Characteristics of Temporal and Spatial Locality of Internet Access Patterns

  • Keisuke Ishibashi
  • Masaki Aida
  • Makoto Imase
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2376)


The locality of access patterns is a significant characteristic to be considered in analyzing Internet access behaviors. Previous research focused on temporal locality, which implies a high probability of the same IP address reappearing. In this paper, in addition to temporal locality we analyze spatial locality behavior, which implies a high probability of neighboring IP addresses appearing. Using actual Internet traces, we have analyzed the relationship between the number of accesses and the number of distinct elements appearing for both full addresses and address prefixes. We found that the number of distinct full addresses appearing grows much faster than the number of address prefixes, and they are related by a power law. Also, we compose a stochastic model that generates an address sequence with hierarchical structure consisting of full address and address prefix. We verify that an address sequence generated by the model shows both spatial and temporal locality behaviors similar to those of actual data.


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  1. 1.
    R. Jain, “Characteristics of destination address locality in computer networks: A comparison of caching schemes,” DEC Tech. Rep., 1990.Google Scholar
  2. 2.
    M. Aida and T. Abe, “Pseudo-address generation algorithm of packet destinations for Internet performance simulation,” proc. IEEE INFOCOM 2001, April 2001.Google Scholar
  3. 3.
    V. Almeida, A. Bestavros, M. Crovella, and A. Oliveira, “Characterizing reference locality in the WWW,” proc. 4th Int. Conf. on Parallel and Distributed Information Systems (PDIS’ 96), December 1996.Google Scholar
  4. 4.
    A. Bestavros, “Speculative data dissemination and service to reduce server load, network traffic and service time in distributed information systems,” proc. 4th Int. Conf. on Data Engineering (ICDE’ 96), March 1996.Google Scholar
  5. 5.
    V. N. Padmanabhan and J. C. Mogul, “Using predictive prefetching to improve World Wide Web latency,” proc. ACM SIGCOMM 96, 1996.Google Scholar
  6. 6.
    Wenjia Fang and Larry Peterson, “Inter-AS Traffic Patterns and Their Implications,” proc. Global Internet 99, Rio, Brazil.Google Scholar
  7. 7.
    M. Kobayashi and M.H. MacDougall, “The stack growth function: Cache line reference models,” IEEE Trans. Comput., vol.38, no.6, 1989.Google Scholar
  8. 8.
    Internet Performance Measurement and Analysis Project, “Internet Routing Table Statistics,”

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Keisuke Ishibashi
    • 1
  • Masaki Aida
    • 1
  • Makoto Imase
    • 1
  1. 1.Information Sharing Platform LaboratoriesNTT CorporationTokyoJapan

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