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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Quick Answer for Big Data in Sharing Economy: Innovative Computer Architecture Design Facilitating Optimal Service-Demand Matching
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Quick Answer for Big Data in Sharing Economy: Innovative Computer Architecture Design Facilitating Optimal Service-Demand Matching

机译：共享经济中大数据的快速答案：促进最佳服务需求匹配的创新计算机体系结构设计

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摘要

In sharing economy, people offer idle social resources to others in a sharing manner. Through community-based online platforms, the people offering services can earn commission while others can enjoy a better life via renting social resources. Consequently, the value-in-use of services is expectedly strengthened within the unit time, although the total amount of social resources remains constant. Influenced by sharing economy, some famous companies have developed intelligent systems to analyze the most appropriate coincidence between citizens' idle supply and renting demand from numerous data sets. However, the big data analysis of the optimal service-demand matching usually runs on the traditional multiprocessors equipped in intelligent systems, so-called “system-on-chip.” In this paper, we design a novel computer architecture - the accelerator based on optical network-on-chip (ONoC) - to further speed up the matching between citizens' offer and demand in sharing economy. Our ONoC-based accelerator is able to quickly calculate the optimal service-demand matching by processing computation tasks on parallel cores, i.e., task-core mapping. In addition, to improve the accelerator reliability, the assorted task-core mapping algorithm is also designed. The extensive simulation results based on real trace file demonstrate the effectiveness of our system and algorithm. Note to Practitioners - Sharing economy is of great importance for realizing green consumption and sustainable development in our human society. Sharing economy enterprise calls for intelligent system design for service-demand matching in the current big data era. In this paper, we design the accelerator based on ONoC to further speed up the matching between citizens' offer and demand in sharing economy. By processing computation tasks on parallel cores using our algorithm, the task-core mapping can be performed with high speed and reliability. The simulation results - based on the trace file of Amazon Mechanical Turk - can well guide the practitioners to design a more clever and reliable product by quickly calculating the optimal service-demand matching.
机译：在共享经济中，人们以共享的方式向他人提供闲置的社会资源。通过基于社区的在线平台，提供服务的人们可以赚取佣金，而其他人则可以通过租借社会资源来享有更好的生活。因此，尽管社会资源总量保持不变，但有望在单位时间内提高服务的使用价值。受共享经济的影响，一些著名公司开发了智能系统，以从众多数据集中分析市民的闲置供给和租赁需求之间的最恰当的重合。但是，最佳服务需求匹配的大数据分析通常在配备有智能系统的传统多处理器（所谓的“片上系统”）上进行。在本文中，我们设计了一种新颖的计算机体系结构-基于片上光网络（ONoC）的加速器-进一步加快了共享经济中公民的供求之间的匹配。我们基于ONoC的加速器能够通过在并行核心上处理计算任务（即任务核心映射）快速计算出最佳的服务需求匹配。此外，为了提高加速器的可靠性，还设计了各种任务核心映射算法。基于真实跟踪文件的大量仿真结果证明了我们的系统和算法的有效性。从业者注意-共享经济对于实现人类社会的绿色消费和可持续发展至关重要。共享经济型企业呼吁在当前的大数据时代实现智能系统设计以实现服务需求匹配。在本文中，我们设计了基于ONoC的加速器，以进一步加快共享经济中公民的供求关系。通过使用我们的算法在并行内核上处理计算任务，可以快速，可靠地执行任务内核映射。仿真结果基于Amazon Mechanical Turk的跟踪文件，可以通过快速计算最佳的服务需求匹配，很好地指导从业人员设计更智能，更可靠的产品。

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