Towards a Multi-Tier Fog/Cloud Architecture for Video Streaming

Gama, Eduardo S.; Immich, Roger; Bittencourt, Luiz F.

doi:10.1109/ucc-companion.2018.00022

Cited by 26 publications

(18 citation statements)

References 3 publications

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“…This work is limited to small-scale scenarios and considers a single VNF type in a single-tier fog nodes. Also, work in [15] presents a multi-tier fog architecture for video streaming applications. Namely, it is composed of three tiers that are classified in terms of coverage, computing and storage capacities.…”

Section: Related Workmentioning

confidence: 99%

“…For example, the LSTM-based provisioning scheme features approximately 90-97% success rates for the first 150 incoming requests, when implemented on the proposed HF architecture. Meanwhile, the conventional SFC provisioning scheme accommodates 85 and 95 requests out of the first 150 incoming requests, when mapping VNFs on the proposed heterogeneous fog and hybrid fog-cloud architectures in [10]- [15], respectively, i.e., approximately 56% and 65% success rates. It is observed here that the conventional scheme starts to saturate the network after 150 incoming success, which results in dropping subsequent incoming traffic from terminals.…”

Section: A Number Of Successful Requestsmentioning

confidence: 99%

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Deep Learning for Service Function Chain Provisioning in Fog Computing

et al. 2020

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Cloud and fog computing along with network function virtualization technology have significantly shifted the development of network architectures. They yield in reduced capital and operating expenditures, while achieving network flexibility and scalability to accommodate the massive growth in data traffic volumes from user terminals requesting various services and applications. Now cloud solutions here offer abundant computing and storage resources, at the detriment of high end-to-end delays, hence limiting quality of service for delay-sensitive applications. Meanwhile, fog solutions offer reduced delays, at the detriment of limited resources. Existing efforts focus on merging the two solutions and propose multi-tier hybrid fog-cloud architectures to leverage their both saliencies. However, these approaches can be inefficient when the applications are delay-sensitive and require high resources. Hence this work proposes a novel standalone heterogeneous fog architecture that is composed of high-capacity and low-capacity fog nodes, both located at the terminals proximity. Thereby, realizing a substrate network that offers reduced delays and high resources, without the need to relay to the cloud nodes. Moreover, the work here leverages and deploys a deep learning network to propose a service function chain provisioning scheme implemented on this architecture. The scheme predicts the popular network functions, and maps them on the high-capacity nodes, whereas it predicts the unpopular network functions and maps them on the low-capacity nodes. The goal is to predict the next incoming function and prefetch it on the node. Hence, when a future request demands the same function, it can be cached directly from the node, at reduced resources consumption, processing times, cost, and energy consumption. Also, this yields in higher number of satisfied requests and increased capacity. The deep learning network yields reduced loss model and high success rates.

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Section: Related Workmentioning

confidence: 99%

Section: A Number Of Successful Requestsmentioning

confidence: 99%

Deep Learning for Service Function Chain Provisioning in Fog Computing

et al. 2020

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“…This work is limited to smallscale scenarios and considers a single VNF type in a single fog layer. Authors in [22] present a multi-layer fog and cloud architecture for video streaming applications, as opposed to single layer fog solutions. Three layers are classified here in terms of their coverage, computing and storage capacities.…”

Section: B Survey Of Sfc Provisioning In Hybrid Fog-cloud Architecturesmentioning

confidence: 99%

Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures

et al. 2020

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Network function virtualization (NFV) technology enables service providers to implement software-based network processing functionalities on standard computing servers (nodes). As such, this approach mandates the need for mapping virtual network functions (VNFs) in service function chains (SFCs) on these nodes for incoming service requests. Now traditional VNF mapping schemes use cloud nodes for its abundant available resources, at the detriment of prolonged network delays. Alternative schemes use fog nodes that return reduced delays, at the detriment of limited resources. Hence in this work, a novel SFC provisioning scheme is proposed for a hybrid fog-cloud architecture of various resources. The architecture is composed of a single fog and single cloud layer, in order to accommodate both delay-sensitive and delay-tolerant requirements for large number of incoming requests, respectively. This scheme yields a tradeoff between standalone cloud and fog solutions when implemented on the proposed hybrid architecture, in terms of the number of satisfied requests, network delay, resources consumption, energy consumption, and realization cost at large traffic volumes. The proposed scheme achieves 15-40% higher traffic capacity than fog solutions, 21-43% reduced delay, 45-52% less energy consumption levels and 28-30% less cost as compared to cloud solutions.INDEX TERMS Cloud computing, fog computing, hybrid fog-cloud architecture, network function virtualization, service function chain

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“…Outra vantagem da localidade dos dadosé a possibilidade de fazer o descarregamento de operações, tais como processamento ou armazenamento. Mesmo com o avanço nas tecnologias móveis, em algumas situações aindaé vantajoso transferir o processamento do próprio dispositivo para a borda da rede visando uma economia de bateria e a redução no tempo de processamento [S. Gama et al 2018]. A computação em névoa auxilia nesta atividade provendo camadas de recursos entre os usuários e a nuvem [Yi et al 2015, Shi et al 2016].…”

Section: Gerenciamento E Localidade Dos Dadosunclassified

Computação Urbana da Teoria à Prática: Fundamentos, Aplicações e Desafios

Rodrigues¹,

Santos²,

Filho³

et al. 2019

Minicursos Do XXXVII Simpósio Brasileiro De Redes De Computadores E Sistemas Distribuídos

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The growing of cities has resulted in innumerable technical and managerial challenges for public administrators such as energy consumption, pollution, urban mobility and even supervision of private and public spaces in an appropriate way. Urban Computing emerges as a promising paradigm to solve such challenges, through the extraction of knowledge, from a large amount of heterogeneous data existing in urban space. Moreover, Urban Computing correlates urban sensing, data management, and analysis to provide services that have the potential to improve the quality of life of the citizens of large urban centers. Consider this context, this chapter aims to present the fundamentals of Urban Computing and the steps necessary to develop an application in this area. To achieve this goal, the following questions will be investigated, namely: (i) What are the main research problems of Urban Computing?; (ii) What are the technological challenges for the implementation of services in Urban Computing?; (iii) What are the main methodologies used for the development of services in Urban Computing?; and (iv) What are the representative applications in this field? ResumoA rápida urbanização das cidades vem resultando em inúmeros desafios técnicos e gerenciais para os gestores públicos tais como consumo de energia, poluição, mobilidade urbana e até mesmo gestão de espaços privados e públicos de forma apropriada. A Computação Urbana surge como um paradigma promissor para resolver tais desafios,

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Towards a Multi-Tier Fog/Cloud Architecture for Video Streaming

Cited by 26 publications

References 3 publications

Deep Learning for Service Function Chain Provisioning in Fog Computing

Deep Learning for Service Function Chain Provisioning in Fog Computing

Delay-Aware SFC Provisioning in Hybrid Fog-Cloud Computing Architectures

Computação Urbana da Teoria à Prática: Fundamentos, Aplicações e Desafios

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