文章基本信息

标题：A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay
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作者：Li Quan ; Zhiliang Wang ; Fuji Ren 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2018
卷号：10
期号：7
页码：60
DOI：10.3390/fi10070060
语种：English
出版社：MDPI Publishing
摘要：Mobile devices could augment their ability via cloud resources in mobile cloud computing environments. This paper developed a novel two-layered reinforcement learning (TLRL) algorithm to consider task offloading for resource-constrained mobile devices. As opposed to existing literature, the utilization rate of the physical machine and the delay for offloaded tasks are taken into account simultaneously by introducing a weighted reward. The high dimensionality of the state space and action space might affect the speed of convergence. Therefore, a novel reinforcement learning algorithm with a two-layered structure is presented to address this problem. First, k clusters of the physical machines are generated based on the k-nearest neighbors algorithm (k-NN). The first layer of TLRL is implemented by a deep reinforcement learning to determine the cluster to be assigned for the offloaded tasks. On this basis, the second layer intends to further specify a physical machine for task execution. Finally, simulation examples are carried out to verify that the proposed TLRL algorithm is able to speed up the optimal policy learning and can deal with the tradeoff between physical machine utilization rate and delay.
关键词：mobile device; task offloading; tradeoff; mobile cloud computing; two layered reinforcement learning mobile device ; task offloading ; tradeoff ; mobile cloud computing ; two layered reinforcement learning