Astor: A compute framework for scalable distributed big data processing

Smriti Prathapan; Navid Golpayegani; Bryan Wyatt; Milton Halem; John Dorband; Jon Trantham; Chris Markey

doi:10.1117/12.2558811

18 May 2020 Astor: A compute framework for scalable distributed big data processing

Smriti Prathapan, Navid Golpayegani, Bryan Wyatt, Milton Halem, John Dorband, Jon Trantham, Chris Markey

Proceedings Volume 11395, Big Data II: Learning, Analytics, and Applications; 113950O (2020) https://doi.org/10.1117/12.2558811
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

The cost of data-movement is one of the fundamental issues with modern compute systems processing Big Data workloads. One approach to move the computation closer to data is to equip the storage or memory devices with processing power. The notion of moving computation to data is known as Near Data Processing (NDP). In this work, we re-examine the idea of reducing the data movement by processing data directly in the storage devices. We evaluate ASTOR, a compute framework on an Active Storage platform, which incorporates a software stack and a dedicated multi-core processor for in-storage processing. ASTOR utilizes the processing power of storage devices by using an array of Active Drive™ devices to significantly reduce the bandwidth requirement on the network. We evaluate the performance and scalability of ASTOR for distributed processing of Big Data workloads. We conclude by discussing a comparative study of other existing data-centric approaches.

Conference Presentation

Citation Download Citation

Smriti Prathapan, Navid Golpayegani, Bryan Wyatt, Milton Halem, John Dorband, Jon Trantham, and Chris Markey "Astor: A compute framework for scalable distributed big data processing", Proc. SPIE 11395, Big Data II: Learning, Analytics, and Applications, 113950O (18 May 2020); https://doi.org/10.1117/12.2558811

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