Motivation

Utilities for generating artificial (synthetic) loads are very important for analyses of performance and behavior of networks and their offered services. Load generators implemented by the industry are mainly dedicated hardware components with very high performance and stringent precision requirements. In research and academia, mainly software based load generators are commonly used because of the expected higher flexibility in operation and maintenance (e.g. due to easy deployment of constituent load generating modules in the network, code customizations for a specific research purpose, etc.) while components of real operating systems and protocol stacks can be used to guarantee realistic load generation at lower costs. However, many existing tools are dedicated to a specific modeling study (e.g., Guernica [1] along with its specific Dweb model for Web traffic, or Harpoon [2] modeling IP traffic flows) or are focusing on generating traffic at some specific interface in a network (e.g., ITG [3] or Brute [4] were designed to generate traffic on UDP and TCP service interfaces). The proposed solutions quite often do not provide an adequate flexibility, e.g. in case the underlying model is to be modified or a completely new model is to be used. Therefore, the unified load generator UniLoG is presented in this paper, which combines the specification and generation of network loads in one single coherent approach. The basic principle underlying the design and elaboration of UniLoG is to start with a formal description of an abstract load model by means of a finite user behavior automaton (UBA, introduced in Sec. 2) and thereafter to use interface-specific adapters to map the abstract requests to the concrete requests as they are “understood” by the service providing component at the real interface in question. An overview of the distributed UniLoG architecture is given in Sec. 3 and a concrete example of its practical use in QoS studies for video streaming is demonstrated in Sec. 4.

Keywords

Video Streaming Service Interface Load Agent Protocol Stack Easy Deployment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pena-Ortiz, R., et al.: Dweb model: Representing Web 2.0 dynamism. Computer Communications 32(6), 1118–1128 (2009)CrossRefGoogle Scholar
  2. 2.
    Sommers, J., Barford, P.: Self-Configuring Network Traffic Generation. In: Proc. of IMC 2004, Taormina, Sicily, pp. 68–80 (2004)Google Scholar
  3. 3.
    Avallone, S., Pescape, A., Ventre, G.: Analysis and experimentation of Internet Traffic Generator. In: Proc. of New2an 2004, St. Petersburg, Russia, pp. 70–75 (2004)Google Scholar
  4. 4.
    Bonelli, N., et al.: BRUTE: A High Performance and Extensible Traffic Generator. In: Proc. of SPECTS 2005, Philadelphia, pp. 839–845 (2005)Google Scholar
  5. 5.
    Kolesnikov, A., Kulas, M.: Load Modeling and Generation for IP-Based Networks: A Unified Approach and Tool Support. In: Müller-Clostermann, B., Echtle, K., Rathgeb, E.P. (eds.) MMB&DFT 2010. LNCS, vol. 5987, pp. 91–106. Springer, Heidelberg (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Andrey Kolesnikov
    • 1
  1. 1.Department of Computer ScienceUniversity of HamburgHamburgGermany

Personalised recommendations