Offloading mechanisms based on reinforcement learning and deep learning algorithms in the fog computing environment
Fog computing has emerged as a computing paradigm for resource-restricted Internet of
things (IoT) devices to support time-sensitive and computationally intensive applications …
things (IoT) devices to support time-sensitive and computationally intensive applications …
Adaptation in edge computing: a review on design principles and research challenges
Edge computing places the computational services and resources closer to the user
proximity, to reduce latency, and ensure the quality of service and experience. Low latency …
proximity, to reduce latency, and ensure the quality of service and experience. Low latency …
Handling uncertainty in self-adaptive systems: an ontology-based reinforcement learning model
Ubiquitous and pervasive systems interact with each other and perform actions favoring the
emergence of a global desired behavior. To function well, these systems need to be self …
emergence of a global desired behavior. To function well, these systems need to be self …
Enhancing VRUs Safety Through Mobility-Aware Workload Orchestration with Trajectory Prediction using Reinforcement Learning
Vulnerable road users (VRUs) such as pedestrians, cyclists, motorcyclists, and animals are
at the highest risk in the road traffic environment since they move in the environment without …
at the highest risk in the road traffic environment since they move in the environment without …
A robust adaptive workload orchestration in pure edge computing
Pure Edge computing (PEC) aims to bring cloud applications and services to the edge of the
network to support the growing user demand for time-sensitive applications and data-driven …
network to support the growing user demand for time-sensitive applications and data-driven …
Urban Traffic Signal Control at the Edge: An Ontology-Enhanced Deep Reinforcement Learning Approach
Static or traditional rule-based urban traffic signal control approaches are inadequate to
handle ever-changing and stochastic urban traffic. Recent works in Intelligent Traffic Signal …
handle ever-changing and stochastic urban traffic. Recent works in Intelligent Traffic Signal …
Multi-Objective Deep Reinforcement Learning for Efficient Workload Orchestration in Extreme Edge Computing
Workload orchestration at the edge of the network has become increasingly challenging with
the ever-increasing penetration of resource demanding mobile, and heterogeneous devices …
the ever-increasing penetration of resource demanding mobile, and heterogeneous devices …
Online Decentralized Scheduling in Fog Computing for Smart Cities Based On Reinforcement Learning
Fog Computing is a widely adopted paradigm that allows distributing the computation in a
geographic area. This makes it possible to implement time-critical applications and opens …
geographic area. This makes it possible to implement time-critical applications and opens …
Deep reinforcement learning edge workload orchestrator for vehicular edge computing
EN Silva, FM Da Silva - 2023 IEEE 9th International …, 2023 - ieeexplore.ieee.org
Smart vehicles in Vehicular Edge Computing Environments run latency sensitive
applications, such as driver assistance, autonomous driving, accident prevention and others …
applications, such as driver assistance, autonomous driving, accident prevention and others …
Fault Tolerant Robust Adaptive Workload Orchestration in Pure Edge Computing
Abstract Pure Edge Computing (PEC) emerges as a solution to meet the increasing demand
for time-sensitive and data-driven applications by bringing cloud applications and services …
for time-sensitive and data-driven applications by bringing cloud applications and services …