Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning
SS Mousavi, M Schukat… - IET Intelligent Transport …, 2017 - Wiley Online Library
Recent advances in combining deep neural network architectures with reinforcement
learning (RL) techniques have shown promising potential results in solving complex control …
learning (RL) techniques have shown promising potential results in solving complex control …
A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment
Fog computing (FC) can be considered as a computing paradigm which performs Internet of
Things (IoT) applications at the edge of the network. Recently, there is a great growth of data …
Things (IoT) applications at the edge of the network. Recently, there is a great growth of data …
Predicting host CPU utilization in the cloud using evolutionary neural networks
Abstract The Infrastructure as a Service (IaaS) platform in cloud computing provides
resources as a service from a pool of compute, network, and storage resources. One of the …
resources as a service from a pool of compute, network, and storage resources. One of the …
Predicting host CPU utilization in cloud computing using recurrent neural networks
One of the major challenges facing cloud computing is to accurately predict future resource
usage for future demands. Cloud resource consumption is constantly changing, which …
usage for future demands. Cloud resource consumption is constantly changing, which …
Stress monitoring using wearable sensors: IoT techniques in medical field
FM Talaat, RM El-Balka - Neural Computing and Applications, 2023 - Springer
Abstract The concept “Internet of Things”(IoT), which facilitates communication between
linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports …
linked devices, is relatively new. It refers to the next generation of the Internet. IoT supports …
Online scheduling of dependent tasks of cloud's workflows to enhance resource utilization and reduce the makespan using multiple reinforcement learning-based …
Due to different heterogeneous cloud resources and diverse and complex applications of
the users, an optimal task scheduling, which can satisfy users and cloud service providers …
the users, an optimal task scheduling, which can satisfy users and cloud service providers …
Scalable virtual machine migration using reinforcement learning
Heuristic approaches require fixed knowledge of how resource allocation should be carried
out, and this can be limiting when managing variable cloud workloads. Solutions based on …
out, and this can be limiting when managing variable cloud workloads. Solutions based on …
A cloud resource management framework for multiple online scientific workflows using cooperative reinforcement learning agents
Cloud is a common distributed environment to share strong and available resources to
increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud …
increase the efficiency of complex and heavy calculations. In return for the cost paid by cloud …
A multitime‐steps‐ahead prediction approach for scheduling live migration in cloud data centers
One of the major challenges facing cloud computing is to accurately predict future resource
usage to provision data centers for future demands. Cloud resources are constantly in a …
usage to provision data centers for future demands. Cloud resources are constantly in a …
Toward using reinforcement learning for trigger selection in network slice mobility
Recent 5G trials have demonstrated the usefulness of the Network Slicing concept that
delivers customizable services to new and under-serviced industry sectors. However, user …
delivers customizable services to new and under-serviced industry sectors. However, user …