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Distributed Log Search Based on Time Series Access and Service Relations

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 450)

Abstract

Distributed tracing helps administrators to analyze root causes of microservices under system failure. It enables tracking procedures by log messages. Distributed trace log searches require short response times. Therefore, this study proposes a log search method with fast response time to search queries. Log messages are stored on several nodes as blocks grouped by date/time and service-name. The search method focuses on time-series access patterns and service relations. It decreases the number of accessed log messages per query on search. Experiment results show that the proposed method is maximally 0.91 s faster than the parallel method ‘all parallel’.

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References

  1. Montesi, F., Weber, J.: From the decorator pattern to circuit breakers in microservices. In: Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 1733–1735 (2018)

    Google Scholar 

  2. Mallanna, S., Devika, M.: Distributed request tracing using zipkin and spring boot sleuth. Int. J. Comput. Appl. 975, 8887 (2020)

    Google Scholar 

  3. Fan, C.-Y., Ma, S.-P.: Migrating monolithic mobile application to microservice architecture: an experiment report. In: 2017 IEEE International Conference on AI & Mobile Services (AIMS), pp. 109–112. IEEE (2017)

    Google Scholar 

  4. Bento, A., Correia, J., Filipe, R., Araujo, F., Cardoso, J.: Automated analysis of distributed tracing: challenges and research directions. J. Grid Comput. 19(1), 1–15 (2021)

    CrossRef  Google Scholar 

  5. Santana, M., Sampaio Jr, A., Andrade, M., Rosa, N.S.: Transparent tracing of microservice-based applications. In: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, pp. 1252–1259 (2019)

    Google Scholar 

  6. Alvarez, C., He, Z., Alonso, G., Singla, A.: Specializing the network for scatter-gather workloads. In: Proceedings of the 11th ACM Symposium on Cloud Computing, pp. 267–280 (2020)

    Google Scholar 

  7. Dan, A., Philip, S.Y., Chung, J.-Y.: Characterization of database access pattern for analytic prediction of buffer hit probability. VLDB J. 4(1), 127–154 (1995)

    CrossRef  Google Scholar 

  8. Ciritoglu, H.E. , Batista de Almeida, L., Cunha de Almeida, E., Buda, T.S., Murphy, J., Thorpe, C.: Investigation of replication factor for performance enhancement in the Hadoop distributed file system. In: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, pp. 135–140 (2018)

    Google Scholar 

  9. Wei, Q., Veeravalli, B., Gong, B., Zeng, L., Feng, D.: CDRM: cost-effective dynamic replication management scheme for cloud storage cluster. In: 2010 IEEE International Conference on Cluster Computing, pp. 188–196. IEEE (2010)

    Google Scholar 

  10. Taware, U., Shaikh, N.: Heterogeneous database system for faster data querying using elasticsearch. In: 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1–4. IEEE (2018)

    Google Scholar 

  11. Krish, K., Khasymski, A., Butt, A.R., Tiwari, S., Bhandarkar, M.: Aptstore: dynamic storage management for hadoop. In: 2013 IEEE 5th International Conference on Cloud Computing Technology and Science, vol. 1, pp. 33–41. IEEE (2013)

    Google Scholar 

  12. Rex, R., Mietke, F., Rehm, W., Raisch, C., Nguyen, H.-N.: Improving communication performance on infiniband by using efficient data placement strategies. In: 2006 IEEE International Conference on Cluster Computing, pp. 1–7. IEEE (2006)

    Google Scholar 

  13. Li, W., Lemieux, Y., Gao, J., Zhao, Z., Han, Y.: Service mesh: challenges, state of the art, and future research opportunities. In: 2019 IEEE International Conference on Service-Oriented System Engineering (SOSE), pp. 122–1225. IEEE (2019)

    Google Scholar 

  14. Krishna, T.L.S.R. , Ragunathan, T., Battula, S.K.: Performance evaluation of read and write operations in hadoop distributed file system. In: 2014 Sixth International Symposium on Parallel Architectures, Algorithms and Programming, pp. 110–113. IEEE (2014)

    Google Scholar 

  15. Ghemawat, S., Gobioff, H., Leung, S.-T.: The google file system. In: Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 29–43 (2003)

    Google Scholar 

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Correspondence to Tomoyuki Koyama .

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Koyama, T., Kushida, T. (2022). Distributed Log Search Based on Time Series Access and Service Relations. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_10

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