TY - GEN
T1 - Migration-Based Service Allocation Optimization in Dynamic IoT Networks
AU - Sun, Mengyu
AU - Zhou, Zhangbing
AU - Xue, Xiao
AU - Gaaloul, Walid
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Considering the resource-hungry and capability-constraint of Internet of Things (IoT) nodes, their functionalities, which are encapsulated as containerized IoT services, are composed to satisfy user requests. IoT nodes are usually duty-cycled and energy-awareness. Therefore, IoT service allocation to respective IoT nodes should be re-calibrated on-demand through migrating certain IoT services from their hosted IoT nodes to the others, in order to satisfy the functionally diversity of requests. To solve this problem, this paper proposes a Distributed Migration-based Service Allocation (DMSA) mechanism in dynamic IoT networks, where a game-theoretic approach is adopted to achieve the Nash equilibrium of IoT service allocation optimization. Extensive experiments are conducted, and evaluation results demonstrate that our DMSA performs better than the state of art’s techniques in reducing the response latency of requests and improving the resource utilization efficiency.
AB - Considering the resource-hungry and capability-constraint of Internet of Things (IoT) nodes, their functionalities, which are encapsulated as containerized IoT services, are composed to satisfy user requests. IoT nodes are usually duty-cycled and energy-awareness. Therefore, IoT service allocation to respective IoT nodes should be re-calibrated on-demand through migrating certain IoT services from their hosted IoT nodes to the others, in order to satisfy the functionally diversity of requests. To solve this problem, this paper proposes a Distributed Migration-based Service Allocation (DMSA) mechanism in dynamic IoT networks, where a game-theoretic approach is adopted to achieve the Nash equilibrium of IoT service allocation optimization. Extensive experiments are conducted, and evaluation results demonstrate that our DMSA performs better than the state of art’s techniques in reducing the response latency of requests and improving the resource utilization efficiency.
U2 - 10.1007/978-3-030-91431-8_24
DO - 10.1007/978-3-030-91431-8_24
M3 - Conference contribution
AN - SCOPUS:85120531837
SN - 9783030914301
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 385
EP - 399
BT - Service-Oriented Computing - 19th International Conference, ICSOC 2021, Proceedings
A2 - Hacid, Hakim
A2 - Kao, Odej
A2 - Mecella, Massimo
A2 - Moha, Naouel
A2 - Paik, Hye-young
PB - Springer Science and Business Media Deutschland GmbH
T2 - 19th International Conference on Service-Oriented Computing, ICSOC 2021
Y2 - 22 November 2021 through 25 November 2021
ER -