Advertisement

Analysis of Web Workload on QoS to Assist Capacity

  • K. Abirami
  • N. Harini
  • P. S. Vaidhyesh
  • Priyanka Kumar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Workload characterization is a well-established discipline, which finds its applications in performance evaluation of modern Internet services. With the high degree of popularity of the Internet, there is a huge variation in the intensity of workload and this opens up new challenging performance issues to be addressed. Internet Services are subject to huge variations in demand, with bursts coinciding with the times that the service has the most value. Apart from these flash crowds, sites are also subject to denial-of-service (DoS) attacks that can knock a service out of commission. The paper aims to study the effect of various workload distributions with the service architecture ‘thread-per-connection’ in use as a basis. The source model is structured as a sequence of activities with equal execution time requirement with an additional load time of page (loading embedded objects, images, etc.). The threads are allocated to the requests in the queue; leftover requests if any are denied service. The rejection rate is used as a criterion for evaluation of the performance of the system with a given capacity. The proposed model could form a basis for various system models to be integrated into the system and get its performance metrics (i.e. QoS) evaluated.

Keywords

Workload distribution Workload modeling Workload characterization Performance evaluation Image loading time 

References

  1. 1.
    Calzarossa MC, Massari L, Tessera D (2016) Workload characterization: a survey. ACM Comput Surv (CSUR) 48(3):48CrossRefGoogle Scholar
  2. 2.
    Galletta DF, Henry R, Mccoy S, Polak P (2004) Web site delays: how tolerant are users. J Assoc Inform Syst pp 1–28CrossRefGoogle Scholar
  3. 3.
    Goncalves MA, Almeida JM, dos Santos LG, Laender AH, Almeida V (2010) On popularity in the blogosphere. IEEE Internet Comput 14(3):42–49CrossRefGoogle Scholar
  4. 4.
    Gusella R (1991) Characterizing the variability of arrival processes with indexes of dispersion. IEEE J Sel Areas Commun 9(2):203–211CrossRefGoogle Scholar
  5. 5.
    Gunther NJ (2001) Performance and scalability models for a hypergrowth e-commerce web site. In: Performance engineering, state of the art and current trends. Springer, London, UK, pp 267–282Google Scholar
  6. 6.
    Levene M, Borges J, Loizou G (2001) Zipfs law for web surfers. Knowl Inf Syst 3(1):120–129CrossRefGoogle Scholar
  7. 7.
    Menasce D, Almeida V, Riedi R, Ribeiro F, Fonseca R, Meira W Jr (2000) In search of invariants for e-business workloads. In: EC 00: proceedings of the 2nd ACM conference on electronic commerce, New York, NY, USA. ACM, pp. 56–65Google Scholar
  8. 8.
    Mi N, Casale G, Cherkasova L, Smirni E (2008) Burstiness in multi-tier applications: symptoms, causes, and new models. In: Middleware 08Proceedings of the 9th ACM/IFIP/USENIX international conference on middleware, New York, NY, USA. Springer, New York, Inc., pp 265–286CrossRefGoogle Scholar
  9. 9.
    Harini N, Padmanabhan TR (2012) A secured-concurrent available architecture for improving performance of web services. In: Communications in computer and information science, vol 292, no 1. Springer, pp 621–631Google Scholar
  10. 10.
    Harini N, Padmanabhan TR (2013) Admission control and request scheduling for secured-concurrent-available architecture. Int J Comput Appl 63(6):24–30Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • K. Abirami
    • 1
  • N. Harini
    • 1
  • P. S. Vaidhyesh
    • 1
  • Priyanka Kumar
    • 1
  1. 1.Department of Computer Science and Engineering, Amrita School of EngineeringAmrita Vishwa VidyapeethamCoimbatoreIndia

Personalised recommendations