Heterogeneity: The Key to Achieve Power-Proportional Computing
da Costa, G. ; IRIT, Univ. de Toulouse, Toulouse, France
The Smart 2020 report on low carbon economy in the information age shows that 2% of the global CO2footprint will come from ICT in 2020. Out of these, 18% will be caused by data-centers, while 45% will come from personal computers. Classical research to reduce this footprint usually focuses on new consolidation techniques for global data-centers. In reality, personal computers and private computing infrastructures are here to stay. They are subject to irregular workload, and are usually largely under-loaded. Most of these computers waste tremendous amount of energy as nearly half of their maximum power consumption comes from simply being switched on. The ideal situation would be to use proportional computers that use nearly 0W when lightly loaded. This article shows the gains of using a perfectly proportional hardware on different type of data-centers: 50% gains for the servers used during 98 World Cup, 20% to the already optimized Google servers. Gains would attain up to 80% for personal computers. As such perfect hardware still does not exist, a real platform composed of Intel I7, Intel Atom and Raspberry Pi is evaluated. Using this infrastructure, gains are of 20% for the World Cup data-center, 5% for Google data-centers and up to 60% for personal computers.
這篇paper有拿intel的處理器和Pi作效能上的比較，可以做為異質環境比較的參考
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Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium onDate of Conference:
13-16 May 2013Affordable and Energy-Efficient Cloud Computing Clusters: The Bolzano Raspberry Pi Cloud Cluster Experiment
Abrahamsson, P. ; Fac. of Comput. Sci., Free Univ. of Bozen-Bolzano, Bolzano, Italy ; Helmer, S. ; Phaphoom, N. ; Nicolodi, L.
We present our ongoing work building a Raspberry Pi cluster consisting of 300 nodes. The unique characteristics of this single board computer pose several challenges, but also offer a number of interesting opportunities. On the one hand, a single Raspberry Pi can be purchased cheaply and has a low power consumption, which makes it possible to create an affordable and energy-efficient cluster. On the other hand, it lacks in computing power, which makes it difficult to run computationally intensive software on it. Nevertheless, by combining a large number of Raspberries into a cluster, this drawback can be (partially) offset. Here we report on the first important steps of creating our cluster: how to set up and configure the hardware and the system software, and how to monitor and maintain the system. We also discuss potential use cases for our cluster, the two most important being an inexpensive and green test bed for cloud computing research and a robust and mobile data center for operating in adverse environments.
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Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on (Volume:2 )Date of Conference:
2-5 Dec. 2013Technical development and socioeconomic implications of the Raspberry Pi as a learning tool in developing countries
Ali, M. ; Sch. of Eng., Univ. of Warwick, Coventry, UK ; Vlaskamp, J.H.A. ; Eddin, N.N. ; Falconer, B.
The recent development of the Raspberry Pi mini computer has provided new opportunities to enhance tools for education. The low cost means that it could be a viable option to develop solutions for education sectors in developing countries. This study describes the design, development and manufacture of a prototype solution for educational use within schools in Uganda whilst considering the social implications of implementing such solutions. This study aims to show the potential for providing an educational tool capable of teaching science, engineering and computing in the developing world. During the design and manufacture of the prototype, software and hardware were developed as well as testing performed to define the performance and limitation of the technology. This study showed that it is possible to develop a viable modular based computer systems for educational and teaching purposes. In addition to science, engineering and computing; this study considers the socioeconomic implications of introducing the EPi within developing countries. From a sociological perspective, it is shown that the success of EPi is dependant on understanding the social context, therefore a next phase implementation strategy is proposed.
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Computer Science and Electronic Engineering Conference (CEEC), 2013 5thDate of Conference:
17-18 Sept. 2013Raspberry PI Hadoop Cluster
安裝教學的blog~
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