Customizable Autonomic Network Management: Integrating Autonomic Network Management and Software-Defined Networking

Tsagkaris, Kostas; Logothetis, Marios; Foteinos, Vassilis; Poulios, George; Michaloliakos, Michalis; Demestichas, Panagiotis

doi:10.1109/mvt.2014.2380633

Cited by 27 publications

(22 citation statements)

References 8 publications

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“…A number of approaches exist in the current literature, as efforts to combine ANM with SDN [2,6,26], and the most recent of them also with NFV [25]. However, none of them focuses on D2D communications in CRN environments, as proposed by our approach, combining SDR with ANM-SDN-NFV concepts.…”

Section: Architectures Combining Anm With Sdn/nfvmentioning

confidence: 99%

“…AUTOFLOW [2] presented an experimentation framework that combined ANM and SDN components, using the GÉANT OpenFlow facility. The framework contained three ANM core blocks, including governance, coordination and knowledge, implementing ACLs, including traffic engineering and load (congestion) prediction.…”

Section: Architectures Combining Anm With Sdn/nfvmentioning

confidence: 99%

“…In this paper, we jointly address the above considerations, by focusing on the interplay between SDN-SDR in distributed wireless networks operating in highly dynamic environments using NFV as a convergence substrate enabling the SDN-SDR interplay. The goal of QoS-aware ubiquitous connectivity and efficient use/reuse of resources requires a network architecture with advanced intelligence, capable of sensing its operational conditions and adapting its configuration accordingly [1,2]. To accommodate those needs, we argue that a future wireless distributed network architecture should utilize principles of autonomic systems in network management dictated by a consummated implementation and inter-dependence of SDN, SDR and NFV.…”

Section: Introductionmentioning

confidence: 99%

“…Each secondary node pair communicates through common available channels, transparently to the primary network. Furthermore, a SU collects information from other SUs within its transmission radius and forms the vector n s = n (1) n (2)…”

mentioning

confidence: 99%

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Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

et al. 2019

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The demand for Autonomic Network Management (ANM) and optimization is as intense as ever, even though significant research has been devoted towards this direction. This paper addresses such need in Software Defined (SDR) based Cognitive Radio Networks (CRNs). We propose a new framework for ANM and network reconfiguration combining Software Defined Networks (SDN) with SDR via Network Function Virtualization (NFV) enabled Virtual Utility Functions (VUFs). This is the first approach combining ANM with SDR and SDN via NFV, demonstrating how these state-of-the-art technologies can be effectively combined to achieve reconfiguration flexibility, improved performance and efficient use of available resources. In order to show the feasibility of the proposed framework, we implemented its main functionalities in a cross-layer resource allocation mechanism for CRNs over real SDR testbeds provided by the Orchestration and Reconfiguration Control Architecture (ORCA) EU project. We demonstrate the efficacy of our framework, and based on the obtained results, we identify aspects that can be further investigated for improving the applicability and increasing performance of our broader framework.

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Section: Architectures Combining Anm With Sdn/nfvmentioning

confidence: 99%

Section: Architectures Combining Anm With Sdn/nfvmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

et al. 2019

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“…In fact, SDN and Autonomic Network Management (ANM) are related in the sens that they both seek to simplify and improve the management of complex heterogeneous networks, hence reducing the operational cost and delivering better quality of service to the clients [17]. Furthermore, SDN can be used as a mean to facilitate the introduction of automation and intelligent control loops in complex networks with multiple technologies, layers and vendors.…”

Section: B Software Defined Network For Son Managementmentioning

confidence: 99%

Softwarized and distributed learning for SON management systems

Daher

Jemaa

Decreusefond

2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Self-Organizing Networks (SON) functions have already proven to be useful for network operations. However, a higher automation level is required to make a network enabled with SON capabilities respond as a whole to the operator's objectives. For this purpose, a Policy Based SON Management (PBSM) layer has been proposed to manage the deployed SON functions. In this paper, we propose to empower the PBSM with cognition capability in order to manage efficiently SON enabled networks. We focus particularly on the implementation of such a Cognitive PBSM (C-PBSM) on a large scale network and propose a scalable approach based on distributed Reinforcement Learning (RL): RL agents are deployed on different clusters of the network. These clusters should be defined in such a way that the RL agents can learn independently. As the interaction between these clusters may evolve in time due for instance to traffic dynamics, we propose a flexible implementation of this C-PBSM framework with dynamic clustering to adapt to network's evolutions. We show how this flexible implementation is rendered possible under Software Defined Networks (SDN) framework. We also assess the performance of the proposed distributed learning approach on an LTE-A simulator.

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ConForm: In-Band Control Flows Self-establishment with Integrated Topology Discovery to SDN-Based Networks

Freitas

Rosa

Silva

2020

Advances in Intelligent Systems and Computing

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Customizable Autonomic Network Management: Integrating Autonomic Network Management and Software-Defined Networking

Cited by 27 publications

References 8 publications

Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

Autonomic Network Management and Cross-Layer Optimization in Software Defined Radio Environments

Softwarized and distributed learning for SON management systems

ConForm: In-Band Control Flows Self-establishment with Integrated Topology Discovery to SDN-Based Networks

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