Bringing the Cloud to Rural and Remote Areas via Cloudlets

Helmer, Sven; Pahl, Claus; Sanin, Julian; Miori, Lorenzo; Brocanelli, Stefan; Cardano, Filippo; Gadler, Daniele; Morandini, Daniel; Piccoli, Alessandro; Salam, Saifur; Sharear, Alam Mahabub; Ventura, Angelo; Abrahamsson, Pekka; Oyetoyan, Tosin Daniel

doi:10.1145/3001913.3001918

Cited by 11 publications

(5 citation statements)

References 29 publications

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“…To show that the proposed architecture can meet the requirements of edge computing, such as cost-efficiency, and low power consumption, the researchers implemented their solution on a cluster of Raspberry Pi (RPi) devices, which are resource-limited devices. Researchers in [408] have looked at the problem of bringing cloud services to rural and remote areas in developing countries, where building large and expensive data centers may not be feasible. They propose a hardware platform based on a cluster of single-board computer (e.g., RPi) for making cloudlet nodes in rural and remote areas that offer cloud services.…”

Section: Experiments With Single-board Computers As Enablers Ofmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,116

613

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With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of securitycritical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

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Section: Experiments With Single-board Computers As Enablers Ofmentioning

confidence: 99%

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Yousefpour

Fung

Nguyen

et al. 2019

Journal of Systems Architecture

1,116

613

View full text Add to dashboard Cite

show abstract

“…A relevant resilience challenge deals with network disconnection between components of different IoT stages, such as thing, mist, fog, or cloud [16], [114][115][116][117][118][119][120][121]. Therefore, it is necessary to establish mechanisms to ensure resilience to fog-based IoT systems for data transmission [16], [122], such as fault management mechanisms for IoT nodes [16].…”

Section: ) Resiliencementioning

confidence: 99%

A Survey on Trustworthiness for the Internet of Things

Ribeiro

Kamienski

2021

IEEE Access

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IoT systems use sensors to collect data from smart environments and manage resources through data analysis. An IoT system deals with many connected devices with different network and hardware constraints in a real-world scenario. An IoT system needs to handle low-latency data analysis, security threats, internal vulnerabilities, and network disconnections, which cause data loss and incorrect decisions. Trustworthiness (also known as dependability) provides various features for an IoT end-to-end data flow, such as resilience, security, availability, reliability, scalability, maintainability, heterogeneity, hardware resources management, fault management policies, and data quality. This paper presents a survey on trustworthiness and dependability in IoT systems and proposes the Trustworthiness for IoT Framework (TW-IoT) to provide trustworthiness at the data level for mist and fog-based IoT systems. The TW-IoT framework provides data trustworthiness to ensure a continuous and uninterrupted operation of IoT data flow. We also discuss challenges and trade-offs related to data trustworthiness in IoT.

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“…SBCs have inspired researchers to build novel hardware configurations for a range of uses cases such as edge compute [8], [9] expendable compute [10], [11], and portable clusters which are difficult to implement using traditional hardware.…”

Section: S B C Amentioning

confidence: 99%

Next generation single board clusters

Singer

Herry

Basford

et al. 2018

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

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Abstract-Until recently, cluster computing was too expensive and too complex for commodity users. However the phenomenal popularity of single board computers like the Raspberry Pi has caused the emergence of the single board computer cluster. This demonstration will present a cheap, practical and portable Raspberry Pi cluster called Pi Stack. We will show pragmatic custom solutions to hardware issues, such as power distribution, and software issues, such as remote updating. We also sketch potential use cases for Pi Stack and other commodity single board computer cluster architectures. I. I15 million Raspberry Pi single board computer (SBC) devices have been sold to date1. The pressing question is: how are people deploying such SBCs? One appealing answer is to network several boards together to produce a micro-cluster environment. This is highly feasible, since each SBC runs a full Linux stack and has conventional ethernet interconnect, effectively enabling the creation of a Beowulf cluster.The key problem is that per-node resource availability is highly constrained, in terms of processing capability, RAM, storage capacity and network bandwidth. For this reason, conventional cluster workloads do not perform particularly well on SBC-based micro-clusters. However, a number of compelling use cases have emerged for this new technology, see Section IV.Our project involves building federated clusters of single board computer (SBC) nodes. During our work, we have encountered a number of practical challenges in terms of both hardware (Section II) and software (Section III). This demo (described in Section V) will identify these challenges and showcase our working solutions. II. P SCreating a cluster of Single Board Computers (SBCs) poses challenges around mounting and cable management. The extremely small form factor of the SBC means requires novel mounting mechanisms and the density available means many more cables are present when compared to a server cluster. The approach used by Iridis-Pi [1] was to use Lego to create a rack to hold the SBCs, in this case Raspberry Pi version 1. Whilst 1https://betanews.com/2017/07/19/raspberry-pi-eben-upton-qa/ the approach allowed the first Pi cluster to be produced and benchmarked it used separate USB power supplies for each Pi meaning fine grained power consumption data is not available and the power distribution hardware was bulkier than the main cluster. Since then there have been other approaches taken to producing Pi Clusters such as using Power-Over-Ethernet2, or 12volt DC power distribution and local power supplies3.An alternative solution to either of these is the Pi Stack [2] shown in Figure 1a. This board sits between 2 Raspberry Pi SBC (or any other SBC board using the same B+ footprint4) and provides individual power control and monitoring for each SBC. The boards use the metal stand-offs that form the mechanical mounting to carry the power and control signals within the stack of boards. Each Pi Stack board is given a unique ID using DIP switches mounted on the board. ...

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Bringing the Cloud to Rural and Remote Areas via Cloudlets

Cited by 11 publications

References 29 publications

All one needs to know about fog computing and related edge computing paradigms: A complete survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

A Survey on Trustworthiness for the Internet of Things

Next generation single board clusters

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