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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
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