scholarly journals Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment

2013 ◽  
Author(s):  
Pekka Abrahamsson ◽  
Sven Helmer ◽  
Nattakarn Phaphoom ◽  
Lorenzo Nicolodi ◽  
Nick Preda ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Raspberry Pi ◽  
Cloud Cluster

scholarly journals An energy-efficient failure detector for vehicular cloud computing

PLoS ONE ◽  
2018 ◽  
Vol 13 (1) ◽  
pp. e0191577 ◽  
Author(s):  
Jiaxi Liu ◽  
Zhibo Wu ◽  
Jian Dong ◽  
Jin Wu ◽  
Dongxin Wen
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Failure Detector ◽  
Vehicular Cloud Computing ◽  
Vehicular Cloud

Toward energy-efficient cloud computing: Prediction, consolidation, and overcommitment

IEEE Network ◽  
2015 ◽  
Vol 29 (2) ◽  
pp. 56-61 ◽  
Author(s):  
Mehiar Dabbagh ◽  
Bechir Hamdaoui ◽  
Mohsen Guizani ◽  
Ammar Rayes
Keyword(s):  
Cloud Computing ◽  
Energy Efficient

PERFORMANCE ANALYSIS OF THE SUPERCOMPUTER BASED ON RASPBERRY PI NODES

2021 ◽  
pp. 76-86
Author(s):  
Hai
Keyword(s):  
Performance Analysis ◽  
Energy Efficient ◽  
Multicore Processors ◽  
Electrical Power ◽  
Experimental Results ◽  
Raspberry Pi ◽  
Cooling Towers ◽  
Cluster Architecture ◽  
Cooling Technology ◽  
Almost All

In this paper, a new Raspberry PI supercomputer cluster architecture is proposed. Generally, to gain speed at petaflops and exaflops, typical modern supercomputers based on 2009-2018 computing technologies must consume between 6 MW and 20 MW of electrical power, almost all of which is converted into heat, requiring high cost for cooling technology and Cooling Towers. The management of heat density has remained a key issue for most centralized supercomputers. In our proposed architecture, supercomputers with highly energy-efficient mobile ARM processors are a new choice as it enables them to address performance, power, and cost issues. With ARM’s recent introduction of its energy-efficient 64-bit CPUs targeting servers, Raspberry Pi cluster module-based supercomputing is now within reach. But how is the performance of supercomputers-based mobile multicore processors? Obtained experimental results reported on the proposed approach indicate the lower electrical power and higher performance in comparison with the previous approaches.

Energy-efficient collaborative optimization for VM scheduling in cloud computing

2021 ◽  
pp. 108565
Author(s):  
Bin Wang ◽  
Fagui Liu ◽  
Weiwei Lin ◽  
Zhenjiang Ma ◽  
Dishi Xu
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Collaborative Optimization ◽  
Vm Scheduling

Failure-aware energy-efficient VM consolidation in cloud computing systems

2019 ◽  
Vol 94 ◽  
pp. 620-633 ◽  
Author(s):  
Yogesh Sharma ◽  
Weisheng Si ◽  
Daniel Sun ◽  
Bahman Javadi
Keyword(s):  
Cloud Computing ◽  
Energy Efficient ◽  
Computing Systems ◽  
Vm Consolidation

scholarly journals Fog Computing in the context of Smart Home, voice assistant and the future of IoT

2020 ◽  
Vol 21 (1) ◽  
pp. 6-12
Author(s):  
Javier Pinzón Castellanos ◽  
Miguel Antonio Cadena Carter
Keyword(s):  
Cloud Computing ◽  
Distributed Computing ◽  
Data Transmission ◽  
Smart Home ◽  
Fog Computing ◽  
Relevant Information ◽  
Raspberry Pi ◽  
Thermal Sensors ◽  
Computing Architecture ◽  
The Future

Fog Computing is the distributed computing layer that lies between the user and the cloud. A successful fog architecture reduces delay or latency and increases efficiency. This paper describes the development and implementation of a distributed computing architecture applied to an automation environment that uses Fog Computing as an intermediary with the cloud computing layer. This study used a Raspberry Pi V3 board connected to end control elements such as servomotors and relays, indicators and thermal sensors. All is controlled by an automation framework that receives orders from Siri and executes them through predetermined instructions. The cloud connection benefits from a reduced amount of data transmission, because it only receives relevant information for analysis.

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