Jointly Optimized Energy-minimal Resource Allocation in Cache-enhanced Mobile Edge Computing Systems

Peng Liu, Gaochao Xu, Kun Yang, Kezhi Wang, Xiangyu Meng

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)
25 Downloads (Pure)

Abstract

Mobile edge computing (MEC) has attracted extensive studies recently due to its ability to augment the computational capabilities of mobile devices. This paper considers a cache-enhanced multiuser MEC system where the task can be cached in the MEC servers to avoid the transmission of duplicate data. To further improve the energy efficiency and satisfy the users’ requirement on delay, we jointly optimize caching, computation, and communication resources in this system. The formulated problem is a mixed integer non-convex optimization problem that is very challenging to solve. We thus propose an efficient iterative algorithm by jointly applying the block coordinate descent and convex optimization techniques, which is guaranteed to converge at least a suboptimal solution. Specifically, the formulated joint optimization problem is decomposed into two subproblems to optimize caching policy and resource allocation, respectively, which are alternately optimized by convex optimization in each iteration. To further speed up the algorithm convergence, an efficient initialization scheme based on the linear weighted method is proposed for caching policy. The extensive simulation results are provided to demonstrate that the proposed jointly optimizing caching, computation, and communication method can improve the energy efficiency with lower time cost compared with other benchmark methods.
Original languageEnglish
Pages (from-to)3336-3347
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 25 Dec 2018

Fingerprint

Dive into the research topics of 'Jointly Optimized Energy-minimal Resource Allocation in Cache-enhanced Mobile Edge Computing Systems'. Together they form a unique fingerprint.

Cite this