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 crowd-sourced 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.

大数据采集和处理对于开发价值驱动的机器学习应用程序很重要。这在计算资源受限的情况下具有挑战性。计算资源受限的情况是由于已安装的云基础架构的容量较低以及高速Internet链接的可用性较低而引起的。这些因素限制了处理众包数据以开发机器学习应用程序的能力。发现自愿计算范式适合解决这些挑战。志愿计算范例利用分布在地理区域上的用户提供的计算节点。它利用具有低成本计算实体的志愿者的可用性。本文提出了分数计算系统（FCS）来解决上述挑战。FCS集成了智能计算节点选择功能，并使用高性能的最终用户计算节点（笔记本电脑）来处理众包数据。针对使用云服务器的现有方法，调查了FCS的性能。结果表明，FCS分别将购置成本和功耗降低了35％，平均降低了56.5％。处理众包数据的每位瓦特数也平均提高了98％。另外，FCS的使用增强了可用于数据处理的内存资源。仿真表明，将模块化计算实体中的内存增加多达58.7％，可使模块化志愿者计算节点网络中的可用内存增加0.5 EB。