Skip to main content

Serving Online Requests with Mobile Servers

  • Conference paper
  • First Online:
Algorithms and Computation (ISAAC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9472))

Included in the following conference series:

Abstract

We study an online problem in which mobile servers have to be moved in order to efficiently serve at set of online requests. More formally, there is a set of n nodes and a set of k mobile servers that are placed at some of the nodes. Each node can potentially host several servers and the servers can be moved between the nodes. There are requests \(1,2,\ldots \) that are adversarially issued at nodes one at a time, where a request issued at time t needs to be served at all times \(t' \ge t\). The cost for serving the requests is a function of the number of servers and requests at the different nodes. The requirements on how to serve the requests are governed by two parameters \(\alpha \ge 1\) and \(\beta \ge 0\). An algorithm needs to guarantee that at all times, the total service cost remains within a multiplicative factor \(\alpha \) and an additive term \(\beta \) of the current optimal service cost.

We consider online algorithms for two different minimization objectives. We first consider the natural problem of minimizing the total number of server movements. We show that in this case for every k, the competitive ratio of every deterministic online algorithm needs to be at least \(\varOmega (n)\). Given this negative result, we then extend the minimization objective to also include the current service cost. We give almost tight bounds on the competitive ratio of the online problem where one needs to minimize the sum of the total number of movements and the current service cost. In particular, we show that at the cost of an additional additive term which is roughly linear in k, it is possible to achieve a multiplicative competitive ratio of \(1+\varepsilon \) for every constant \(\varepsilon >0\).

A full version of this paper is available at http://arxiv.org/abs/1404.5510 [12].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The most basic cost function would incur a service cost of x whenever x requests are at a node with no server and a service cost of 0 for all requests at nodes with at least one server.

References

  1. Ahmadian, S., Friggstad, Z., Swamy, C.: Local-search based approximation algorithms for mobile facility location problems. In: Proceedings of the 24th Symposium on Discrete Algorithms (SODA), pp. 1607–1621 (2013)

    Google Scholar 

  2. Arora, S., Hazan, E., Kale, S.: The multiplicative weights update method: a meta-algorithm and applications. Theory Comput. 8(1), 121–164 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  3. Arya, V., Garg, N., Khandekar, R., Meyerson, A., Munagala, K., Pandit, V.: Local search heuristics for k-median and facility location problems. J. Comput. 33(3), 544–562 (2004)

    MathSciNet  MATH  Google Scholar 

  4. Bansal, N., Buchbinder, N., Madry, A., Naor, J.S.: A polylogarithmic-competitive algorithm for the k-server problem. In: Proceedings of the 52nd Symposium on Foundations of Computer Science (FOCS), pp. 267–276 (2011)

    Google Scholar 

  5. Byrka, J., Aardal, K.: An optimal bifactor approximation algorithm for the metric uncapacitated facility location problem. J. Comput. 39(6), 2212–2231 (2010)

    MathSciNet  MATH  Google Scholar 

  6. Charikar, M., Chekuri, C., Feder, T., Motwani, R.: Incremental clustering and dynamic information retrieval. In: Proceedings of the 29th Symposium on Theory of Computing (STOC), pp. 626–635 (1997)

    Google Scholar 

  7. Demaine, E.D., Hajiaghayi, M., Mahini, H., Sayedi-Roshkhar, A.S., Oveisgharan, S., Zadimoghaddam, M.: Minimizing movement. Tran. Algorithms (TALG) 5(3), 30 (2009)

    MathSciNet  MATH  Google Scholar 

  8. Drezner, Z., Hamacher, H.W.: Facility Location: Applications and Theory. Springer Science & Business Media, Heidelberg (2004)

    MATH  Google Scholar 

  9. Fakcharoenphol, J., Rao, S., Talwar, K.: A tight bound on approximating arbitrary metrics by tree metrics. In: Proceedings of the 35th Symposium on Theory of Computing (STOC), pp. 448–455 (2003)

    Google Scholar 

  10. Fotakis, D.: Online and incremental algorithms for facility location. SIGACT News 42(1), 97–131 (2011)

    Article  Google Scholar 

  11. Friggstad, Z., Salavatipour, M.R.: Minimizing movement in mobile facility location problems. Trans. Algorithms (TALG) 7(3), 28 (2011)

    MathSciNet  MATH  Google Scholar 

  12. Ghodselahi, A., Kuhn, F.: Serving online demands with movable centers. arXiv preprint arXiv:1404.5510 (2014)

  13. Guha, S., Khuller, S.: Greedy strikes back: improved facility location algorithms. In: Proceedings of the 9th Symposium on Discrete Algorithms (SODA), pp. 649–657 (1998)

    Google Scholar 

  14. Hajiaghayi, M.T., Mahdian, M., Mirrokni, V.S.: The facility location problem with general cost functions. Networks 42(1), 42–47 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  15. Jain, K., Mahdian, M., Markakis, E., Saberi, A., Vazirani, V.V.: Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP. J. ACM 50(6), 795–824 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  16. Koutsoupias, E., Papadimitriou, C.H.: On the k-server conjecture. J. ACM 42(5), 971–983 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  17. Littlestone, N., Warmuth, M.K.: The weighted majority algorithm. Inf. Comput. 108(2), 212–261 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  18. Manasse, M.S., McGeoch, L.A., Sleator, D.D.: Competitive algorithms for server problems. J. Algorithms 11(2), 208–230 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  19. Meyerson, A.: Online facility location. In: Proceedings of the 42nd Symposium on Foundations of Computer Science (FOCS), p. 426 (2001)

    Google Scholar 

  20. Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends Mach. Learn. 4(2), 107–194 (2011)

    Article  MATH  Google Scholar 

  21. Sleator, D.D., Tarjan, R.E.: Amortized efficiency of list update and paging rules. Commun. ACM 28(2), 202–208 (1985)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abdolhamid Ghodselahi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ghodselahi, A., Kuhn, F. (2015). Serving Online Requests with Mobile Servers. In: Elbassioni, K., Makino, K. (eds) Algorithms and Computation. ISAAC 2015. Lecture Notes in Computer Science(), vol 9472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48971-0_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-48971-0_62

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48970-3

  • Online ISBN: 978-3-662-48971-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics