IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2021i1p101-d712883.html
   My bibliography  Save this article

Cognitive Load Balancing Approach for 6G MEC Serving IoT Mashups

Author

Listed:
  • Barbara Attanasio

    (Dipartimento di Ingegneria Elettrica, Elettronica ed Informatica (DIEEI), Universitá di Catania, 95125 Catania, Italy
    These authors contributed equally to this work.)

  • Andriy Mazayev

    (Centre for Electronics, Optoelectronics and Telecommunications(CEOT), Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
    These authors contributed equally to this work.)

  • Shani du Plessis

    (Centre for Electronics, Optoelectronics and Telecommunications(CEOT), Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
    These authors contributed equally to this work.)

  • Noélia Correia

    (Centre for Electronics, Optoelectronics and Telecommunications(CEOT), Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal
    These authors contributed equally to this work.)

Abstract

The sixth generation (6G) of communication networks represents more of a revolution than an evolution of the previous generations, providing new directions and innovative approaches to face the network challenges of the future. A crucial aspect is to make the best use of available resources for the support of an entirely new generation of services. From this viewpoint, the Web of Things (WoT), which enables Things to become Web Things to chain, use and re-use in IoT mashups, allows interoperability among IoT platforms. At the same time, Multi-access Edge Computing (MEC) brings computing and data storage to the edge of the network, which creates the so-called distributed and collective edge intelligence. Such intelligence is created in order to deal with the huge amount of data to be collected, analyzed and processed, from real word contexts, such as smart cities, which are evolving into dynamic and networked systems of people and things. To better exploit this architecture, it is crucial to break monolithic applications into modular microservices, which can be executed independently. Here, we propose an approach based on complex network theory and two weighted and interdependent multiplex networks to address the Microservices-compliant Load Balancing (McLB) problem in MEC infrastructure. Our findings show that the multiplex network representation represents an extra dimension of analysis, allowing to capture the complexity in WoT mashup organization and its impact on the organizational aspect of MEC servers. The impact of this extracted knowledge on the cognitive organization of MEC is quantified, through the use of heuristics that are engineered to guarantee load balancing and, consequently, QoS.

Suggested Citation

  • Barbara Attanasio & Andriy Mazayev & Shani du Plessis & Noélia Correia, 2021. "Cognitive Load Balancing Approach for 6G MEC Serving IoT Mashups," Mathematics, MDPI, vol. 10(1), pages 1-16, December.
  • Handle: RePEc:gam:jmathe:v:10:y:2021:i:1:p:101-:d:712883
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/1/101/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/1/101/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:10:y:2021:i:1:p:101-:d:712883. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.