Mattia Zaccarini

TL;DR: This paper makes a comparison of different optimization algorithms and a first investigation of how they can perform in this kind of scenario and demonstrates how all approaches can solve the service management problem with similar performance—with a different sample efficiency—if a high number of samples can be evaluated for training and optimization.

TL;DR: This paper introduces a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications, and shows the improvement in performance with respect to simulation-based inference procedures proposed in literature.

TL;DR: KubeTwin is a Digital Twin framework for optimizing Kubernetes deployments at scale, particularly in multi-cloud and edge computing environments, enabling accurate virtual representations to optimize deployment and management policies.

TL;DR: In this article , the authors proposed an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network and introduced refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios.