mlsanityData to 14 Oct 2026

Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level

arXiv 2110.13621 ↗First posted 21 Oct 2021cs.DC cs.AI cs.LGcited by 5

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Abstract (arXiv)

Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment. Associated technologies such as service mesh provide application-level fault resilience through attribute configurations that govern the behavior of request-response service -- and the interactions among them -- in the presence of failures. While this provides tremendous flexibility, the configured values of these attributes -- and the relationships among them -- can significantly affect the performance and fault resilience of the overall application. Furthermore, it is impossible to determine the best and worst combinations of attribute values with respect to fault resiliency via testing, due to the complexities of the underlying distributed system and the many possible attribute value combinations. In this paper, we present a model-based reinforcement learning workflow towards service mesh fault resiliency. Our approach enables the prediction of the most significant fault resilience behaviors at a web application-level, scratching from single service to aggregated multi-service management with efficient agent collaborations.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 20 of its 51 references that are in the map. First posted on arXiv 21 Oct 2021.

Builds on

20 of 51 references.

References · 20

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2019Model-Based Reinforcement Learning for Ataricited by 502
  4. 2017Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuningcited by 297
  5. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 194
  6. 2018Learning Attentional Communication for Multi-Agent Cooperationcited by 175
  7. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  8. 2018Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guaranteescited by 140
  9. 2017Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learningcited by 117
  10. 2019Learning to Schedule Communication in Multi-agent Reinforcement Learningcited by 93
  11. 2020On the role of planning in model-based deep reinforcement learningcited by 57
  12. 2019Simplified Action Decoder for Deep Multi-Agent Reinforcement Learningcited by 48
8 more
  1. 2019Biases for Emergent Communication in Multi-agent Reinforcement Learningcited by 48
  2. 2019No Press Diplomacy: Modeling Multi-Agent Gameplaycited by 38
  3. 2017Lenient Multi-Agent Deep Reinforcement Learningcited by 35
  4. 2019Decentralized Multi-Agent Reinforcement Learning with Networked Agents: Recent Advancescited by 23
  5. 2017ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learningcited by 8
  6. 2017Federated Control with Hierarchical Multi-Agent Deep Reinforcement Learningcited by 8
  7. 2019Collaboration of AI Agents via Cooperative Multi-Agent Deep Reinforcement Learningcited by 2
  8. 2021Self-Adaptive Microservice-based Systems -- Landscape and Research Opportunitiescited by 2

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Citing papers per year: 2023: 4; 2024: 1.

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