Skip to main navigation Skip to search Skip to main content

Programming framework and infrastructure for self-adaptation and optimized evolution method for microservice systems in cloud–edge environments

Research output: Contribution to journalArticlepeer-review

Abstract

Edge computing technologies facilitate the deployment of services on nearby edge servers with a large number of end users and their mobile devices to fulfill personalized demands. Owing to frequent changes in user mobility and demands, service systems deployed in an edge–cloud environment must continuously adapt to ensure that the quality of service (QoS) perceived by the end users is maintained at a stable and satisfactory level. As it is difficult for system operation engineers to manually deal with such frequent and large-scale evolution due to problems of cost and efficiency, self-adaptation of the system is essential. In this paper, we present a programming framework for microservices (EPF4M) and an infrastructure for self-adaptive microservice systems (EI4MS) for the cloud–edge environment based on microservice architecture. Our study follows a “monitoring–analyzing–planning–execution” control loop that empowers the service systems to redeploy the services according to changes in the QoS. A two-phase strategy is adopted to minimize the side effects of the loop on the performance of the service system. A prototype of this framework and infrastructure has been open-sourced and verified through experiments conducted in a real cloud–edge environment. The results demonstrate the usefulness and advantages of our approach.

Original languageEnglish
Pages (from-to)263-281
Number of pages19
JournalFuture Generation Computer Systems
Volume118
DOIs
StatePublished - May 2021
Externally publishedYes

Keywords

  • Cloud–edge environment
  • DevOps
  • Infrastructure
  • Microservice systems
  • Quality of Services (QoS)
  • Self-adaptation

Fingerprint

Dive into the research topics of 'Programming framework and infrastructure for self-adaptation and optimized evolution method for microservice systems in cloud–edge environments'. Together they form a unique fingerprint.

Cite this