Skip to main content
SpringerLink
Log in

ADLER: Adaptive Sampling for Precise Monitoring

  • Conference paper
  • First Online:
Languages and Compilers for Parallel Computing (LCPC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11403))

  • 356 Accesses

Abstract

In this paper, we present ADLER, a tool for profiling applications using a sampling frequency that is tuned at program runtime. ADLER can not only determine the adaptive sampling rate for any application, but also can instrument the code for profiling so that different parts of the application can be sampled at different frequencies. The frequencies are selected to provide enough information without collecting redundant data. ADLER uses performance models of program kernels and prepare the kernels for sampling according to their complexity classes. We also show an example use case of real-time anomaly detection, where using ADLER’s execution models, the anomalies can be detected 23% quicker than static sampling.

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

References

  1. Yahoo Cloud Service Benchmarks. https://research.yahoo.com/news/yahoo-cloud-serving-benchmark/

  2. Arnold, M., Ryder, B.G.: A framework for reducing the cost of instrumented code. ACM SIGPLAN Not. 36(5), 168–179 (2001)

    Article  Google Scholar 

  3. Barham, P., Donnelly, A., Isaacs, R., Mortier, R.: Using magpie for request extraction and workload modelling. In: OSDI, vol. 4, p. 18 (2004)

    Google Scholar 

  4. Bhattacharyya, A., Hoefler, T.: Pemogen: automatic adaptive performance modeling during program runtime. In: 2014 23rd International Conference on Parallel Architecture and Compilation Techniques (PACT), pp. 393–404. IEEE (2014)

    Google Scholar 

  5. Bhattacharyya, A., Jandaghi, S.A.J., Sotiriadis, S., Amza, C.: Semantic aware online detection of resource anomalies on the cloud. In: 2016 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), pp. 134–143. IEEE (2016)

    Google Scholar 

  6. Bhattacharyya, A., Kwasniewski, G., Hoefler, T.: Using compiler techniques to improve automatic performance modeling. In: 2015 International Conference on Parallel Architecture and Compilation (PACT), pp. 468–479. IEEE (2015)

    Google Scholar 

  7. Chen, M.Y., Kiciman, E., Fratkin, E., Fox, A., Brewer, E.: Pinpoint: problem determination in large, dynamic internet services. In: Null, p. 595. IEEE (2002)

    Google Scholar 

  8. Symantec Corporation: Symantec i3 for J2EE - performance management for the J2EE platform

    Google Scholar 

  9. Jandaghi, S.J., Bhattacharyya, A., Sotiriadis, S., Amza, C.: Consolidation of underutilized virtual machines to reduce total power usage. In: Proceedings of the 26th Annual International Conference on Computer Science and Software Engineering, pp. 128–137. IBM Corp. (2016)

    Google Scholar 

  10. Kumar, N., Childers, B.R., Soffa, M.L.: Low overhead program monitoring and profiling. ACM SIGSOFT Softw. Eng. Notes 31(1), 28–34 (2006)

    Article  Google Scholar 

  11. Magalhaes, J.P., Silva, L.M.: Adaptive profiling for root-cause analysis of performance anomalies in web-based applications. In: 2011 10th IEEE International Symposium on Network Computing and Applications (NCA), pp. 171–178. IEEE (2011)

    Google Scholar 

  12. Munawar, M.A., Ward, P.: Adaptive monitoring in enterprise software systems. SysML, June 2006

    Google Scholar 

  13. Padmanabha, S., Lukefahr, A., Das, R., Mahlke, S.: Trace based phase prediction for tightly-coupled heterogeneous cores. In: Proceedings of the 46th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 445–456. ACM (2013)

    Google Scholar 

  14. Rish, I., et al.: Adaptive diagnosis in distributed systems. IEEE Trans. Neural Netw. 16(5), 1088–1109 (2005)

    Article  Google Scholar 

  15. Sherwood, T., Perelman, E., Hamerly, G., Sair, S., Calder, B.: Discovering and exploiting program phases. IEEE Micro 23(6), 84–93 (2003)

    Article  Google Scholar 

  16. Vallée-Rai, R., Co, P., Gagnon, E., Hendren, L., Lam, P., Sundaresan, V.: Soot: a java bytecode optimization framework. In: CASCON First Decade High Impact Papers, pp. 214–224. IBM Corp. (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada

    Arnamoy Bhattacharyya & Cristiana Amza

Authors
  1. Arnamoy Bhattacharyya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cristiana Amza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arnamoy Bhattacharyya .

Editor information

Editors and Affiliations

  1. Texas A&M University, College Station, TX, USA

    Lawrence Rauchwerger

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bhattacharyya, A., Amza, C. (2019). ADLER: Adaptive Sampling for Precise Monitoring. In: Rauchwerger, L. (eds) Languages and Compilers for Parallel Computing. LCPC 2017. Lecture Notes in Computer Science(), vol 11403. Springer, Cham. https://doi.org/10.1007/978-3-030-35225-7_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-35225-7_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35224-0

  • Online ISBN: 978-3-030-35225-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation