Skip to main content
SpringerLink
Log in

IOscope: A Flexible I/O Tracer for Workloads’ I/O Pattern Characterization

  • Conference paper
  • First Online:
High Performance Computing (ISC High Performance 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11203))

Included in the following conference series:

Abstract

Storage systems are getting complex to handle HPC and Big Data requirements. This complexity triggers performing in-depth evaluations to ensure the absence of issues in all systems’ layers. However, the current performance evaluation activity is performed around high-level metrics for simplicity reasons. It is therefore impossible to catch potential I/O issues in lower layers along the Linux I/O stack. In this paper, we introduce IOscope tracer for uncovering I/O patterns of storage systems’ workloads. It performs filtering-based profiling over fine-grained criteria inside Linux kernel. IOscope has near-zero overhead and verified behaviours inside the kernel thanks to relying on the extended Berkeley Packet Filter (eBPF) technology. We demonstrate the capabilities of IOscope to discover patterns-related issues through a performance study on MongoDB and Cassandra. Results show that clustered MongoDB suffers from a noisy I/O pattern regardless of the used storage support (HDDs or SSDs). Hence, IOscope helps to have better troubleshooting process and contributes to have in-depth understanding of I/O performance.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/LeUnAiDeS/IOscope.

  2. 2.

    A major version of MongoDB (v3.6) has been released during writing this paper. It suffers from the same performance issue discussed in Sect. 3.2, regardless of the optimized throughput.

References

  1. Abramova, V., Bernardino, J.: NoSQL databases: MongoDB vs cassandra. In: Proceedings of the International C* Conference on Computer Science and Software Engineering, pp. 14–22. ACM (2013)

    Google Scholar 

  2. Balouek, D., et al.: Adding virtualization capabilities to the Grid’5000 testbed. In: Ivanov, I.I., van Sinderen, M., Leymann, F., Shan, T. (eds.) CLOSER 2012. CCIS, vol. 367, pp. 3–20. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-04519-1_1

    Chapter  Google Scholar 

  3. Betke, E., Kunkel, J.: Real-time I/O-monitoring of HPC applications with SIOX, elasticsearch, Grafana and FUSE. In: Kunkel, J.M., Yokota, R., Taufer, M., Shalf, J. (eds.) High Performance Computing, pp. 174–186. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67630-2_15

    Chapter  Google Scholar 

  4. Chahal, D., Virk, R., Nambiar, M.: Performance extrapolation of IO intensive workloads: work in progress. In: Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering, pp. 105–108. ACM (2016)

    Google Scholar 

  5. Cooper, B.F., Silberstein, A., Tam, E., Ramakrishnan, R., Sears, R.: Benchmarking cloud serving systems with YCSB. In: Proceedings of the 1st ACM Symposium on Cloud Computing, pp. 143–154. ACM (2010)

    Google Scholar 

  6. Daoud, H., Dagenais, M.R.: Recovering disk storage metrics from low-level trace events. Softw.: Pract. Exp. 48(5), 1019–1041 (2018)

    Google Scholar 

  7. Desnoyers, M., Dagenais, M.R.: The LTTng tracer: a low impact performance and behavior monitor for GNU/Linux. In: OLS (Ottawa Linux Symposium), vol. 2006, pp. 209–224. Citeseer, Linux Symposium (2006)

    Google Scholar 

  8. Gandini, A., Gribaudo, M., Knottenbelt, W.J., Osman, R., Piazzolla, P.: Performance evaluation of NoSQL databases. In: Horváth, A., Wolter, K. (eds.) EPEW 2014. LNCS, vol. 8721, pp. 16–29. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10885-8_2

    Chapter  Google Scholar 

  9. Jacob, B., Larson, P., Leitao, B., Da Silva, S.: SystemTap: instrumenting the Linux kernel for analyzing performance and functional problems. IBM Redbook (2008)

    Google Scholar 

  10. Jeong, S., Lee, K., Hwang, J., Lee, S., Won, Y.: Androstep: Android storage performance analysis tool. Software Engineering (Workshops), vol. 13, pp. 327–340 (2013)

    Google Scholar 

  11. Jung, M.G., Youn, S.A., Bae, J., Choi, Y.L.: A study on data input and output performance comparison of MongoDB and PostgreSQL in the big data environment. In: 2015 8th International Conference on Database Theory and Application (DTA), pp. 14–17. IEEE (2015)

    Google Scholar 

  12. Klein, J., Gorton, I., Ernst, N., Donohoe, P., Pham, K., Matser, C.: Performance evaluation of NoSQL databases: a case study. In: Proceedings of the 1st Workshop on Performance Analysis of Big Data Systems (2015)

    Google Scholar 

  13. Manual page on Linux, B.: (2017). http://man7.org/linux/man-pages/man2/bpf.2.html

  14. Luo, X., et al.: HPC I/O trace extrapolation. In: Proceedings of the 4th Workshop on Extreme Scale Programming Tools. p. 2. ACM (2015)

    Google Scholar 

  15. Luo, X., et al.: ScalaiOExtrap: elastic I/O tracing and extrapolation. In: 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 585–594. IEEE (2017)

    Google Scholar 

  16. Mantri, S.G.: Efficient in-depth IO tracing and its application for optimizing systems. Ph.D. thesis, Virginia Tech (2014)

    Google Scholar 

  17. McDougall, R., Mauro, J., Gregg, B.: Solaris performance and tools: DTrace and MDB techniques for Solaris 10 and OpenSolaris. Prentice Hall (2006)

    Google Scholar 

  18. Collection project, B.C.: https://github.com/iovisor/bcc

  19. Schulist, J., Borkmann, D., Starovoitov, A.: Linux socket filtering aka Berkeley Packet Filter (BPF) (2016)

    Google Scholar 

  20. Sharma, S.D., Dagenais, M.: Enhanced userspace and in-kernel trace filtering for production systems. J. Comput. Sci. Technol. 6, 1161–1178 (2016)

    Article  Google Scholar 

  21. Starovoitov, A.: (2014). https://lwn.net/Articles/598545/

  22. Tak, B.C., Tang, C., Huang, H., Wang, L.: PseudoApp: performance prediction for application migration to cloud. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), pp. 303–310. IEEE (2013)

    Google Scholar 

  23. Vef, M.A., Tarasov, V., Hildebrand, D., Brinkmann, A.: Challenges and solutions for tracing storage systems: a case study with spectrum scale. ACM Trans. Storage 14(2), 1–24 (2018). https://doi.org/10.1145/3149376

    Article  Google Scholar 

  24. Virk, R., Chahal, D.: Trace replay based I/O performance studies for enterprise workload migration. In: 2nd Annual Conference of CMG India, Page Online (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Université de Lorraine, CNRS, Inria, LORIA, 54000, Nancy, France

    Abdulqawi Saif, Lucas Nussbaum & Ye-Qiong Song

  2. Qwant Enterprise, 88000, Épinal, France

    Abdulqawi Saif

Authors
  1. Abdulqawi Saif
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lucas Nussbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ye-Qiong Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdulqawi Saif .

Editor information

Editors and Affiliations

  1. Tokyo Institute of Technology, Tokyo, Japan

    Rio Yokota

  2. University of Edinburgh, Edinburgh, UK

    Michèle Weiland

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    John Shalf

  4. Swiss National Supercomputing Centre, Lugano, Switzerland

    Sadaf Alam

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Saif, A., Nussbaum, L., Song, YQ. (2018). IOscope: A Flexible I/O Tracer for Workloads’ I/O Pattern Characterization. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02465-9_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02464-2

  • Online ISBN: 978-3-030-02465-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation