Page 397 - 《软件学报》2025年第9期
P. 397
4308 软件学报 2025 年第 36 卷第 9 期
究如何利用分布式集群对 NCQT 水平扩展, 进一步提高算法的并行度, 从而更高效地压缩和查询分布式追踪数据.
References:
[1] Yang Y, Li Y, Wu ZH. Survey of state-of-the-art distributed tracing technology. Ruan Jian Xue Bao/Journal of Software, 2020, 31(7):
2019–2039 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6047.htm [doi: 10.13328/j.cnki.jos.006047]
[2] Zhang ZZ, Ramanathan MK, Raj P, Parwal A, Sherwood T, Chabbi M. CRISP: Critical path analysis of large-scale microservice
architectures. In: Proc. of the 2022 USENIX Annual Technical Conf. Carlsbad: USENIX Association, 2022. 655–672.
[3] Zhang YZ, Isaacs R, Yue Y, Yang JC, Zhang L, Vigfusson Y. LatenSeer: Causal modeling of end-to-end latency distributions by
harnessing distributed tracing. In: Proc. of the 2023 ACM Symp. on Cloud Computing. Santa Cruz: ACM, 2023. 502–519. [doi: 10.1145/
3620678.3624787]
[4] Zhang CX, Peng X, Sha CF, Zhang K, Fu ZQ, Wu XY, Lin QW, Zhang DM. DeepTraLog: Trace-log combined microservice anomaly
detection through graph-based deep learning. In: Proc. of the 44th Int’l Conf. on Software Engineering. Pittsburgh: ACM, 2022. 623–634.
[doi: 10.1145/3510003.3510180]
[5] Zhang K, Zhang CX, Peng X, Sha CF. PUTraceAD: Trace anomaly detection with partial labels based on GNN and PU learning. In: Proc.
of the 33rd IEEE Int’l Symp. on Software Reliability Engineering (ISSRE). Charlotte: IEEE, 2022. 239–250. [doi: 10.1109/ISSRE55969.
2022.00032]
[6] Zhou T, Zhang CX, Peng X, Yan ZH, Li PR, Liang JM, Zheng HB, Zheng WJ, Deng YT. TraceStream: Anomalous service localization
based on trace stream clustering with online feedback. In: Proc. of the 34th IEEE Int’l Symp. on Software Reliability Engineering
(ISSRE). Florence: IEEE, 2023. 601–611. [doi: 10.1109/ISSRE59848.2023.00033]
[7] Liu JS, Wang QY, Zhang SG, Hu LT, Da Silva D. Sora: A latency sensitive approach for microservice soft resource adaptation. In: Proc.
of the 24th Int’l Middleware Conf. Bologna: ACM, 2023. 43–56. [doi: 10.1145/3590140.3592851]
[8] Huang LX, Zhu T. tprof: Performance profiling via structural aggregation and automated analysis of distributed systems traces. In: Proc.
of the 2021 ACM Symp. on Cloud Computing. Seattle: ACM, 2021. 76–91. [doi: 10.1145/3472883.3486994]
[9] Huang ZC, Chen PF, Yu GB, Chen HY. Transparent request tracing and sampling method for Java-based microservice system. Ruan Jian
Xue Bao/Journal of Software, 2023, 34(7): 3167–3187 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6523.htm
[doi: 10.13328/j.cnki.jos.006523]
[10] Peng X, Zhang CX, Zhao ZY, Isami A, Guo XF, Cui YN. Trace analysis based microservice architecture measurement. In: Proc. of the
30th ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. Singapore: ACM, 2022.
1589–1599. [doi: 10.1145/3540250.3558951]
[11] He SL, Feng BT, Li LQ, Zhang X, Kang Y, Lin QW, Rajmohan S, Zhang DM. STEAM: Observability-preserving trace sampling. In:
Proc. of the 31st ACM Joint European Software Engineering Conf. and Symp. on the Foundations of Software Engineering. San
Francisco: ACM, 2023. 1750–1761. [doi: 10.1145/3611643.3613881]
[12] Gias AU, Gao YC, Sheldon M, Perusquía JA, O’Brien O, Casale G. SampleHST: Efficient on-the-fly selection of distributed traces. In:
Proc. of the 2023 IEEE/IFIP Network Operations and Management Symp. (NOMS 2023). Miami: IEEE, 2023. 1–9. [doi: 10.1109/
NOMS56928.2023.10154383]
[13] Zhou H, Chen M, Lin Q, Wang Y, She XB, Liu SF, Gu R, Ooi BC, Yang JF. Overload control for scaling WeChat microservices. In:
Proc. of the 2018 ACM Symp. on Cloud Computing. Carlsbad: ACM, 2018. 149–161. [doi: 10.1145/3267809.3267823]
[14] Annamalai M, Ravichandran K, Srinivas H, Zinkovsky I, Pan LN, Savor T, Nagle D, Stumm M. Sharding the shards: Managing datastore
locality at scale with Akkio. In: Proc. of the 13th USENIX Symp. on Operating Systems Design and Implementation (OSDI 2018).
Carlsbad: USENIX Association, 2018. 445–460.
[15] Las-Casas P, Mace J, Guedes D, Fonseca R. Weighted sampling of execution traces: Capturing more needles and less hay. In: Proc. of the
2018 ACM Symp. on Cloud Computing. Carlsbad: ACM, 2018. 326–332. [doi: 10.1145/3267809.3267841]
[16] Las-Casas P, Papakerashvili G, Anand V, Mace J. Sifter: Scalable sampling for distributed traces, without feature engineering. In: Proc. of
the 2019 ACM Symp. on Cloud Computing. Santa Cruz: ACM, 2019. 312–324. [doi: 10.1145/3357223.3362736]
[17] Huang ZC, Chen PF, Yu GB, Chen HY, Zheng ZB. Sieve: Attention-based sampling of end-to-end trace data in distributed microservice
systems. In: Proc. of the 2021 IEEE Int’l Conf. on Web Services (ICWS). Chicago: IEEE, 2021. 436–446. [doi: 10.1109/ICWS53863.
2021.00063]
[18] Free Software Foundation. GNU gzip. 2024. https://www.gnu.org/software/gzip/
[19] Ziv J, Lempel A. A universal algorithm for sequential data compression. IEEE Trans. on Information Theory, 1977, 23(3): 337–343. [doi:
10.1109/TIT.1977.1055714]

