학술논문

A Cache-assisted Computing Offloading Strategy Based on Deep Q Network
Document Type
Conference
Source
2023 7th International Conference on Management Engineering, Software Engineering and Service Sciences (ICMSS) Management Engineering, Software Engineering and Service Sciences (ICMSS), 2023 7th International Conference on. :80-85 Jan, 2023
Subject
Computing and Processing
General Topics for Engineers
Multi-access edge computing
Cooperative caching
Simulation
Collaboration
Smart homes
Servers
Task analysis
mobile edge computing
caching
collaborative offloading
deep reinforcement learning
computing offloading
Language
Abstract
Mobile edge computing (MEC) provides users with abundant wireless resources and cloud computing capabilities to meet their computing demand. Existing works tend to consider caching and computing offloading separately, so it is difficult to achieve overall optimization of system performance. To further improve system performance in smart home scenario, a novel collaborative caching and computing offloading scheme (CCCO) was proposed in this paper. First, a new collaborative caching strategy is designed in this paper to improve the cache hit rate, i.e., smart devices cache the task's computation results and edge servers collaboratively cache the related data of the sub-tasks after task division, Then, sub-tasks are collaboratively offloaded to servers for processing. Finally, Deep Q Network algorithm is used to obtain the optimal offloading and caching decisions for minimizing system latency. Simulation results show that the proposed algorithm significantly outperforms the traditional computing offloading scheme in terms of latency.