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Service-Oriented Energy-Latency Tradeoff for IoT Task Partial Offloading in MEC-Enhanced Multi-RAT Networks

计算机科学 移动边缘计算 能源消耗 计算卸载 服务质量 延迟(音频) 计算机网络 分布式计算 边缘计算 高效能源利用 无线 物联网 服务器 嵌入式系统 电信 生物 电气工程 工程类 生态学
作者
Meng Qin,Nan Cheng,Zewei Jing,Tingting Yang,Wenchao Xu,Qinghai Yang,Ramesh R. Rao
出处
期刊:IEEE Internet of Things Journal [Institute of Electrical and Electronics Engineers]
卷期号:8 (3): 1896-1907 被引量:88
标识
DOI:10.1109/jiot.2020.3015970
摘要

The development of the 5G network is envisioned to offer various types of services like virtual reality/augmented reality and autonomous vehicles applications with low-latency requirements in Internet-of-Things (IoT) networks. Mobile-edge computing (MEC) has become a promising solution for enhancing the computation capacity of mobile devices at the edge of the network in a 5G wireless network. Additionally, multiple radio access technologies (multi-RATs) have been verified with the potential in lowering the transmission latency and energy consumption, while improving the Quality of Services (QoS). Benefiting from the cooperation of multi-RATs, large latency-sensitive computing service tasks (L2SC) can be offloaded by different RATs simultaneously, which has great practical significance for data partitioned oriented applications with large task sizes. In this article, to enhance the L2SC offloading services for satisfying low-latency requirements with low energy consumption, we investigate the energy-latency tradeoff problem for partial task offloading in the MEC-enhanced multi-RAT network, considering the limitation of energy and computing in capability-constrained end devices in IoT networks. Specifically, we formulated the L2SC task computation offloading problem to minimize the weighted sum of the latency cost and the energy consumption by jointly optimizing the local computing frequency, task splitting, and transmit power, while guaranteeing the stringent latency requirement and the residual energy constraint. Due to the nonsmoothness and nonconvexity of the formulated problem with high complexity, we convert the tradeoff problem into a smooth biconvex problem and propose an alternate convex search-based algorithm, which can greatly reduce the computational complexity. Numerical simulation results show the effectiveness of the proposed algorithm with various performance parameters.
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