English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Measurement-Based Analysis, Modeling, and Synthesis of the Internet Delay Space

MPS-Authors
/persons/resource/persons180339

Nandi,  Animesh
Group P. Druschel, Max Planck Institute for Software Systems, Max Planck Society;

/persons/resource/persons144511

Druschel,  Peter
Group P. Druschel, Max Planck Institute for Software Systems, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Zhang, B., Ng, T. S. E., Nandi, A., Riedi, R., Druschel, P., & Wang, G. (2006). Measurement-Based Analysis, Modeling, and Synthesis of the Internet Delay Space. In Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement 2006 (pp. 85-98). New York, USA: ACM.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0028-8C89-D
Abstract
Understanding the characteristics of the Internet delay space (i.e., the all-pairs set of static round-trip propagation delays among edge networks in the Internet) is important for the design of global-scale distributed systems. For instance, algorithms used in overlay networks are often sensitive to violations of the triangle inequality and to the growth properties within the Internet delay space. Since designers of distributed systems often rely on simulation and emulation to study design alternatives, they need a realistic model of the Internet delay space. Our analysis shows that existing models do not adequately capture important properties of the Internet delay space. In this paper, we analyze measured delays among thousands of Internet edge networks and identify key properties that are important for distributed system design. Furthermore, we derive a simple model of the Internet delay space based on our analytical findings. This model preserves the relevant metrics far better than existing models, allows for a compact representation, and can be used to synthesize delay data for simulations and emulations at a scale where direct measurement and storage are impractical.