Software-Defined Networking for Big-Data Science - Architectural Models from Campus to the WAN

Monga, Inder; Pouyoul, Eric; Guok, Chin

doi:10.1109/sc.companion.2012.341

Cited by 46 publications

(19 citation statements)

References 1 publication

(2 reference statements)

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“…Specifically, the run-time network configuration for big data applications is studied to jointly optimize the network utilization and application performance. Monga et al in [392] introduce SDN to big scientific data architectural models while an approach to big data analysis in SDN/NFV-based 5G networks is introduced by Barona et al in [367]. However, while it will be challenging to meet QoS and QoE requirements and orchestrate VNFs without big data analytics, we note that the relationship between SDN, NFV, and big data is not yet studied, especially in the perspective of future networks.…”

Section: Intelligent Qoe-based Big Data Strategies In Future Softwmentioning

confidence: 97%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

162

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The highly demanding Over-The-Top (OTT) multimedia applications pose increased challenges to Internet Service Providers (ISPs) for assuring a reasonable Quality of Experience (QoE) to their customers due to lack of flexibility, agility and scalability in traditional networks. The future networks are shifting towards the cloudification of the network resources via Software Defined Networks (SDN) and Network Function Virtualization (NFV). This will equip ISPs with cutting-edge technologies to provide service customization during service delivery and offer QoE which meets customers' needs via intelligent QoE control and management approaches. Towards this end, we provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a highlevel description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoEoriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

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Section: Intelligent Qoe-based Big Data Strategies In Future Softwmentioning

confidence: 97%

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

Barakabitze

Barman

Ahmad

et al. 2020

IEEE Commun. Surv. Tutorials

162

View full text Add to dashboard Cite

show abstract

“…When using the good features of SDN in a Big Data environment, it is expected to be able to benefit its applications in several visible aspects such as those shown in Figure 10, including: (1) Big data processing in Cloud DCs, (2) improvement in data delivery, (3) runtime programming for application optimization, (4) Big Data scientific architectures and (5) Hadoop programming 28 [54]. In this regard [55], [56], [57], [58], [59] include the benefits provided by SDN characteristics for Big data applications.…”

Section: Big Datamentioning

confidence: 99%

State of the art in software defined networking (SDN)

Pérez¹,

Losada²,

Rojas³

et al. 2019

Vis. Electron.

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The growth of networks at a global level is inevitable due to the increase of users, devices and applications, such as: Internet of Things (IoT), processing and analysis of large amounts of information (Big Data), or streaming audio and video, which has demanded from the systems, greater storage resources and bandwidth. To this purpose, diverse paradigms have emerged for the centralized management of all the components of a network through fully administrable, centralized and dynamic technological platforms; among these is SDN (Software-Defined Networks). This document, consequently, establishes the state-of-art from a documentary research of a categorical type to be used as a frame of reference for research in the area of SDN by the Research Group of New Technologies of Social Application GIDENUTAS ascribed to the University Francisco Jose de Caldas. This is chronologically limited to a review, from 2007 until today, focused on the countries that have promoted the development and implementation of this new paradigm, using databases such as IEEE Xplore, Google Scholar, as well as documents from standardization organizations such as ONF and ITU.

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“…When the failure occurs, controllers can reassign the node to avoid the latency. The major problems in the software defined networks are the controller placement, delay, power consumption, selection of controllers and load imbalance [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Single and Multi-Type Controllers with Soft Computing Methods and Routing in Software Defined Network

2019

IJEAT

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The software defined networking topology is enabled for efficient configuration for monitoring and enhance the network performance. The multi-controller placement becomes very critical, and it can be rectified by the proposed approach. The comparison of single type and the multi controllers are discussed in this paper.The first paper is compared with the dragonfly algorithm, in this we have considered only multi POX controllers. So the latency difference depends on the iteration of the algorithm. Thus the latency is reduced in proposed algorithm. Similarly the second paper has compared with the existing algorithm of firefly, which states the multi type of controllers such as POX, NOX, BEACON and FLOODLIGHT, among that latency is calculated and compared. The selection criteria are varied according to its type. Hence the routing is carried out on the two controllers alike POX, and NOX, through ad hoc on demand distance vector routing protocol. Subsequently, the selected controllers specifically are placed and simulated in Pareto-based Optimal Controller placement (POCO) tool for the Internet 2 topology and network simulator(NS2). Finally the comparison results makes the proposed system is better than previous methods.

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Software-Defined Networking for Big-Data Science - Architectural Models from Campus to the WAN

Cited by 46 publications

References 1 publication

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

State of the art in software defined networking (SDN)

Single and Multi-Type Controllers with Soft Computing Methods and Routing in Software Defined Network

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