Fog Computing May Help to Save Energy in Cloud Computing

Jalali, Fahimeh; Hinton, Kerry; Ayre, Robert; Alpcan, Tansu; Tucker, R.S.

doi:10.1109/jsac.2016.2545559

Cited by 357 publications

(228 citation statements)

References 10 publications

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“…The experiment results are used for building power and performance models. The network energy consumption model is defined in a similar way in [25] and based on bit. These models were integrated into the simulator we developed in [13].…”

Section: B Renewable Energy and Esd Modelmentioning

confidence: 99%

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Orgerie

Rodero

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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Abstract-The emergence of Internet of Things (IoT) is participating to the increase of data-and energy-hungry applications. As connected devices do not yet offer enough capabilities for sustaining these applications, users perform computation offloading to the cloud. To avoid network bottlenecks and reduce the costs associated to data movement, edge cloud solutions have started being deployed, thus improving the Quality of Service. In this paper, we advocate for leveraging on-site renewable energy production in the different edge cloud nodes to green IoT systems while offering improved QoS compared to core cloud solutions. We propose an analytic model to decide whether to offload computation from the objects to the edge or to the core Cloud, depending on the renewable energy availability and the desired application QoS. This model is validated on our application use-case that deals with video stream analysis from vehicle cameras.

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Section: B Renewable Energy and Esd Modelmentioning

confidence: 99%

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Orgerie

Rodero

et al. 2017

2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)

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“…The method described from (8) results in a fast implementation in terms of execution time for d ≥ 0.3 [29]. It is however not able to utilize the full performance potential for low densities, as the total number of Bernoulli trials remains unchanged independently of the density.…”

Section: Methods 2: Sampling the Amount Of Packets To Combinementioning

confidence: 99%

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

et al. 2016

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Abstract:Given that next generation networks are expected to be populated by a large number of devices, there is a need for quick deployment and evaluation of alternative mechanisms to cope with the possible generated traffic in large-scale distributed data networks. In this sense, the Raspberry Pi has been a popular network node choice due to its reduced size, processing capabilities, low cost and its support by widely-used operating systems. For information transport, network coding is a new paradigm for fast and reliable data processing in networking and storage systems, which overcomes various limitations of state-of-the-art routing techniques. Therefore, in this work, we provide an in-depth performance evaluation of Random Linear Network Coding (RLNC)-based schemes for the Raspberry Pi Models 1 and 2, by showing the processing speed of the encoding and decoding operations and the corresponding energy consumption. Our results show that, in several scenarios, processing speeds of more than 80 Mbps in the Raspberry Pi Model 1 and 800 Mbps in the Raspberry Pi Model 2 are attainable. Moreover, we show that the processing energy per bit for network coding is below 1 nJ or even an order of magnitude less in these scenarios.

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“…As Cloud operations require large amount of continuous energy, different types of applications are investigated in [87] using Raspberry Pi based servers, which can be installed and configured as a Fog platform to reduce energy consumption. According to the results, applications that continuously produce static data within end-user premises and have low connection rate (e.g.…”

Section: Saving Energy In Cloud Computingmentioning

confidence: 99%

Fog computing security: a review of current applications and security solutions

2017

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Fog computing is a new paradigm that extends the Cloud platform model by providing computing resources on the edges of a network. It can be described as a cloud-like platform having similar data, computation, storage and application services, but is fundamentally different in that it is decentralized. In addition, Fog systems are capable of processing large amounts of data locally, operate on-premise, are fully portable, and can be installed on heterogeneous hardware. These features make the Fog platform highly suitable for time and location-sensitive applications. For example, Internet of Things (IoT) devices are required to quickly process a large amount of data. This wide range of functionality driven applications intensifies many security issues regarding data, virtualization, segregation, network, malware and monitoring. This paper surveys existing literature on Fog computing applications to identify common security gaps. Similar technologies like Edge computing, Cloudlets and Micro-data centres have also been included to provide a holistic review process. The majority of Fog applications are motivated by the desire for functionality and end-user requirements, while the security aspects are often ignored or considered as an afterthought. This paper also determines the impact of those security issues and possible solutions, providing future security-relevant directions to those responsible for designing, developing, and maintaining Fog systems.

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Fog Computing May Help to Save Energy in Cloud Computing

Cited by 357 publications

References 10 publications

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

Leveraging Renewable Energy in Edge Clouds for Data Stream Analysis in IoT

On Goodput and Energy Measurements of Network Coding Schemes in the Raspberry Pi

Fog computing security: a review of current applications and security solutions

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