Page 286 - 《软件学报》2025年第7期

秦政 等: 面向 Apache Flink 流式分析应用的高吞吐优化技术 3207

1145/2882903.2882906]
[26] Mai L, Zeng K, Potharaju R, Xu L, Suh S, Venkataraman S, Costa P, Kim T, Muthukrishnan S, Kuppa V, Dhulipalla S, Rao S. Chi: A
scalable and programmable control plane for distributed stream processing systems. Proc. of the VLDB Endowment, 2018, 11(10):
1303–1316. [doi: 10.14778/3231751.3231765]
[27] Varga B, Balassi M, Kiss A. Towards autoscaling of Apache Flink jobs. Acta Universitatis Sapientiae, Informatica, 2021, 13(1): 39–59.
[doi: 10.2478/ausi-2021-0003]
[28] Arkian HR, Pierre G, Tordsson J, Elmroth E. Model-based stream processing auto-scaling in geo-distributed environments. In: Proc. of
the 2021 Int’l Conf. on Computer Communications and Networks (ICCCN). Athens: IEEE, 2021. 1–10. [doi: 10.1109/ICCCN52240.2021.
9522236]
[29] He CL, Huang Y, Wang CY, Wang N. Dynamic data partitioning strategy based on heterogeneous Flink cluster. In: Proc. of the 5th Int’l
Conf. on Artificial Intelligence and Big Data (ICAIBD). Chengdu: IEEE, 2022. 355–360. [doi: 10.1109/ICAIBD55127.2022.9820336]
[30] Tangwongsan K, Hirzel M, Schneider S. Optimal and general out-of-order sliding-window aggregation. Proc. of the VLDB Endowment,
2019, 12(10): 1167–1180. [doi: 10.14778/3339490.3339499]
[31] Shahvarani A, Jacobsen HA. Parallel index-based stream join on a multicore CPU. In: Proc. of the 2020 ACM SIGMOD Int’l Conf. on
Management of Data. Portland: ACM, 2020. 2523–2537. [doi: 10.1145/3318464.3380576]
[32] Karimov J, Rabl T, Markl V. AStream: Ad-hoc shared stream processing. In: Proc. of the 2019 Int’l Conf. on Management of Data.
Amsterdam: ACM, 2019. 607–622. [doi: 10.1145/3299869.3319884]
[33] Karimov J, Rabl T, Markl V. AJoin: Ad-hoc stream joins at scale. Proc. of the VLDB Endowment, 2019, 13(4): 435–448. [doi: 10.14778/
3372716.3372718]
[34] McSherry F, Lattuada A, Schwarzkopf M, Roscoe T. Shared arrangements: Practical inter-query sharing for streaming dataflows.
arXiv:1812.02639, 2020.
[35] Zhang XQ, Ma K. Toward sliding time window of low watermark to detect delayed stream arrival. In: Proc. of the 16th EAI Int’l Conf.
on Collaborative Computing: Networking, Applications and Worksharing. Shanghai: Springer, 2021. 444–454. [doi: 10.1007/978-3-030-
67540-0_28]
[36] Yue XF, Shi L, Zhao YH, Ji HX, Wang GR. Dynamic resource allocation strategy for Flink iterative jobs. Ruan Jian Xue Bao/Journal of
Software, 2022, 33(3): 985–1004 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6447.htm [doi: 10.13328/j.cnki.jos.
006447]
[37] Shaikh SA, Mariam K, Kitagawa H, Kim KS. GeoFlink: A distributed and scalable framework for the real-time processing of spatial
streams. In: Proc. of the 29th ACM Int’l Conf. on Information & Knowledge Management. Virtual Event: ACM, 2020. 3149–3156. [doi:
10.1145/3340531.3412761]
[38] Putatunda S, Laha AK. Travel time prediction in real time for GPS taxi data streams and its applications to travel safety. Human-centric
Intelligent Systems, 2023, 3: 381–401. [doi: 10.1007/s44230-023-00028-0]
[39] Apache Kafka. 2023. https://kafka.apache.org/
[40] Redis. 2024. https://redis.io/
[41] Akidau T, Begoli E, Chernyak S, Hueske F, Knight K, Knowles K, Mills D, Sotolongo D. Watermarks in stream processing systems:
Semantics and comparative analysis of apache Flink and google cloud dataflow. Proc. of the VLDB Endowment, 2021, 14(12):
3135–3147. [doi: 10.14778/3476311.3476389]
[42] Wilmanns PS, Geuns SJ, Hausmans JPHM, Bekooij MJG. Buffer sizing to reduce interference and increase throughput of real-time
stream processing applications. In: Proc. of the 18th IEEE Int’l Symp. on Real-time Distributed Computing. Auckland: IEEE, 2015. 9–18.
[doi: 10.1109/ISORC.2015.14]
[43] Gulisano V, Palyvos-Giannas D, Havers B, Papatriantafilou M. The role of event-time order in data streaming analysis. In: Proc. of the
14th ACM Int’l Conf. on Distributed and Event-based Systems. Montreal: ACM, 2020. 214–217. [doi: 10.1145/3401025.3404088]
[44] Dahlgaard S, Knudsen MBT, Thorup M. Practical hash functions for similarity estimation and dimensionality reduction. In: Proc. of the
31st Int’l Conf. on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017. 6618–6628.
[45] Aumayr D, Marr S, Gonzalez Boix E, Mössenböck H. Asynchronous snapshots of actor systems for latency-sensitive applications. In:
Proc. of the 16th ACM SIGPLAN Int’l Conf. on Managed Programming Languages and Runtimes. Athens: ACM, 2019. 157–171. [doi:
10.1145/3357390.3361019]
[46] Chandy KM, Lamport L. Distributed snapshots: Determining global states of distributed systems. ACM Trans. on Computer Systems
(TOCS), 1985, 3(1): 63–75. [doi: 10.1145/214451.214456]
[47] Performance Tuning. 2023. https://nightlies.apache.org/flink/flink-docs-release-1.13/docs/dev/table/tuning/