Hardware-Conscious Stream Processing-Reference-Cited by-同舟云学术

Hardware-Conscious Stream Processing

Published:2020-02-25 Issue:4 Volume:48 Page:18-29
ISSN:0163-5808
Container-title:ACM SIGMOD Record
language:en
Short-container-title:SIGMOD Rec.

Author:

Zhang Shuhao¹,Zhang Feng²,Wu Yingjun³,He Bingsheng¹,Johns Paul¹

Affiliation:

1. National University of Singapore, Singapore

2. Renmin University of China, China

3. Amazon Web Services, Seattle, WA, USA

Abstract

Data stream processing systems (DSPSs) enable users to express and run stream applications to continuously process data streams. To achieve realtime data analytics, recent researches keep focusing on optimizing the system latency and throughput. Witnessing the recent great achievements in the computer architecture community, researchers and practitioners have investigated the potential of adoption hardware-conscious stream processing by better utilizing modern hardware capacity in DSPSs. In this paper, we conduct a systematic survey of recent work in the field, particularly along with the following three directions: 1) computation optimization, 2) stream I/O optimization, and 3) query deployment. Finally, we advise on potential future research directions.

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems,Software

Link

https://dl.acm.org/doi/pdf/10.1145/3385658.3385662

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