.png){kind=icon}
返回
Data Security Aware and Effective Task Offloading Strategy in Mobile Edge Computing
.png){kind=icon}
PRE
AI
DOI：10.1007/s10723-023-09673-y.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the research and development of 5G technology, emerging markets such as Wise Information Technology of med, smart transportation and industrial Internet are gradually growing, which not only provide convenience to people's life, but also put forward increasingly urgent demand for efficient parallel and distributed technologies. Therefore, in order to meet the need of high computing amount for application diversification, this paper proposes a novel scheduling solution with data security, aiming at simultaneously optimizing the system response time and the user's energy consumption. First, we model the scheduling problem in a mobile edge computing (MEC) environment as a Markov decision process (MDP) problem, and a three-tier collaboration model considering data security in the MEC environment is constructed. Second, the system response time and the energy consumption are simultaneously optimized in this paper, with objective weights which change in real-time. At the same time, load balancing at the edge layer is considered. Third, a deep reinforcement learning (DRL)-based secure offloading (DRLSO) algorithm is given as the solution for the research problem. In experiments from multiple angles, the proposed algorithm has good performance.
Keyword:
Collaborative optimization
Data security
Deep reinforcement learning (DRL)
Mobile edge computing (MEC)
Task offloading
期刊
IF:
2.9
论文数:
762
被引数:
1.2K
机构
引用论文
.png){kind=icon}
Greening Duplication-Based Dependent-Tasks Scheduling on Heterogeneous Large-Scale Computing Platforms异构大规模计算平台上基于绿色复制的依赖任务调度
PREAI
分享收藏Adjunctive Recombinant Human Interferon Gamma-1b for Treatment-Resistant Schizophrenia in 2 Patients
OAAI
OAAI分享收藏
查看更多内容