TY - JOUR
T1 - Budget-Constrained Optimal Deployment of Redundant Services in Edge Computing Environment
AU - Wang, Pengwei
AU - Xu, Jin
AU - Zhou, Mengchu
AU - Albeshri, Aiiad
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61602109; the DHU Distinguished Young Professor Program under Grant LZB2019003; the Shanghai Science and Technology Innovation Action Plan under Grant 19511101802; the Ministry of Education and Deanship of Scientific Research (DSR), King Abdulaziz University (KAU), Jeddah, Saudi Arabia via Institutional Fund Projects under Grant IFPIP: 1476-611-1443; and the Fundamental Research Funds for the Central Universities.
Publisher Copyright:
© 2014 IEEE.
PY - 2023/6/1
Y1 - 2023/6/1
N2 - With the development of multiaccess edge computing (also called mobile-edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired Quality of Service (QoS). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to limited resources, heterogeneous servers, and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies of the deployment problem in edge environment consider such property. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work first builds a redundant service deployment model under a heterogeneous edge environment and defines it as a multiobjective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and the K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.
AB - With the development of multiaccess edge computing (also called mobile-edge computing, MEC), more and more service-based applications are deployed to edge servers in order to ensure desired Quality of Service (QoS). In edge environment, how to reasonably deploy application services emerges as a challenging problem due to limited resources, heterogeneous servers, and different geographical locations of users. Benefiting from its reusability, a single service can be used by multiple applications. Yet only a few studies of the deployment problem in edge environment consider such property. This work considers the redundant deployment of reused services by different applications, so as to achieve high QoS. Due to the importance of cost for providers, it aims to minimize transmission cost and network latency under the constraint of deployment budget. This work first builds a redundant service deployment model under a heterogeneous edge environment and defines it as a multiobjective optimization problem under a given budget constraint. Then, service priority is calculated to determine redundancy, and the K-medoids clustering algorithm based on request frequency filtering is used to conduct edge server selection. It next proposes a genetic algorithm based on priority to obtain an optimized plan. Finally, this work conducts experiments on real-world datasets to prove the superiority of the proposed method over existing ones.
KW - Budget constraint
KW - multiaccess edge computing (MEC)
KW - service deployment
KW - service-based application
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U2 - 10.1109/JIOT.2023.3234966
DO - 10.1109/JIOT.2023.3234966
M3 - Article
AN - SCOPUS:85147265439
SN - 2327-4662
VL - 10
SP - 9453
EP - 9464
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 11
ER -