A Novel Two-Layered Reinforcement Learning for Task Offloading with Tradeoff between Physical Machine Utilization Rate and Delay

Li Quan, Zhiliang Wang and Fuji Ren
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Li Quan: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Zhiliang Wang: School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Fuji Ren: School of Computer and Information, Hefei University of Technology, Hefei 230000, China

Future Internet, 2018, vol. 10, issue 7, 1-17

Abstract: 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.

Keywords: mobile device; task offloading; tradeoff; mobile cloud computing; two layered reinforcement learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
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