Delay-Aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach
Beijing University of Posts and Telecommunications · Texas A&M University – Corpus Christi · +1 more institution
Abstract
As an emerging service architecture, microservice enables decomposition of a monolithic web service into a set of independent lightweight services which can be executed independently. With mobile edge computing, microservices can be further deployed in edge clouds dynamically, launched quickly, and migrated across edge clouds easily, providing better services for users in proximity. However, the user mobility can result in frequent switch of nearby edge clouds, which increases the service delay when users move away from their serving edge clouds. To address this issue, this article investigates microservice coordination among edge clouds to enable seamless and real-time responses to service requests from…
Citation impact
- FWCI
- 30.85
- Percentile
- 100%
- References
- 46
Authors
6Topics & keywords
- Computer science
- Microservices
- Cloud computing
- Reinforcement learning
- Mobile edge computing
- Enhanced Data Rates for GSM Evolution
- Distributed computing
- Edge computing
- Reduced inequalities