IDEAS home Printed from https://ideas.repec.org/a/spr/infotm/vyid10.1007_s10799-016-0261-9.html
   My bibliography  Save this article

Application placement in computer clustering in software as a service (SaaS) networks

Author

Listed:
  • Ali Amiri

    (Oklahoma State University)

Abstract

One major service provided by cloud computing is Software as a Service (SaaS). As competition in the SaaS market intensifies, it becomes imperative for a SaaS provider to design and configure its computing system properly. This paper studies the application placement problem encountered in computer clustering in SaaS networks. This problem involves deciding which software applications to install on each computer cluster of the provider and how to assign customers to the clusters in order to minimize total cost. Given the complexity of the problem, we propose two algorithms to solve it. The first one is a probabilistic greedy algorithm which includes randomization and perturbation features to avoid getting trapped in a local optimum. The second algorithm is based on a reformulation of the problem where each cluster is to be assigned an application configuration from a properly generated subset of configurations. We conducted an extensive computational study using large data sets with up to 300 customers and 50 applications. The results show that both algorithms outperform a standard branch-and-bound procedure for problem instances with large sizes. The probabilistic greedy algorithm is shown to be the most efficient in solving the problem.

Suggested Citation

  • Ali Amiri, 0. "Application placement in computer clustering in software as a service (SaaS) networks," Information Technology and Management, Springer, vol. 0, pages 1-13.
    • Handle: RePEc:spr:infotm:v::y::i::d:10.1007_s10799-016-0261-9
    DOI: 10.1007/s10799-016-0261-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10799-016-0261-9
    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/s10799-016-0261-9?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. Lan, Guanghui & DePuy, Gail W. & Whitehouse, Gary E., 2007. "An effective and simple heuristic for the set covering problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1387-1403, February.
    2. Mazzola, Joseph B. & Neebe, Alan W., 1999. "Lagrangian-relaxation-based solution procedures for a multiproduct capacitated facility location problem with choice of facility type," European Journal of Operational Research, Elsevier, vol. 115(2), pages 285-299, June.
    3. M Haouari & J S Chaouachi, 2002. "A probabilistic greedy search algorithm for combinatorial optimisation with application to the set covering problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(7), pages 792-799, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ramzi El-Haddadeh, 0. "Digital Innovation Dynamics Influence on Organisational Adoption: The Case of Cloud Computing Services," Information Systems Frontiers, Springer, vol. 0, pages 1-15.
    2. Ramzi El-Haddadeh, 2020. "Digital Innovation Dynamics Influence on Organisational Adoption: The Case of Cloud Computing Services," Information Systems Frontiers, Springer, vol. 22(4), pages 985-999, August.

    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. Ali Amiri, 2017. "Application placement in computer clustering in software as a service (SaaS) networks," Information Technology and Management, Springer, vol. 18(2), pages 161-173, June.
    2. Naji-Azimi, Zahra & Toth, Paolo & Galli, Laura, 2010. "An electromagnetism metaheuristic for the unicost set covering problem," European Journal of Operational Research, Elsevier, vol. 205(2), pages 290-300, September.
    3. Li, Shengyin & Huang, Yongxi, 2014. "Heuristic approaches for the flow-based set covering problem with deviation paths," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 72(C), pages 144-158.
    4. Haouari, Mohamed & Chaouachi, Jouhaina Siala, 2006. "Upper and lower bounding strategies for the generalized minimum spanning tree problem," European Journal of Operational Research, Elsevier, vol. 171(2), pages 632-647, June.
    5. Correia, Isabel & Melo, Teresa & Saldanha-da-Gama, Francisco, 2012. "Comparing classical performance measures for a multi-period, two-echelon supply chain network design problem with sizing decisions," Technical Reports on Logistics of the Saarland Business School 1, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    6. Holzapfel, Andreas & Potoczki, Tobias & Kuhn, Heinrich, 2023. "Designing the breadth and depth of distribution networks in the retail trade," International Journal of Production Economics, Elsevier, vol. 257(C).
    7. Lan, Guanghui & DePuy, Gail W. & Whitehouse, Gary E., 2007. "An effective and simple heuristic for the set covering problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1387-1403, February.
    8. Seok, Hyesung & Nof, Shimon Y., 2014. "Dynamic coalition reformation for adaptive demand and capacity sharing," International Journal of Production Economics, Elsevier, vol. 147(PA), pages 136-146.
    9. Masoud Yaghini & Mohammad Karimi & Mohadeseh Rahbar, 2015. "A set covering approach for multi-depot train driver scheduling," Journal of Combinatorial Optimization, Springer, vol. 29(3), pages 636-654, April.
    10. Bai, Yun & Hwang, Taesung & Kang, Seungmo & Ouyang, Yanfeng, 2011. "Biofuel refinery location and supply chain planning under traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 45(1), pages 162-175, January.
    11. Abdullah Alshehri & Mahmoud Owais & Jayadev Gyani & Mishal H. Aljarbou & Saleh Alsulamy, 2023. "Residual Neural Networks for Origin–Destination Trip Matrix Estimation from Traffic Sensor Information," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    12. Siyang Xie & Xi Chen & Zhaodong Wang & Yanfeng Ouyang & Kamalesh Somani & Jing Huang, 2016. "Integrated Planning for Multiple Types of Locomotive Work Facilities Under Location, Routing, and Inventory Considerations," Interfaces, INFORMS, vol. 46(5), pages 391-408, October.
    13. Drexl, Andreas & Klose, Andreas, 2001. "Facility location models for distribution system design," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 546, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    14. Victor Reyes & Ignacio Araya, 2021. "A GRASP-based scheme for the set covering problem," Operational Research, Springer, vol. 21(4), pages 2391-2408, December.
    15. Giovanni Felici & Sokol Ndreca & Aldo Procacci & Benedetto Scoppola, 2016. "A-priori upper bounds for the set covering problem," Annals of Operations Research, Springer, vol. 238(1), pages 229-241, March.
    16. Owais, Mahmoud & Moussa, Ghada S. & Hussain, Khaled F., 2019. "Sensor location model for O/D estimation: Multi-criteria meta-heuristics approach," Operations Research Perspectives, Elsevier, vol. 6(C).
    17. Sourirajan, Karthik & Ozsen, Leyla & Uzsoy, Reha, 2009. "A genetic algorithm for a single product network design model with lead time and safety stock considerations," European Journal of Operational Research, Elsevier, vol. 197(2), pages 599-608, September.
    18. Bahman Naderi & Kannan Govindan & Hamed Soleimani, 2020. "A Benders decomposition approach for a real case supply chain network design with capacity acquisition and transporter planning: wheat distribution network," Annals of Operations Research, Springer, vol. 291(1), pages 685-705, August.
    19. Ünal, Yusuf Ziya & Uysal, Özgür, 2014. "A new mixed integer programming model for curriculum balancing: Application to a Turkish university," European Journal of Operational Research, Elsevier, vol. 238(1), pages 339-347.
    20. Huang, Huei-Chuen & Li, Rongheng, 2008. "A k-product uncapacitated facility location problem," European Journal of Operational Research, Elsevier, vol. 185(2), pages 552-562, March.

    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:infotm:v::y::i::d:10.1007_s10799-016-0261-9. 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.