KIPS Transactions on Computer and Communication Systems (정보처리학회논문지:컴퓨터 및 통신 시스템)

Korea Information Processing Society (한국정보처리학회)
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Characterizing the Tail Distribution of Android IO Workload

안드로이드 입출력 부하의 꼬리분포 특성분석

  • 박창현 (한양대학교 컴퓨터소프트웨어학과) ;
  • 원유집 (한국과학기술원 전기및전자공학부) ;
  • 박영준 (한양대학교 컴퓨터소프트웨어학과)
  • Received : 2019.07.25
  • Accepted : 2019.09.19
  • Published : 2019.10.31
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Abstract

The use of NAND flash memory has increased rapidly due to the development of mobile fields. However, NAND flash memory has a limited lifespan, so studies are underway to predict its lifespan. Workload is one of the factors that significantly affect the life of NAND flash memory, and workload analysis studies in mobile environments are insufficient. In this paper, we analyze the distribution of workload in the mobile environment by collecting traces generated by using Android-based smartphones. The collected traces can be divided into three groups of hotness. Also they are distributed in the form of heavy tails. We fit this to the Pareto, Lognormal, and Weibull distributions, and Traces are closest to the Pareto distribution.

모바일 분야의 발전으로 인해 낸드 플래시 메모리의 사용이 급증하였다. 그러나 낸드 플래시 메모리는 수명에 제한이 있어서 수명을 예측하기 위한 연구가 진행되고 있다. 낸드 플래시 메모리의 수명에 큰 영향을 주는 요소 중 하나가 워크로드인데, 모바일 환경에서의 워크로드 분석 연구는 미비하다. 이에 본 논문에서는 안드로이드 기반의 스마트폰을 사용하면서 발생하는 트레이스를 수집하고, 모바일 환경에서의 워크로드 분포를 분석하였다. 수집한 트레이스는 hotness 그룹을 3개로 분류할 수 있다. 또한 트레이스의 분포는 무거운 꼬리를 가지는 형태이다. 본 논문은 이를 Pareto, Lognormal, Weibull 분포에 피팅하였고, 그 결과 Pareto 분포에 가장 가까운 것을 확인하였다.

Keywords

References

  1. S. Kavalanekar, B. Worthington, Q. Zhang, and V. Sharda, "Characterization of Storage Workload Traces from Production Windows Servers," Proceedings of the 2008 IEEE International Symposium on Workload Characterization, pp. 119-128. Seattle, WA, USA. Sep. 2008.
  2. P. Carns, R. Latham, R. Ross, K. Iskra, S. Lang, and K. Riley, "24/7 Characterization of Petascale I/O Workloads," Proceedings of the 2009 IEEE International Conference on Cluster Computing and Workshops, New Orleans, Louisiana, USA. Aug. 2009.
  3. A. Khan, X. Yan, S. Tao, and N. Anerousis, "Workload Characterization and Prediction in the Cloud: A Multiple Time Series Approach," Proceedings of the 2012 IEEE Network Operations and Management Symposium, pp.1287-1294. Maui, Hawaii, USA. Apr. 2012.
  4. E. Casalicchio, S. Iannucci, and L. Silvestri, "Cloud Desktop Workload: a Characterization Study," Proceedings of the 2015 IEEE International Conference on Cloud Engineering, pp. 66-75. Tempe. AZ. USA. Mar. 2015.
  5. S. Liu, X. Huang, H. Fu, and G. Yang, "Understanding Data Characteristics and Access Patterns in a Cloud Storage System," Proceedings of the 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, pp.327-334. Delft, Netherlands. May 2013.
  6. B. Atikoglu, Y. Xu, E. Frachtenberg, S. Jiang, and M. Paleczny, "Workload Analysis of a Large-Scale Key-Value Store," Proceedings of the 12th ACM SIGMETRICS/ PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems, pp.53-64. London, UK. Jun. 2012.
  7. M. Alam, K. A. Shakil, and S. Sethi, "Analysis and Clustering of Workload in Google Cluster Trace based on Resource Usage," Proceedings of the 2016 IEEE Intl Conference on Computational Science and Engineering (CSE) and IEEE Intl Conference on Embedded and Ubiquitous Computing (EUC) and 15th Intl Symposium on Distributed Computing and Applications for Business Engineering (DCABES), pp. 740-747. Paris, France. Aug. 2016.
  8. Y. Liu, R. Gunasekaran, X. Ma, and S. S. Vazhkudai, "Server-side Log Data Analytics for I/O Workload Characterization and Coordination on Large Shared Storage Systems," Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, pp.819-829. Salt Lake City, Utah, USA. Nov. 2016.
  9. R. Gunasekaran, S. Oral, J. Hill, R. Miller, F. Wang, and D. Leverman, "Comparative I/O Workload Characterization of Two Leadership Class Storage Clusters," Proceedings of the 10th Parallel Data Storage Workshop, pp.31-36. Austin, TX. USA. Nov. 2015.
  10. J. P. White, M. Innus, M. D. Jones, R. L. DeLeon, N. Simakov, J. T. Palmer, S. M. Gallo, T. R. Furlani, M. Showerman, R. Brunner, A. Kot, G. Bauer, B. Bode, J. Enos, and W. Kramer, "Challenges of Workload Analysis on Large HPC Systems; A Case Study on NCSA Blue Waters," Proceedings of the Practice and Experience in Advanced Research Computing, New Orleans, Louisiana, USA. Jul. 2017.
  11. E. Lim, S. Lee, and Y. Won, "Androtrace: Framework for Tracing and Analyzing IOs on Android." Proceedings of the 3rd Workshop on Interactions of NVM/FLASH with Operating Systems and Workloads, Monterey, CA, USA, Oct. 2015.
  12. C. Min, K. Kim, H. Cho, S.-W. Lee, and Y. I. Eom, "SFS: Random Write Considered Harmful in Solid State Drives," Proceedings of the 10th USENIX conference on File and Storage Technologies, San Jose, CA, USA. Feb. 2012.
  13. B. Seo, S. Kang, J. Choi, J. Cha Y. Won, and S. Yoon, "Io workload characterization revisited: A data-mining approach," IEEE Trans. Computer, Vol.63, No.12, pp.3026- 3038, Dec. 2014. https://doi.org/10.1109/TC.2013.187
  14. P. N. Tan, M. Steinbach, and V. Kumar, "Introduction to Data Mining, (First Edition)," Addison-Wesley Longman Publishing Co., Inc., 2005.
  15. B. C. Arnold, "Pareto Distributions (Second Edition)," CRC Press, Taylor & Francis Group, 2015.
  16. R, Adler, R. Feldman, and M. Taqqu, "A Practical Guide to Heavy Tails: Statistical Techniques and Applications," Birkhauser, 1998.