Locality prediction for oblivious clients

Kevin P. Shanahan, Michael J. Freedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

To improve performance, large-scale Internet systems require clients to access nearby servers. While centralized systems can leverage static topology maps for rough network distances, fully-decentralized systems have turned to active probing and network coordinate algorithms to scalably predict inter-host latencies. Internet applications seeking immediate adoption, however, must inter-operate with unmodified clients running existing protocols such as HTTP and DNS. This paper explores a variety of active probing algorithms for locality prediction. Upon receiving an external client request, peers within a decentralized system are able to quickly estimate nearby servers, using a minimum of probes from multiple vantages. We find that, while network coordinates may play an important role in scalably choosing effective vantage points, they are not directly useful for predicting a client's nearest servers.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages252-263
Number of pages12
DOIs
StatePublished - Dec 1 2005
Externally publishedYes
Event4th International Workshop on Peer-to-Peer Systems, IPTPS 2005 - Ithaca, NY, United States
Duration: Feb 24 2005Feb 25 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3640 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Peer-to-Peer Systems, IPTPS 2005
CountryUnited States
CityIthaca, NY
Period2/24/052/25/05

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Shanahan, K. P., & Freedman, M. J. (2005). Locality prediction for oblivious clients. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 252-263). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3640 LNCS). https://doi.org/10.1007/11558989_23