Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling

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Title: Minimizing Network Traffic for Distributed Joins Using Lightweight Locality-Aware Scheduling
Authors: Cheng, Long
Murphy, John
Liu, Qingzhi
et al.
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Date: 31-Aug-2018
Online since: 2019-04-18T11:48:29Z
Abstract: Large computing systems such as data centers are becoming the mainstream infrastructures for big data processing. As one of the key data operators in such scenarios, distributed joins is still challenging current techniques since it always incurs a significant cost on network communication. Various advanced approaches have been proposed to improve the performance, however, most of them just focus on data skew handling, and algorithms designed specifically for communication reduction have received less attention. Moreover, although the state-of-the-art technique can minimize network traffic, it provides fine-grained optimal schedules for all individual join keys, which could result in obvious overhead. In this paper, we propose a new approach called LAS (Lightweight Locality-Aware Scheduling), which targets reducing network communication for large distributed joins in an efficient and effective manner. We present the detailed design and implementation of LAS, and conduct an experimental evaluation using large data joins. Our results show that LAS can effectively reduce scheduling overhead and achieve comparable performance on network reduction compared to the state-of-the-art.
Funding Details: European Commission Horizon 2020
Type of material: Conference Publication
Publisher: Euro-Par
Start page: 293
End page: 305
Copyright (published version): 2018 Springer Nature Switzerland AG
Keywords: Distributed joinsData localityNetwork communicationLocality-aware scheduling
DOI: 10.1007/978-3-319-96983-1
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Language: en
Status of Item: Unspecified
Is part of: Aldinucci, M., Padovani, L., Torquati, M. (eds.). Euro-Par 2018: Parallel Processing 24th International Conference on Parallel and Distributed Computing, Turin, Italy, August 27 - 31, 2018, Proceedings
Conference Details: The 24th International European Conference on Parallel and Distributed Computing (EURO-PAR 2018), Turin, Italy, 27-31 2018
ISBN: 978-3-319-96983-1
Appears in Collections:Computer Science Research Collection

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