Improving DDPG-Based Reinforcement Learning for Efficient Microservice Deployment in Edge Environments

Jenn-Wei Lin; Po-Hsien Kuo

doi:10.1145/3700523.3700556

ScienceGate Book Chapters

JOURNAL ARTICLE

Improving DDPG-Based Reinforcement Learning for Efficient Microservice Deployment in Edge Environments

Jenn-Wei Lin Po-Hsien Kuo

Year: 2024 Pages: 181-187

DOI: 10.1145/3700523.3700556

Get Full-Text PDF Get Analytical Report

Keywords:

Software deployment Computer science Reinforcement learning Enhanced Data Rates for GSM Evolution Human–computer interaction Distributed computing Software engineering Artificial intelligence

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.27

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

IoT and Edge/Fog Computing

Physical Sciences → Computer Science → Computer Networks and Communications

Cloud Computing and Resource Management

Physical Sciences → Computer Science → Information Systems

Software System Performance and Reliability

Physical Sciences → Computer Science → Computer Networks and Communications

Improving DDPG-Based Reinforcement Learning for Efficient Microservice Deployment in Edge Environments

Metrics

Topics

Related Documents

Graph-Reinforcement-Learning-Based Dependency-Aware Microservice Deployment in Edge Computing

Delay-Aware Joint Microservice Deployment and Request Routing in Multi-Edge Environments Based on Reinforcement Learning

Efficient Microservice Deployment in the Edge-Cloud Networks With Policy-Gradient Reinforcement Learning

Efficient Microservice Deployment in Kubernetes Multi-Clusters through Reinforcement Learning

IoT Microservice Deployment in Edge-Cloud Hybrid Environment Using Reinforcement Learning