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WORKLOAD CHARACTERIZATION OF TELECOM SOFTWARE

Year 2020, Volume: 8 Issue: 1, 151 - 163, 05.03.2020
https://doi.org/10.36306/konjes.698708

Abstract

Telecom software systems are a major aspect of telecommunications networks used for managing, administrating, auditing and monitoring purposes. One of the most significant features is the performance of the telecom software system, since there is a great variety of network elements in use within built systems through which the telecom software interacts. Workload characterization is an essential methodology for simulating load usage for a given infrastructure that is exposed to a production environment. Hence, exploring the characteristics of workloads well leads an increase on system efficiency and performance. Nevertheless, workload characterization has not been well examined yet for Telecom Software System. In this paper, a new workload characterization architecture is proposed and implemented to model the workload and simulate the behavior of the network element. This architecture provides information regarding the accuracy of the workload models for more precise performance testing. Additionally, we verify our workload characterization results obtained from our architecture in a real software system. Performance results show that proposed workload model-based simulation environment is promising to predict the performance of the software systems.

References

  • Almeida, V., Fonseca R., Menasce, D.A., Mendes M.A., 1999. A Methodology for Workload Characterization of E-commerce Sites. In ACM Conference on Electronic Commerce.
  • Amrehn, E., Busch A., Kounev S., Koziolek A., Noorshams Q., Reussner R., Jan. 2015. Automated work-load characterization for i/o performance analysis in virtualized environments. In Proc. of the 6th ACM/SPEC International Conference on Performance Engineering, pp. 265-276.
  • Avritzer, A., Kondek J., D. Liu., July 2002. Software performance testing based on workload characterization.Proceedings of the 3rd international workshop on Software and performance.
  • Calzarossa M.C., Luisa M., Tessera D. 2016. Workload Characterization: A Survey Revisited. ACM Computing Surveys, Vol. 48, No. 3, Article 48.
  • Casalicchio E., Iannucci S., Silvestri L., Mar. 2015. Cloud desktop workload: A characterization study. In IEEE International Conference on Cloud Engineering (IC2E). IEEE, pp. 66-75.
  • Celik, I., Gunaydın M., Resber C., Sonmez O., Tasdemir B., Unlu O.F. 2015.Network Element Simulation Based On Log Files. ICCIT Conference.
  • Curiel, M., Pont A., January 2018. Workload Generators for Web-Based Systems: Characteristics, Current Status and Challenges. In IEEE Communication Surveys & Tutorials.
  • Daniel, V., Mahmoud A., November 3-5 2015. Automated Workload Characterization Using System Log Analysis. In Computer Measurement Group Conf., San Antonio. Feitelson, D. G., Rudolph L., May 1996. Evaluation of design choices for gang scheduling using distributed hierarchical control. J. Parallel & Distributed Comput. 35(1), pp. 18-34, DOI:10.1006/jpdc.1996.0064.
  • Feitelson, D. G., 21 Oct 2014. Workload Modeling for Computer Systems Performance Evaluation. Cambridge University Press.
  • Feitelson, D. G., Zakay N., 2014. Workload resampling for performance evaluation of parallel job schedulers. Concurrency and Computation: Practice and Experience, Vol. 26, No. 12, pp. 2079-2105.
  • Franks, G., Woodside M., Petriu D.C., 2007. Future software performance engineering. Washington: IEEE Computer Society, pp. 171-187.
  • Goren, H., Gorgun O., Yigit M., February 2017. IMS Automated Audit and Configuration: Parameters Audit SmartApp (PAS). International Journal of Electronics and Electrical Engineering, Vol. 5, No. 1.
  • Gruttner, K., Hartmann P. A., Ittershagen P., Nebel W., 2015. A Workload Extraction Framework for Software Performance Model Generation. RAPIDO '15 Proceedings of the 2015 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, Article No. 3.
  • Hasselbring, W., Rohr M., Van Hoorn A., 2008. Generating probabilistic and intensity-varying workload for Web-based software systems. In Proc. SIPEW '08, pp. 124-143.
  • Hasselbring, W., Krcmar H., Schulz E., Van Hoorn A., Vogele C., 2014. Automatic extraction of probabilistic workload specifications for load testing session-based application systems. In Proc. Valuetools.
  • Helm, B. R., Malony A. D., September 2001. A theory and architecture for automating performance diagnosis. Future Generation Computer Systems - I. High Performance Numerical Methods and Applications. II. Performance Data Mining: Automated Diagnosis, Adaption, and Optimization archive, Volume 18, Issue 1, pp. 189-200.
  • Jain, R., 2008. The art of computer systems performance analysis. John Wiley & Sons.
  • Java Swing Framework - https://en.wikipedia.orgnwikinSwing (Java)
  • Menasce, D. A., Pentakalos O. I., Yesha Y., 1996. Automated Clustering-Based Workload Characterization. In Proceedings of the 5th NASA Goddard Mass Storage Systems and Technologies Conference.
  • Nakaike, T., Ohara M., Ueda T., Sept. 2016. Workload Characterization for Microservices. In IEEE Workload Characterization (IISWC), pp. 25-27.
  • Neuts, M. F., 17 July 1989. Structured stochastic matrices of M/G/1 type and their applications. CRC Press.
  • Politi, R., Ruffo G., Schifanella R., Sereno M., 2004. WALTy (Web application load-based testing tool): A User Behavior Tailored Tool for Evaluating Web Application Performance. In: Proceedings of the third IEEE International Symposium on Network Computing and Applications (NCA04).
  • Saraswathi, S., Sheela M. I., November 2014. A Comparative Study of Various Clustering Algorithms in DataMining. IJCSMC, Vol. 3 Issue 11, pp.422-428.
  • Shirasb, S., 1983. Workload Modeling and Characterization in Computer Systems Performance Evaluation. North Dakota State University Press.
  • Tietjen, G.L., 1986. The analysis and detection of outliers. In Goodness-of-Fit Techniques, R. B. D'Agostino and M. A. Stephens (eds.), pp. 497-522, Marcel Dekker, Inc.
  • WEKA Java API - http://weka.sourceforge.netndoc.stablen
  • Xu D., Tian Y., June 2015. A Comprehensive Survey of Clustering Algorithms. Volume 2 Issue 2 pp 165-193.

Telekom Yazılımı İş Yükü Karakterizasyonu

Year 2020, Volume: 8 Issue: 1, 151 - 163, 05.03.2020
https://doi.org/10.36306/konjes.698708

Abstract

Telekom yazılım sistemleri, yönetim, denetim ve izleme amacıyla kullanılan telekomünikasyon ağlarının önemli bir parçasıdır. Telekom yazılım sistemlerinin en önemli özelliklerinden biri performansıdır. Bunun nedeni telekom yazılımının etkileşim içinde olduğu yerleşik sistemler içinde kullanılan çok farklı ağ elemanları vardır. İş yükü karakterizasyonu, üretim ortamına maruz kalan belirli bir altyapı için yük kullanımını simüle etmek için temel bir metodolojidir. Bu nedenle, iş yüklerinin özelliklerini iyi araştırmak, sistem verimliliği ve performansında artışa neden olmaktadır. Bununla birlikte, literatürde iş yükü karakterizasyonunun Telekom Yazılım Sistemlerinin performansına etkileri henüz iyi bir şekilde incelenmemiştir. Bu yazıda, iş yükünü modellemek ve ağ elemanının davranışını simüle etmek için yeni bir iş yükü karakterizasyon mimarisi önerilmiş ve uygulanmıştır. Bu mimari, daha yüksek doğrulukta performans testleri için iş yükü modellerinin doğruluğu hakkında bilgi sağlamaktadır. Ek olarak, çalışma da önerilen mimariden elde edilen iş yükü karakterizasyon sonuçları gerçek bir yazılım sistemi ile doğrulanmaktadır. Performans sonuçları, önerilen iş yükü modeline dayalı simülasyon ortamının yazılım sistemlerinin performansını öngörmeyi vaat ettiğini göstermektedir.

References

  • Almeida, V., Fonseca R., Menasce, D.A., Mendes M.A., 1999. A Methodology for Workload Characterization of E-commerce Sites. In ACM Conference on Electronic Commerce.
  • Amrehn, E., Busch A., Kounev S., Koziolek A., Noorshams Q., Reussner R., Jan. 2015. Automated work-load characterization for i/o performance analysis in virtualized environments. In Proc. of the 6th ACM/SPEC International Conference on Performance Engineering, pp. 265-276.
  • Avritzer, A., Kondek J., D. Liu., July 2002. Software performance testing based on workload characterization.Proceedings of the 3rd international workshop on Software and performance.
  • Calzarossa M.C., Luisa M., Tessera D. 2016. Workload Characterization: A Survey Revisited. ACM Computing Surveys, Vol. 48, No. 3, Article 48.
  • Casalicchio E., Iannucci S., Silvestri L., Mar. 2015. Cloud desktop workload: A characterization study. In IEEE International Conference on Cloud Engineering (IC2E). IEEE, pp. 66-75.
  • Celik, I., Gunaydın M., Resber C., Sonmez O., Tasdemir B., Unlu O.F. 2015.Network Element Simulation Based On Log Files. ICCIT Conference.
  • Curiel, M., Pont A., January 2018. Workload Generators for Web-Based Systems: Characteristics, Current Status and Challenges. In IEEE Communication Surveys & Tutorials.
  • Daniel, V., Mahmoud A., November 3-5 2015. Automated Workload Characterization Using System Log Analysis. In Computer Measurement Group Conf., San Antonio. Feitelson, D. G., Rudolph L., May 1996. Evaluation of design choices for gang scheduling using distributed hierarchical control. J. Parallel & Distributed Comput. 35(1), pp. 18-34, DOI:10.1006/jpdc.1996.0064.
  • Feitelson, D. G., 21 Oct 2014. Workload Modeling for Computer Systems Performance Evaluation. Cambridge University Press.
  • Feitelson, D. G., Zakay N., 2014. Workload resampling for performance evaluation of parallel job schedulers. Concurrency and Computation: Practice and Experience, Vol. 26, No. 12, pp. 2079-2105.
  • Franks, G., Woodside M., Petriu D.C., 2007. Future software performance engineering. Washington: IEEE Computer Society, pp. 171-187.
  • Goren, H., Gorgun O., Yigit M., February 2017. IMS Automated Audit and Configuration: Parameters Audit SmartApp (PAS). International Journal of Electronics and Electrical Engineering, Vol. 5, No. 1.
  • Gruttner, K., Hartmann P. A., Ittershagen P., Nebel W., 2015. A Workload Extraction Framework for Software Performance Model Generation. RAPIDO '15 Proceedings of the 2015 Workshop on Rapid Simulation and Performance Evaluation: Methods and Tools, Article No. 3.
  • Hasselbring, W., Rohr M., Van Hoorn A., 2008. Generating probabilistic and intensity-varying workload for Web-based software systems. In Proc. SIPEW '08, pp. 124-143.
  • Hasselbring, W., Krcmar H., Schulz E., Van Hoorn A., Vogele C., 2014. Automatic extraction of probabilistic workload specifications for load testing session-based application systems. In Proc. Valuetools.
  • Helm, B. R., Malony A. D., September 2001. A theory and architecture for automating performance diagnosis. Future Generation Computer Systems - I. High Performance Numerical Methods and Applications. II. Performance Data Mining: Automated Diagnosis, Adaption, and Optimization archive, Volume 18, Issue 1, pp. 189-200.
  • Jain, R., 2008. The art of computer systems performance analysis. John Wiley & Sons.
  • Java Swing Framework - https://en.wikipedia.orgnwikinSwing (Java)
  • Menasce, D. A., Pentakalos O. I., Yesha Y., 1996. Automated Clustering-Based Workload Characterization. In Proceedings of the 5th NASA Goddard Mass Storage Systems and Technologies Conference.
  • Nakaike, T., Ohara M., Ueda T., Sept. 2016. Workload Characterization for Microservices. In IEEE Workload Characterization (IISWC), pp. 25-27.
  • Neuts, M. F., 17 July 1989. Structured stochastic matrices of M/G/1 type and their applications. CRC Press.
  • Politi, R., Ruffo G., Schifanella R., Sereno M., 2004. WALTy (Web application load-based testing tool): A User Behavior Tailored Tool for Evaluating Web Application Performance. In: Proceedings of the third IEEE International Symposium on Network Computing and Applications (NCA04).
  • Saraswathi, S., Sheela M. I., November 2014. A Comparative Study of Various Clustering Algorithms in DataMining. IJCSMC, Vol. 3 Issue 11, pp.422-428.
  • Shirasb, S., 1983. Workload Modeling and Characterization in Computer Systems Performance Evaluation. North Dakota State University Press.
  • Tietjen, G.L., 1986. The analysis and detection of outliers. In Goodness-of-Fit Techniques, R. B. D'Agostino and M. A. Stephens (eds.), pp. 497-522, Marcel Dekker, Inc.
  • WEKA Java API - http://weka.sourceforge.netndoc.stablen
  • Xu D., Tian Y., June 2015. A Comprehensive Survey of Clustering Algorithms. Volume 2 Issue 2 pp 165-193.
There are 27 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Osman Ferit Ünlü This is me

Pınar Bölük This is me 0000-0001-8274-8423

Publication Date March 5, 2020
Submission Date March 1, 2019
Acceptance Date July 30, 2019
Published in Issue Year 2020 Volume: 8 Issue: 1

Cite

IEEE O. F. Ünlü and P. Bölük, “WORKLOAD CHARACTERIZATION OF TELECOM SOFTWARE”, KONJES, vol. 8, no. 1, pp. 151–163, 2020, doi: 10.36306/konjes.698708.