement (SLA) to its clients, while lowering the capital and operational expenditures. Both SDN and autonomous management concepts have a centralized architecture, requiring a single entity managing every aspect of the network, making it difficult to manage an entire network in the massive IoT world, where services tend to be provided close to the users, and edgebased approaches are key to the support of 5G and beyond services. The support of such dynamic and flexible network requires an approach where the network management responsibilities are distributed through multiple autonomous OpenFlow controllers, which we denote as ArchSDN controllers. By assigning OpenFlow switches to different ArchSDN controllers, the network becomes divided into different sectors, each controlled exclusivly by one ArchSDN controller. These controllers, coordinate their actions and use a reinforcement-learning based decision mechanism to explore, learn and implement near-to-optimum end-to-end communication paths. The evaluation results show that the proposed decision system is capable of finding near-to-optimum solutions, learn from the obtained results to improve future service activation results, and quickly adapth the network to the loss of a communication link by responding in less than 100 ms.

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Section: Distributed Autonomous Management In Sdnmentioning

confidence: 99%

ArchSDN: a reinforcement learning-based autonomous OpenFlow controller with distributed management properties

2020

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“…OpenSAFE by Ballard et al [27] utilized OpenFlow-based SDN to route selected traffic to monitoring appliances for deeper analysis without impacting network performance. Donatini et al [33,34] developed an SDN-based monitoring platform for 4G networks to detect issues causing complexity and high bandwidth. Youssaf et al described technologies [35] These tools have mainly focused on quality and performance issues in general.…”

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confidence: 99%

Security Awareness in Software-Defined Multi-Domain 5G Networks

et al. 2018

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Fifth generation (5G) technologies will boost the capacity and ease the management of mobile networks. Emerging virtualization and softwarization technologies enable more flexible customization of network services and facilitate cooperation between different actors. However, solutions are needed to enable users, operators, and service providers to gain an up-to-date awareness of the security and trustworthiness of 5G systems. We describe a novel framework and enablers for security monitoring, inferencing, and trust measuring. The framework leverages software-defined networking and big data technologies to customize monitoring for different applications. We present an approach for sharing security measurements across administrative domains. We describe scenarios where the correlation of multi-domain information improves the accuracy of security measures with respect to two threats: end-user location tracking and Internet of things (IoT) authentication storms. We explore the security characteristics of data flows in software networks dedicated to different applications with a mobile network testbed.

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Monitoring and Traffic Optimization Using Vertical Controller in Multi-domain SDN

Chaturvedi

Somwanshi

Bundele

et al. 2020

Algorithms for Intelligent Systems

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Design and development of management functions for distributed monitoring based on SDN-based network

Cited by 3 publications

References 3 publications

ArchSDN: a reinforcement learning-based autonomous OpenFlow controller with distributed management properties

ArchSDN: a reinforcement learning-based autonomous OpenFlow controller with distributed management properties

Security Awareness in Software-Defined Multi-Domain 5G Networks

Monitoring and Traffic Optimization Using Vertical Controller in Multi-domain SDN

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