Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading

By Jinke Ren, Guanding Yu, Yunlong Cai, Yinghui He. IEEE Transactions on Wireless Communications, 2017

400 citations recorded by the source corpus.

By offloading intensive computation tasks to the edge cloud located at the cellular base stations, mobile-edge computation offloading (MECO) has been regarded as a promising means to accomplish the ambitious millisecond-scale end-to-end latency requirement of fifth-generation networks. In this paper, we investigate the latency-minimization problem in a multi-user time-division multiple access MECO system with joint communication and computation resource allocation. Three different computation models are studied, i.e., local compression, edge cloud compression, and partial compression offloading. First, closed-form expressions of optimal resource allocation and minimum system delay for both local and edge cloud compression models are derived. Then, for the partial compression offloading model, we formulate a piecewise optimization problem and prove that the optimal data segmentation strategy has a piecewise structure. Based on this result, an optimal joint communication and computation resource allocation algorithm is developed. To gain more insights, we also analyze a specific scenario where communication resource is adequate while computation resource is limited. In this special case, the closed-form solution of the piecewise optimization problem can be derived. Our proposed algorithms are finally verified by numerical results, which show that the novel partial compression offloading model can significantly reduce the end-to-end latency.

Subfields: Biomedical Engineering, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/0cdf96a88b25edb6adcd91ecb26617c535ded069

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In Biomedical Engineering

Geoffrey Y. Li

In Computer Networks and Communications

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Citations in the corpus

Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “How does one effectively manage computation, communication, and storage resources between different players in smart cities to optimize the overall system performance? To answer this question, we must devise resource management schemes that consider energy [156]–[159], latency [160], traffic handlin”
Collaborative Cloud and Edge Computing for Latency Minimization (2019, background): “Last but not least, our previous work in [23] proposed a partial computation offloading scheme between the mobile devices and the edge cloud in a multi-user time-division multiple access (TDMA) MEC system, and developed an optimal closed-form data segmentation strategy to minimize the weighted-sum d”