Graph attention networks and deep Q-learning for service mesh optimization: a digital twinning approach

Michel Gokan Khan, Javid Taheri, Andreas Kassler, Arsineh Boodaghian Asl

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dependencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

Original languageEnglish
Title of host publicationICC 2024 - IEEE International Conference on Communications: Proceedings
EditorsMatthew Valenti, David Reed, Melissa Torres
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2913-2918
Number of pages6
ISBN (Electronic)9781728190549
ISBN (Print)9781728190556
DOIs
Publication statusPublished - 20 Aug 2024
Event59th Annual IEEE International Conference on Communications, ICC 2024 - Denver, United States
Duration: 09 Jun 202413 Jun 2024

Publication series

NameIEEE International Conference on Communications
ISSN (Print)1550-3607
ISSN (Electronic)1938-1883

Conference

Conference59th Annual IEEE International Conference on Communications, ICC 2024
Country/TerritoryUnited States
CityDenver
Period09/06/202413/06/2024

Publications and Copyright Policy

This work is licensed under Queen’s Research Publications and Copyright Policy.

Keywords

  • component
  • formatting
  • insert
  • style
  • styling

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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