IDEAS home Printed from https://ideas.repec.org/a/spr/jcomop/v38y2019i3d10.1007_s10878-019-00411-3.html
   My bibliography  Save this article

Hybrid algorithms for placement of virtual machines across geo-separated data centers

Author

Listed:
  • Fernando Stefanello

    (Federal University of Rio Grande do Sul)

  • Vaneet Aggarwal

    (Purdue University)

  • Luciana S. Buriol

    (Federal University of Rio Grande do Sul)

  • Mauricio G. C. Resende

    (Amazon.com
    University of Washington)

Abstract

Cloud computing has emerged as a new paradigm for hosting and supplying services over the Internet. This technology has brought many benefits, such as eliminating the need for maintaining expensive computing hardware. With an increasing demand for cloud computing, providing performance guarantees for applications that run over cloud become important. Applications can be abstracted into a set of virtual machines with certain guarantees depicting the quality of service of the application. In this paper, we consider the placement of these virtual machines across multiple data centers (VMPlacement), meeting the quality of service requirements while minimizing the bandwidth cost of the data centers. This problem is a generalization of the NP-hard generalized quadratic assignment problem (GQAP). In this paper, we present a greedy randomized adaptive search procedure and a biased random-key genetic algorithm, both hybridized with a path-relinking strategy and a local search based on variable neighborhood descent for solving this problem. The hybrid heuristics are also tested on instances of the GQAP. We show that both algorithms are effective in quickly solving small and large instances of VMPlacement problem, especially when the path-relinking is used. For GQAP, the results outperform the previous state-of-the-art algorithms.

Suggested Citation

  • Fernando Stefanello & Vaneet Aggarwal & Luciana S. Buriol & Mauricio G. C. Resende, 2019. "Hybrid algorithms for placement of virtual machines across geo-separated data centers," Journal of Combinatorial Optimization, Springer, vol. 38(3), pages 748-793, October.
  • Handle: RePEc:spr:jcomop:v:38:y:2019:i:3:d:10.1007_s10878-019-00411-3
    DOI: 10.1007/s10878-019-00411-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10878-019-00411-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10878-019-00411-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. F. Stefanello & L. S. Buriol & M. J. Hirsch & P. M. Pardalos & T. Querido & M. G. C. Resende & M. Ritt, 2017. "On the minimization of traffic congestion in road networks with tolls," Annals of Operations Research, Springer, vol. 249(1), pages 119-139, February.
    2. Manuel Laguna & Rafael Marti, 1999. "GRASP and Path Relinking for 2-Layer Straight Line Crossing Minimization," INFORMS Journal on Computing, INFORMS, vol. 11(1), pages 44-52, February.
    3. Mauricio G. C. Resende & Celso C. Ribeiro, 2014. "GRASP: Greedy Randomized Adaptive Search Procedures," Springer Books, in: Edmund K. Burke & Graham Kendall (ed.), Search Methodologies, edition 2, chapter 0, pages 287-312, Springer.
    4. Vallada, Eva & Ruiz, Rubén, 2010. "Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem," Omega, Elsevier, vol. 38(1-2), pages 57-67, February.
    5. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    6. Pessoa, Artur Alves & Hahn, Peter M. & Guignard, Monique & Zhu, Yi-Rong, 2010. "Algorithms for the generalized quadratic assignment problem combining Lagrangean decomposition and the Reformulation-Linearization Technique," European Journal of Operational Research, Elsevier, vol. 206(1), pages 54-63, October.
    7. Mauricio G.C. Resende & Celso C. Ribeiro & Fred Glover & Rafael Martí, 2010. "Scatter Search and Path-Relinking: Fundamentals, Advances, and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 87-107, Springer.
    8. Marti, Rafael & Laguna, Manuel & Glover, Fred, 2006. "Principles of scatter search," European Journal of Operational Research, Elsevier, vol. 169(2), pages 359-372, March.
    9. Fred Glover, 1989. "Tabu Search---Part I," INFORMS Journal on Computing, INFORMS, vol. 1(3), pages 190-206, August.
    10. Thomas A. Feo & Mauricio G. C. Resende & Stuart H. Smith, 1994. "A Greedy Randomized Adaptive Search Procedure for Maximum Independent Set," Operations Research, INFORMS, vol. 42(5), pages 860-878, October.
    11. Pierre Hansen & Nenad Mladenović & José Moreno Pérez, 2010. "Variable neighbourhood search: methods and applications," Annals of Operations Research, Springer, vol. 175(1), pages 367-407, March.
    12. Kaufman, L. & Broeckx, F., 1978. "An algorithm for the quadratic assignment problem using Bender's decomposition," European Journal of Operational Research, Elsevier, vol. 2(3), pages 207-211, May.
    13. Fred Glover, 2014. "Exterior Path Relinking for Zero-One Optimization," International Journal of Applied Metaheuristic Computing (IJAMC), IGI Global, vol. 5(3), pages 1-8, July.
    14. Matteo Fischetti & Michele Monaci, 2014. "Exploiting Erraticism in Search," Operations Research, INFORMS, vol. 62(1), pages 114-122, February.
    15. Mauricio G.C. Resende & Celso C. Ribeiro, 2010. "Greedy Randomized Adaptive Search Procedures: Advances, Hybridizations, and Applications," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 283-319, Springer.
    16. Martí, Rafael & Resende, Mauricio G.C. & Ribeiro, Celso C., 2013. "Multi-start methods for combinatorial optimization," European Journal of Operational Research, Elsevier, vol. 226(1), pages 1-8.
    17. Jean-François Cordeau & Manlio Gaudioso & Gilbert Laporte & Luigi Moccia, 2006. "A Memetic Heuristic for the Generalized Quadratic Assignment Problem," INFORMS Journal on Computing, INFORMS, vol. 18(4), pages 433-443, November.
    18. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Drexl, Andreas & Salewski, Frank, 1996. "Distribution Requirements and Compactness Constraints in School Timetabling. Part II: Methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 384, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    2. Caio César Freitas & Dario José Aloise & Fábio Francisco Costa Fontes & Andréa Cynthia Santos & Matheus Silva Menezes, 2023. "A biased random-key genetic algorithm for the two-level hub location routing problem with directed tours," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 903-924, September.
    3. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    4. Schirmer, Andreas & Riesenberg, Sven, 1997. "Parameterized heuristics for project scheduling: Biased random sampling methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 456, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    5. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    6. F. Stefanello & L. S. Buriol & M. J. Hirsch & P. M. Pardalos & T. Querido & M. G. C. Resende & M. Ritt, 2017. "On the minimization of traffic congestion in road networks with tolls," Annals of Operations Research, Springer, vol. 249(1), pages 119-139, February.
    7. Soares, Leonardo Cabral R. & Carvalho, Marco Antonio M., 2020. "Biased random-key genetic algorithm for scheduling identical parallel machines with tooling constraints," European Journal of Operational Research, Elsevier, vol. 285(3), pages 955-964.
    8. Ghorashi Khalilabadi, S. M. & Roy, D. & de Koster, M.B.M., 2022. "A Data-driven Approach to Enhance Worker Productivity by Optimizing Facility Layout," ERIM Report Series Research in Management ERS-2022-003-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    9. Gonçalves, José Fernando & Wäscher, Gerhard, 2020. "A MIP model and a biased random-key genetic algorithm based approach for a two-dimensional cutting problem with defects," European Journal of Operational Research, Elsevier, vol. 286(3), pages 867-882.
    10. Salewski, Frank & Bartsch, Thomas, 1994. "A comparison of genetic and greedy randomized algorithms for medium-to-short-term audit-staff scheduling," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 356, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    11. Andreas Schirmer, 2000. "Case‐based reasoning and improved adaptive search for project scheduling," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(3), pages 201-222, April.
    12. A. D. López-Sánchez & J. Sánchez-Oro & M. Laguna, 2021. "A New Scatter Search Design for Multiobjective Combinatorial Optimization with an Application to Facility Location," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 629-642, May.
    13. Geiza Silva & André Leite & Raydonal Ospina & Víctor Leiva & Jorge Figueroa-Zúñiga & Cecilia Castro, 2023. "Biased Random-Key Genetic Algorithm with Local Search Applied to the Maximum Diversity Problem," Mathematics, MDPI, vol. 11(14), pages 1-11, July.
    14. Pinto, Bruno Q. & Ribeiro, Celso C. & Rosseti, Isabel & Plastino, Alexandre, 2018. "A biased random-key genetic algorithm for the maximum quasi-clique problem," European Journal of Operational Research, Elsevier, vol. 271(3), pages 849-865.
    15. Edson Ticona-Zegarra & Rafael CS Schouery & Leandro A Villas & Flávio K Miyazawa, 2018. "Improved continuous enhancement routing solution for energy-aware data aggregation in wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(5), pages 15501477187, May.
    16. Li, Xueping & Zhang, Kaike, 2018. "Single batch processing machine scheduling with two-dimensional bin packing constraints," International Journal of Production Economics, Elsevier, vol. 196(C), pages 113-121.
    17. Bruno Q. Pinto & Celso C. Ribeiro & Isabel Rosseti & Thiago F. Noronha, 2020. "A biased random-key genetic algorithm for routing and wavelength assignment under a sliding scheduled traffic model," Journal of Global Optimization, Springer, vol. 77(4), pages 949-973, August.
    18. Paola Festa & Panos Pardalos, 2012. "Efficient solutions for the far from most string problem," Annals of Operations Research, Springer, vol. 196(1), pages 663-682, July.
    19. Anurag Agarwal, 2009. "Theoretical insights into the augmented-neural-network approach for combinatorial optimization," Annals of Operations Research, Springer, vol. 168(1), pages 101-117, April.
    20. Ayşegül Altın & Bernard Fortz & Mikkel Thorup & Hakan Ümit, 2013. "Intra-domain traffic engineering with shortest path routing protocols," Annals of Operations Research, Springer, vol. 204(1), pages 65-95, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jcomop:v:38:y:2019:i:3:d:10.1007_s10878-019-00411-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.