abstract

Lever: towards low-latency batched stream processing by pre-scheduling

Authors:

Fei Chen,

Song Wu,

Hai Jin,

Yin Yao,

Zhiyi Liu,

Lin Gu,

Yongluan ZhouAuthors Info & Claims

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

Page 643

https://doi.org/10.1145/3127479.3132687

Published: 24 September 2017 Publication History

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Abstract

With the vast involvement of streaming big data in many applications (e.g., stock market data, sensor data, social network data, etc.), quickly mining and analyzing such data is becoming more and more important. To provide fault tolerance and efficient stream processing at scale, recent stream processing frameworks have proposed to adapt batch processing systems, such as MapReduce and Spark, to handle streaming data by putting the streams into micro-batches and treating the workloads as a continuous series of small jobs [1].

The fundamental challenge of building a batched stream processing system is to minimize the processing latency of each micro-batch. In this paper, we focus on the straggler problem, where a subset of workers are straggling behind and significantly affecting the job completion time. The straggler problem is a well-known critical problem in parallel processing systems. In comparing to large batch processing, the straggler problems in micro-batch processing are more severe and harder to tackle.

We argue that the problem of using the existing straggler mitigation solutions for micro-batch processing is that they detect (or predict) stragglers and re-schedule stragglers too late in the data handling pipeline. The re-scheduling actions are carried out during the task execution period, hence it would inevitably increase the processing time of the micro-batches. Furthermore, as the data have already been dispatched, re-scheduling would inherently incur expensive data relocation. Such overhead would become significant in micro-batch processing due to the short processing time of each micro-batch. We refer to this type of methods as post-scheduling techniques.

To address the problem, we propose a new pre-scheduling framework, called Lever, which predicts stragglers and makes timely scheduling decisions to minimize the processing latency. As shown in Figure 1, Lever periodically collects and analyzes the historical job profiles of the recurring micro-batch jobs. Based on such information, Lever pre-schedules the data through three main steps, i.e. identify potential stragglers, evaluate node capacity and choose suitable helpers. More importantly, Lever makes the re-scheduling decisions before the batching module dispatches the data. As the scheduling is done while the data are being batched, it would not increase the processing time of the micro-batch.

We implemented Lever in Spark Streaming, which has been contributed to the open source community as an extension of Apache Spark Streaming. To the best of our knowledge, this is the first work specifically addressing the straggler problem in continuous micro-batch processing. We conduct various experiments to validate the effectiveness of Lever. The experimental results demonstrate that Lever reduces job completion time by 30.72% to 42.19% and outperforms traditional techniques significantly.

Reference

[1]

M. Zaharia, T. Das, H. Li, T. Hunter, S. Shenker, and I. Stoica, "Discretized Streams: Fault-Tolerant Streaming Computation at Scale," In SOSP'13.

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Lee SJeong YPark KJung GPark S(2022)zStream: towards a low latency micro-batch streaming systemCluster Computing10.1007/s10586-022-03758-126:5(2773-2787)Online publication date: 14-Oct-2022
https://doi.org/10.1007/s10586-022-03758-1
Ni FHao JLu JTong XYuan MDuan JMa YHe KZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling ProblemProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467135(3441-3451)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467135
Gill SOuyang XGarraghan P(2020)Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centresThe Journal of Supercomputing10.1007/s11227-020-03241-x76:12(10050-10089)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1007/s11227-020-03241-x
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Information

Published In

SoCC '17: Proceedings of the 2017 Symposium on Cloud Computing

September 2017

672 pages

ISBN:9781450350280

DOI:10.1145/3127479

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 September 2017

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National Key Research and Development Program
National Science Foundation of China
863 Hi-Tech Research and Development Program

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SoCC '17

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SoCC '17: ACM Symposium on Cloud Computing

September 24 - 27, 2017

California, Santa Clara

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Overall Acceptance Rate 169 of 722 submissions, 23%

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Cited By

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Lee SJeong YPark KJung GPark S(2022)zStream: towards a low latency micro-batch streaming systemCluster Computing10.1007/s10586-022-03758-126:5(2773-2787)Online publication date: 14-Oct-2022
https://doi.org/10.1007/s10586-022-03758-1
Ni FHao JLu JTong XYuan MDuan JMa YHe KZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)A Multi-Graph Attributed Reinforcement Learning based Optimization Algorithm for Large-scale Hybrid Flow Shop Scheduling ProblemProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467135(3441-3451)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467135
Gill SOuyang XGarraghan P(2020)Tails in the cloud: a survey and taxonomy of straggler management within large-scale cloud data centresThe Journal of Supercomputing10.1007/s11227-020-03241-x76:12(10050-10089)Online publication date: 12-Mar-2020
https://dl.acm.org/doi/10.1007/s11227-020-03241-x
Tantalaki NSouravlas SRoumeliotis M(2019)A review on big data real-time stream processing and its scheduling techniquesInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2019.158584835:5(571-601)Online publication date: 1-Mar-2019
https://doi.org/10.1080/17445760.2019.1585848
Wu SChen FJin HLin LLi R(2018)Radar: Reducing Tail Latencies for Batched Stream Processing with Blank Scheduling2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2018.00134(797-804)Online publication date: Jun-2018
https://doi.org/10.1109/HPCC/SmartCity/DSS.2018.00134

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