A framework for dynamically meeting performance objectives on a service mesh
We present a framework for achieving end-to-end management objectives for multiple
services that concurrently execute on a service mesh. We apply reinforcement learning (RL) …
services that concurrently execute on a service mesh. We apply reinforcement learning (RL) …
Comparing Transfer Learning and Rollout for Policy Adaptation in a Changing Network Environment
Dynamic resource allocation for network services is pivotal for achieving end-to-end
management objectives. Previous research has demonstrated that Reinforcement Learning …
management objectives. Previous research has demonstrated that Reinforcement Learning …
Online Policy Adaptation for Networked Systems using Rollout
Dynamic resource allocation in networked systems is needed to continuously achieve end-
to-end management objectives. Recent research has shown that reinforcement learning can …
to-end management objectives. Recent research has shown that reinforcement learning can …
Distributed and Adaptive Workload Prediction for In-Network Computing
Workload prediction is the key to improving the quality of service (QoS) of various user
applications running in autonomous large-scale networks. Machine learning (ML) …
applications running in autonomous large-scale networks. Machine learning (ML) …
Efficiently learning the system model for microservice-based applications
L Macià Coll - 2023 - upcommons.upc.edu
Microservice-based applications are increasing in popularity. They offer numerous
advantages and qualities (ie, decentralized architecture, ease of monitoring and …
advantages and qualities (ie, decentralized architecture, ease of monitoring and …