Hybrid wireless aided volunteer computing paradigm
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Hybrid wireless aided volunteer computing paradigm Ayodele A. Periola1
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Olabisi E. Falowo2
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Senior Member IEEE
Ó Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Big-data acquisition and processing is important in developing value driven machine learning applications. This is challenging in compute resource-constrained scenarios. Compute resource-constrained scenarios arise due to low capacity of installed cloud infrastructure and low availability of high speed internet links. These factors limit the ability to process crowd-sourced data to develop machine learning applications. The volunteer computing paradigm is found to be suitable for addressing these challenges. Volunteer computing paradigm makes use of computing nodes provided by users distributed over a geographical area. It leverages on the availability of volunteers with low cost computing entities. This paper proposes the fractionated computing system (FCS) to address the challenges described above. FCS incorporates intelligent compute node selection and uses high performance end-user computing nodes (laptops) to process the crowdsourced data. The performance of FCS is investigated against the existing method of using cloud servers. Results show that FCS reduces acquisition costs and power consumption by 35% and up to 56.5% on average, respectively. The watt per bit expended in processing crowd-sourced data is also enhanced by up to 98% on average. In addition, the use of FCS enhances memory resources accessible for data processing. Simulations show that increasing memory in modular computing entities by up to 58.7% enhances memory available across network of modular volunteer computing nodes by 0.5 EB. The use of end-user nodes with modular communication subsystems instead of end-user computing nodes with non-modular communication sub-systems enhances channel capacity by 37.5% on average. Keywords Crowd-sourcing Data processing Wireless computing Volunteer computing
1 Introduction The needs driving the necessity of having access to high performance computing resources and facilities are universal. However, the access to high performance computing resources is not. Developed nations are at a vantage point because of the significant investment in developing high performance computing infrastructure. However, this is not true for developing nations with comparatively lower levels of technological development. This affects the
& Ayodele A. Periola [email protected] Olabisi E. Falowo [email protected] 1
Department of Electrical, Electronics and Computer Engineering, Bells University of Technology, Otta, Nigeria
2
Department of Electrical Engineering, University of Cape Town, Cape Town, South Africa
ability of developing nations to deploy applications in areas like wireless communications requiring big data processing [1–5]. There is an increasing recognition of the usefulness of big data in designing intelligent solutions leveraging on machine learning algorithms. This ha
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