Nodes Discovery in the In-Network Management Communication Framework

Guardalben, Lucas; Gomes, Tome; Pinho, António; Salvador, Paulo; Sargento, Susana

doi:10.1007/978-3-642-30422-4_11

Cited by 4 publications

(5 citation statements)

References 12 publications

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“…Although this entity is unique in the network, it is perfectly possible to replicate the ArchSDN central manager and embed it in each ArchSDN controller. Previously published work on dissemination of information through multiple agents in a network provides the base-ground for such replication [66][67][68].…”

Section: Archsdn Central Managermentioning

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: Archsdn Central Managermentioning

confidence: 99%

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

2020

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“…For the discovery process, we propose the Hide and Seek (H&S) mechanism [1,2]. In order to create and gather information of surrounding neighbours, the devices send HELLO contact messages to its neighbourhood using a pre-defined depth.…”

Section: Ned Function For Discoverymentioning

confidence: 99%

“…The current trend of distributed management demands the embedding of management functionalities within the network nodes [1,2]. However, this embedding requires the network nodes to have sufficient information to take management decisions in an efficient and cooperative way.…”

Section: Introductionmentioning

confidence: 99%

Supporting Unified Distributed Management and Autonomic Decisions: Design, Implementation and Deployment

et al. 2016

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Nowadays, the prevailing use of networks based on traditional centralized management systems reflects on a fast increase of the management costs. The growth in the number of network equipments and services reinforces the need to distribute the management responsibilities throughout the network devices. In this approach, each device executes common network management functionalities, being part of the overall network management platform. In this paper, we present a Unified Distributed Network Management (UDNM) framework that provides a unified (wired and wireless) management network solution, where further different network services can take part of this infrastructure, e.g., flow monitoring, accurate routing decisions, distributed policies dissemination, etc. This framework is divided in two main components: (A) Situation awareness, which sets up initial information through bootstrapping, discovery, fault-management process and exchange of & Lucas Guardalben

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“…The depth is the number of hops to disseminate the information, which ensures the possibility to define how long a device wants to disseminate the management information in the network. For discovery process, the Hide and Seek (H&S) concept was proposed and integrated to work with the NED function [13], [14], in order to create and gather information of surrounding neighbors. The work previously developed in [13] considers the NED applicability only in static wired and small scenarios.…”

Section: Related Workmentioning

confidence: 99%

On the analysis of dissemination management information through an Eyesight perspective

Guardalben

Condeixa

Gomes

et al. 2013

2013 IEEE Globecom Workshops (GC Wkshps)

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The dissemination of information in wireless networks is a fundamental aspect in several network processes, such as routing, monitoring and management. Nowadays, most of the techniques for dissemination of management information adopt controlled flooding approaches, which usually impairs the network performance. In this sense, the optimization of the dissemination of management information for dense wireless scenarios remains a relevant research challenge. In this paper, it is presented and analyzed the performance of the Neighbors Eyesight Direction (NED) dissemination against to OSPF, uncontrolled flooding and Gossip approaches in wired and large network scenarios. NED is able to work on wired and dense wireless scenarios, showing to be more efficient than the baseline approaches. With lower dissemination cost and lesser recursive cycles, NED proved to be scalable to disseminate management information for scenarios with a large number of devices.

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Nodes Discovery in the In-Network Management Communication Framework

Cited by 4 publications

References 12 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

Supporting Unified Distributed Management and Autonomic Decisions: Design, Implementation and Deployment

On the analysis of dissemination management information through an Eyesight perspective

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