首页|QoS-Aware Augmented Reality Task Offloading and Resource Allocation in Cloud-Edge Collaboration Environment
QoS-Aware Augmented Reality Task Offloading and Resource Allocation in Cloud-Edge Collaboration Environment
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NETL
NSTL
Springer Nature
The integration of Augmented Reality (AR) into mobile devices has sparked a trend in the development of mobile AR applications across diverse sectors. Nevertheless, the execution of AR tasks necessitates substantial computational, memory, and stor-age resources, which poses a challenge for mobile terminals with limited hardware capabilities to run AR applications within a constrained time. To address this issue, we introduce a mobile AR offloading approach in the cloud-edge collaboration envi-ronment. Initially, we break down the AR task into a series of subtasks and gather characteristics related to hardware, software, configuration, and runtime environ-ments from the edge servers designated for offloading. Utilizing these characteris-tics, we build an AR Subtask Execution Delay Prediction Bayesian Network (EPBN) to forecast the execution delays of various subtasks on different edge platforms. Fol-lowing the predictions, we frame the task offloading as an NP-hard Traveling Sales-man Problem (TSP) and propose a solution based on Particle Swarm Optimization (PSO) heuristic algorithm to encode the offloading strategy. Comprehensive experi-ments have demonstrated that the prediction performance of the EPBN surpasses the other baselines, and PSO approach can reduce offloading latency effectively.
Key Laboratory of Education Informatization for Nationalities, Ministry of Education, Yunnan Normal University, Kunming 650500, China||Yunnan Key Laboratory of Smart Education, Yunnan Normal University, Kunming 650500, China
NETL
NSTL
Springer Nature
