TY - JOUR
T1 - Optimal Edge Resource Allocation in IoT-Based Smart Cities
AU - Zhao, Lei
AU - Wang, Jiadai
AU - Liu, Jiajia
AU - Kato, Nei
N1 - Funding Information:
Acknowledgment This work was supported by the National Natural Science Foundation of China (61771374, 61771373, and 61601357), in part by China 111 Project (B16037), and in part by the Fundamental Research Fund for the Central Universities (JB171501, JB181506, JB181507, and JB181508).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - With IoT-based smart cities, massive heterogeneous IoT devices are running diverse advanced services for unprecedented intelligence and efficiency in various domains of city life. Given the exponentially growing number of IoT devices and the large number of smart city services as well as their different QoS requirements, it has been a big challenge for servers to optimally allocate limited resources to all hosted applications for satisfactory performance. Note that by pushing the computing and storage resources to the proximity of end IoT devices, and deploying applications in distributed edge servers, edge computing technology appears to be a promising solution for this challenge. Toward this, we study in this article how to allocate edge resources for average service response time minimization. Besides the proposed algorithms, extensive numerical results are also presented to validate their efficacy.
AB - With IoT-based smart cities, massive heterogeneous IoT devices are running diverse advanced services for unprecedented intelligence and efficiency in various domains of city life. Given the exponentially growing number of IoT devices and the large number of smart city services as well as their different QoS requirements, it has been a big challenge for servers to optimally allocate limited resources to all hosted applications for satisfactory performance. Note that by pushing the computing and storage resources to the proximity of end IoT devices, and deploying applications in distributed edge servers, edge computing technology appears to be a promising solution for this challenge. Toward this, we study in this article how to allocate edge resources for average service response time minimization. Besides the proposed algorithms, extensive numerical results are also presented to validate their efficacy.
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U2 - 10.1109/MNET.2019.1800221
DO - 10.1109/MNET.2019.1800221
M3 - Article
AN - SCOPUS:85063802805
VL - 33
SP - 30
EP - 35
JO - IEEE Network
JF - IEEE Network
SN - 0890-8044
IS - 2
M1 - 8675169
ER -
