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Building a large-scale object-based active storage platform for data analytics in the internet of things

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Abstract

Due to consistent improvements in memory and processor technology, object storage devices (OSDs) have greater memory space and more powerful processing power, which allow the OSDs to execute user-defined programs. Shifting part of an application’s processing to the disk drives drops the amount of data transferred across the network and explores the parallelism of large-scale distributed storage systems, reducing the execution time for many basic data analytics tasks. In this paper, we propose a large-scale object-based active storage platform, named Gem, for data analytics in the internet of things (IoT). All data from the IoT that resides in disk drives form objects with attributes, methods and policies. For some applications such as data analytics, application-specific operations are executed by the drive processors. In this way, only the results are returned to clients, rather than data files being read by the clients. Therefore, the platform Gem is able to greatly reduce the overhead of data analytics applications in the Internet of Things. By conducting performance evaluation, experimental results demonstrate the effectiveness and scalability of Gem.

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Acknowledgments

This work is supported by A\(^*\)STAR Thematic Strategic Research Programme (TSRP) Grant No. 1121720013.

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Authors and Affiliations

  1. Data Storage Institute, A*STAR, Singapore, Singapore

    Quanqing Xu, Khin Mi Mi Aung, Yongqing Zhu & Khai Leong Yong

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  1. Quanqing Xu
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  2. Khin Mi Mi Aung
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Correspondence to Quanqing Xu.

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Xu, Q., Aung, K.M.M., Zhu, Y. et al. Building a large-scale object-based active storage platform for data analytics in the internet of things. J Supercomput 72, 2796–2814 (2016). https://doi.org/10.1007/s11227-016-1621-2

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