IDEAS home Printed from https://ideas.repec.org/a/sae/intdis/v17y2021i5p15501477211019912.html
   My bibliography  Save this article

User preference–based QoS-aware service function placement in IoT-Edge cloud

Author

Listed:
  • Briytone Mutichiro
  • Younghan Kim

Abstract

In the Internet of Things-Edge cloud, service provision presents a challenge to operators to satisfy user service-level agreements while meeting service-specific quality-of-service requirements. This is because of inherent limitations in the Internet of Things-Edge in terms of resource infrastructure as well as the complexity of user requirements in terms of resource management in a heterogeneous environment like edge. An efficient solution to this problem is service orchestration and placement of service functions to meet user-specific requirements. This work aims to satisfy user quality of service through optimizing the user response time and cost by factoring in the workload variation on the edge infrastructure. We formulate the service function placement at the edge problem. We employ user service request patterns in terms of user preference and service selection probability to model service placement. Our framework proposal relies on mixed-integer linear programming and heuristic solutions. The main objective is to realize a reduced user response time at minimal overall cost while satisfying the user service requirements. For this, several parameters, and factors such as capacity, latency, workload, and cost constraints, are considered. The proposed solutions are evaluated based on different metrics and the obtained results show the gap between the heuristic user preference placement algorithm and the optimal solution to be minimal.

Suggested Citation

  • Briytone Mutichiro & Younghan Kim, 2021. "User preference–based QoS-aware service function placement in IoT-Edge cloud," International Journal of Distributed Sensor Networks, , vol. 17(5), pages 15501477211, May.
    • Handle: RePEc:sae:intdis:v:17:y:2021:i:5:p:15501477211019912
    DOI: 10.1177/15501477211019912
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/15501477211019912
    Download Restriction: no
    File URL: https://libkey.io/10.1177/15501477211019912?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
    ---><---

    References listed on IDEAS

    as
    1. Kuo-Hsiung Wang & Kuo-Liang Yen, 2003. "Optimal control of an M/H k /1 queueing system with a removable server," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 57(2), pages 255-262, May.
    2. Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C Millham & Simon James Fong & Jason J Jung, 2020. "Intelligent energy optimization for advanced IoT analytics edge computing on wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 16(7), pages 15501477209, July.
    3. Shaoyong Guo & Xing Hu & Gangsong Dong & Wencui Li & Xuesong Qiu, 2019. "Mobile edge computing resource allocation: A joint Stackelberg game and matching strategy," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.
    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. Tri-Hai Nguyen & Luong Vuong Nguyen & Jason J. Jung & Israel Edem Agbehadji & Samuel Ofori Frimpong & Richard C. Millham, 2020. "Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges," Sustainability, MDPI, vol. 12(20), pages 1-24, October.

    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:sae:intdis:v:17:y:2021:i:5:p:15501477211019912. 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: SAGE Publications (email available below). General contact details of provider: .

    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.