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Facility location models to locate data in information networks: a literature review

Author

Listed:
  • Goutam Sen

    (IIT Bombay)

  • Mohan Krishnamoorthy

    (IIT Bombay)

  • Narayan Rangaraj

    (IIT Bombay)

  • Vishnu Narayanan

    (IIT Bombay)

Abstract

The usage of the Internet has grown substantially in recent times. This has resulted in high volumes of data traffic. There is a concomitant rise in bandwidth demands that could result in excessive download delays (or latency). Thus, a single-server system is no more a prudent choice for data storage. Replication of content and placing them on multiple servers is a method that is used to reduce latency. However, this solution comes at a huge cost. Moreover, replicating objects randomly does not necessarily improve system performance. It is possible to arrive at a solution to the problem of placing content so as to achieve better cost performance. Other performance measures include latency, load balancing and data availability. We refer to the problem of locating content as data location problem in information networks, or DLPIN. The choice of server locations, query routing strategy and user assignment are some of the important problems that require attention along with the location of the data/content. Resource constraints and the nature of traffic (static/dynamic) are two important parameters in the problem environment, and therefore are key distinguishing features in the models. The main contribution of this paper is a novel classification and study of DLPIN on the basis of problem features. The research in this area started with files, the smallest units of allocation. Gradually, files and programs, database segments and entire databases (or mirrors) have been studied. We design examples from these use cases to elaborate a variety of problems in a comprehensive review. Facility location models from physical logistics are extensively used to model these problems. Our paper presents a literature survey of such mathematical models for data location problems. We present a gap analysis that provides pointers to possible future research in this area. This paper also serves to document the success in the use of mathematical programming approaches for data location in information networks.

Suggested Citation

  • Goutam Sen & Mohan Krishnamoorthy & Narayan Rangaraj & Vishnu Narayanan, 2016. "Facility location models to locate data in information networks: a literature review," Annals of Operations Research, Springer, vol. 246(1), pages 313-348, November.
    • Handle: RePEc:spr:annopr:v:246:y:2016:i:1:d:10.1007_s10479-015-1840-6
    DOI: 10.1007/s10479-015-1840-6
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    References listed on IDEAS

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    1. J. F. Campbell & A. T. Ernst & M. Krishnamoorthy, 2005. "Hub Arc Location Problems: Part I---Introduction and Results," Management Science, INFORMS, vol. 51(10), pages 1540-1555, October.
    2. Pirkul, Hasan, 1986. "An integer programming model for the allocation of databases in a distributed computer system," European Journal of Operational Research, Elsevier, vol. 26(3), pages 401-411, September.
    3. Andreas Ernst & Houyuan Jiang & Mohan Krishnamoorthy, 2006. "Exact Solutions to Task Allocation Problems," Management Science, INFORMS, vol. 52(10), pages 1634-1646, October.
    4. J. F. Campbell & A. T. Ernst & M. Krishnamoorthy, 2005. "Hub Arc Location Problems: Part II---Formulations and Optimal Algorithms," Management Science, INFORMS, vol. 51(10), pages 1556-1571, October.
    5. Deb Ghosh & Ishwar Murthy & Allen Moffett, 1992. "File Allocation Problem: Comparison of Models with Worst Case and Average Communication Delays," Operations Research, INFORMS, vol. 40(6), pages 1074-1085, December.
    6. Lidong Wang, 2012. "RFID-based information technology and management in agriculture and food supply chains," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 11(3), pages 225-239.
    7. Sarathy, Rathindra & Shetty, Bala & Sen, Arun, 1997. "A constrained nonlinear 0-1 program for data allocation," European Journal of Operational Research, Elsevier, vol. 102(3), pages 626-647, November.
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