TY - GEN
T1 - Service Provisioning for IoT Applications with Multiple Sources in Mobile Edge Computing
AU - Li, Jing
AU - Liang, Weifa
AU - Xu, Zichuan
AU - Zhou, Wanlei
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - We are embracing an era of Internet of Things (IoTs). However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices seriously impacts the quality of service of user experienced. To address these shortcomings, the Mobile Edge Computing (MEC) platform provides a promising solution for the service provisioning of IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices, and the service response latency can be significantly reduced. Meanwhile, each IoT application usually imposes a service function chain enforcement for its data transmission, which consists of different service functions in a specified order, and each data packet transfer in the network from the gateways of IoT devices to the destination must pass through each of the service functions in order.In this paper, we study IoT-driven service provisioning in an MEC network for various IoT applications with service function chain requirements, where an IoT application consists of multiple data streams from different IoT sources that will be uploaded to the MEC network for aggregation, processing, and storage. We first formulate a novel cost minimization problem for IoT-driven service provisioning in MEC networks. We then show that the problem is NP-hard, and propose an IoT-driven service provisioning framework for IoT applications, which consists of streaming data uploading from multiple IoT sources to the MEC network, data stream aggregation and routing, and Virtual Network Function (VNF) instance placement and sharing in cloudlets in the MEC network. In addition, we devise an efficient algorithm for the problem, built upon the proposed service framework. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising, compared with the lower bound on the optimal solution of the problem and another comparison heuristic.
AB - We are embracing an era of Internet of Things (IoTs). However, the latency brought by unstable wireless networks and computation failures caused by limited resources on IoT devices seriously impacts the quality of service of user experienced. To address these shortcomings, the Mobile Edge Computing (MEC) platform provides a promising solution for the service provisioning of IoT applications, where edge-clouds (cloudlets) are co-located with wireless access points in the proximity of IoT devices, and the service response latency can be significantly reduced. Meanwhile, each IoT application usually imposes a service function chain enforcement for its data transmission, which consists of different service functions in a specified order, and each data packet transfer in the network from the gateways of IoT devices to the destination must pass through each of the service functions in order.In this paper, we study IoT-driven service provisioning in an MEC network for various IoT applications with service function chain requirements, where an IoT application consists of multiple data streams from different IoT sources that will be uploaded to the MEC network for aggregation, processing, and storage. We first formulate a novel cost minimization problem for IoT-driven service provisioning in MEC networks. We then show that the problem is NP-hard, and propose an IoT-driven service provisioning framework for IoT applications, which consists of streaming data uploading from multiple IoT sources to the MEC network, data stream aggregation and routing, and Virtual Network Function (VNF) instance placement and sharing in cloudlets in the MEC network. In addition, we devise an efficient algorithm for the problem, built upon the proposed service framework. We finally evaluate the performance of the proposed algorithm through experimental simulations. Experimental results demonstrate that the proposed algorithm is promising, compared with the lower bound on the optimal solution of the problem and another comparison heuristic.
UR - http://www.scopus.com/inward/record.url?scp=85099886470&partnerID=8YFLogxK
U2 - 10.1109/LCN48667.2020.9314795
DO - 10.1109/LCN48667.2020.9314795
M3 - Conference contribution
AN - SCOPUS:85099886470
T3 - Proceedings - Conference on Local Computer Networks, LCN
SP - 42
EP - 53
BT - Proceedings of the IEEE 45th Conference on Local Computer Networks, LCN 2020
A2 - Tan, Hwee-Pink
A2 - Khoukhi, Lyes
A2 - Oteafy, Sharief
PB - IEEE Computer Society
T2 - 45th IEEE Conference on Local Computer Networks, LCN 2020
Y2 - 16 November 2020 through 19 November 2020
ER -