Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing Mobile edge computingcomputation offloadingresource allocationdeep reinforcement learningTo reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (...
Mobile edge computing (MEC) is a promising approach for enabling cloud-computing capabilities at the edge of cellular networks. Nonetheless, security is becoming an increasingly important issue in MEC-based applications. In this paper, we propose a deep-learning-based model to detect security threats...
Deep Reinforcement Learning Based Task Offloading Algorithm for Mobile-edge Computing Systems Mobile-edge computing(MEC) is deemed to a promising paradigm. By deploying high-performance servers on the mobile access network side, MEC can provide auxiliary computing power for mobile devices, greatly reduci...
Tan, T., & Cao, G. (2022, May). Deep Learning on Mobile Devices Through Neural Processing Units and Edge Computing. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications (pp. 1209-1218). IEEE. Q1 该paper解决了什么问题? 在训练DNN的过程中,NPU存在处理速度快但有精度损失。原因是:N...
But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(...
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing NetworksMobile-edge computing L Huang,S Bi,YJA Zhang - 《IEEE Transactions on Mobile ...
Mobile edge computing Joint computation offloading and resource allocation Deep-Q network 1. Introduction The past decade has witnessed an explosive growth in mobile internet applications that consumed a significant amount of computational resources, e.g., online gaming, virtual/augmented reality, real-...
Forlivesi, L. Jiao, L. Qendro, and F. Kawsar, “Deepx: A software accelerator for low-power deep learning inference on mobile devices,” in 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). IEEE, 2016, pp. 1–12. Google Scholar D. Ravi,...
Deep Q Learning的基本概念扩充 如何将DQL应用在集群中,获取的信息是什么?算法与集群的接口?交互方式?得到的结果以何种方式怎样再作用在集群中的? 摘要 微服务部署策略有望在面向微服务的边缘计算平台上减少总体服务响应时间。 However,现有的工作忽略了微服务间通信频率不同的影响,服务节点负载增加导致的性能下降的问题...
VI Edge Computing for Deep Learning VI-A Edge Hardware for DL VI-A1 Mobile CPUs and GPUs VI-A2 FPGA-based Solutions VI-B Communication and Computation Modes for Edge DL VI-B1 Integral(不可缺少的) Offloading VI-B2 Partial(部分的) Offloading ...