IDEAS home Printed from https://ideas.repec.org/a/spr/telsys/v74y2020i3d10.1007_s11235-020-00660-2.html
   My bibliography  Save this article

Data centers’ services restoration based on the decision-making of distributed agents

Author

Listed:
  • Príscila Alves Lima

    (Federal University of Pernambuco)

  • Antônio Sá Barreto Neto

    (Federal Institute of Education, Science, and Technology of Pernambuco (IFPE))

  • Paulo Maciel

    (Federal University of Pernambuco)

Abstract

The increasing number of companies that are migrating their IT infrastructure to cloud environments has been motivated many studies on distributed backup strategies to improve the availability of these companies’ systems. In this scenario, it is essential to study mechanisms to evaluate the network conditions to minimize the transmission time to improve the availability of the system. The goal of this study is to build models to evaluate the availability of services running in cloud data center infrastructure, emphasizing the impact of the variation of throughput on the data redundancy, and consequently, on the availability of the service. Based on it, this research purposes some smart models which can be deployed in each data center of a distributed arrange of data centers and help the system administrator to choose the best data center to restore the services of a faulty one. To analyze the impact of the network throughput over the service’s availability, we gathered the MTTF and MTTR metrics of data center’s components and services, generated a reliability block diagram to get the MTTF of the system as a whole, and developed a formalism to model the network component. Based on the results, we built an SPN model to represent the system and get the availability of it in many network conditions. After that, we analyze the availability of the system to discuss the impact of the network conditions over the system’s availability. After building the models and get the system’s availability in many network conditions, we can perceive the enormous impact of the network conditions over the system’s availability through a plot that exhibits the annual downtime along of a year. Using the models developed to study the system availability, we developed smart agents capable of predicting the transfer time of a bulk of data and, with it, choose the data center with the best network conditions to restore the services of a faulty one.

Suggested Citation

  • Príscila Alves Lima & Antônio Sá Barreto Neto & Paulo Maciel, 2020. "Data centers’ services restoration based on the decision-making of distributed agents," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 74(3), pages 367-378, July.
    • Handle: RePEc:spr:telsys:v:74:y:2020:i:3:d:10.1007_s11235-020-00660-2
    DOI: 10.1007/s11235-020-00660-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11235-020-00660-2
    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/s11235-020-00660-2?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.

    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:telsys:v:74:y:2020:i:3:d:10.1007_s11235-020-00660-2. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.