Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing
نویسندگان
چکیده
منابع مشابه
Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning
To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile dev...
متن کاملDeep Reinforcement Learning for Dynamic Multichannel Access
We consider the problem of dynamic multichannel access in a Wireless Sensor Network (WSN) containing N correlated channels, where the states of these channels follow a joint Markov model. A user at each time slot selects a channel to transmit a packet and receives a reward based on the success or failure of the transmission, which is dictated by the state of the selected channel. The objective ...
متن کاملDeep Learning for Secure 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. The model uses unsupervised learning to automate the detection process, and uses location info...
متن کاملUsing Active Relocation to Aid Reinforcement Learning
We propose a new framework for aiding a reinforcement learner by allowing it to relocate, or move, to a state it selects so as to decrease the number of steps it needs to take in order to develop an effective policy. The framework requires a minimal amount of human involvement or expertise and assumes a cost for each relocation. Several methods for taking advantage of the ability to relocate ar...
متن کاملMulti-Objective Deep Reinforcement Learning
We propose Deep Optimistic Linear Support Learning (DOL) to solve highdimensional multi-objective decision problems where the relative importances of the objectives are not known a priori. Using features from the high-dimensional inputs, DOL computes the convex coverage set containing all potential optimal solutions of the convex combinations of the objectives. To our knowledge, this is the fir...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Communications Standards Magazine
سال: 2019
ISSN: 2471-2825,2471-2833
DOI: 10.1109/mcomstd.2019.1900011