Core Concepts
The author introduces FIRE, a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment. The approach involves importance sampling to handle server failures and optimize service migration.
Abstract
The content discusses the challenges of server failures in edge computing and proposes the FIRE framework to address them. It introduces ImRE, a Q-learning algorithm, and emphasizes the importance of handling rare events in reinforcement learning algorithms. The framework aims to reduce costs and improve resilience in edge computing systems.
Key points include:
- Introduction of FIRE framework for handling rare events like server failures.
- Importance of backups in ensuring uninterrupted application operation.
- Use of digital twin environment for training RL policies without real-world consequences.
- Implementation of importance sampling to increase sampling of rare events.
- Proposal of deep Q-learning and actor critic versions for scalability.
- Consideration of users with varying risk tolerances in the optimization process.
The content provides detailed insights into the challenges faced in edge computing due to server failures and offers a comprehensive solution through the FIRE framework.
Stats
"the average cost of an outage at a cloud data center has increased to $740000 in 2016"
"with 9 access points, there will be 360 states and 90 actions, leading to 32400 state-action combinations"