Intelligent optimization of distributed systems: performance analysis with Lithops and machine learning

Entity: Universitat Rovira i Virgili (URV)

Confidenciality: No

Education area(s): Enginyeria de la Seguretat Informàtica i Intel·ligència Artificial

APS: No

Title in different languages: Intelligent optimization of distributed systems: performance analysis with Lithops and machine learning

Abstract: This thesis presents a solution for optimizing and monitoring distributed systems using Lithops in serverless environments. A lightweight profiler integrated with Prometheus was developed to collect real-time metrics and manage resources efficiently. The profiler tracks CPU, memory, disk, and network usage, ensuring scalability and resilience across multicloud platforms. Additionally, a machine learning model predicts the optimal task parallelization to minimize execution time. The proposed solution was validated through extensive testing in various environments, demonstrating its effectiveness in improving serverless computing performance by offering a robust tool for enhancing operational efficiency and resource optimization.

Subject: Aprenentatge automàtic

Academic year: 2023-2024

Language: en

Work's public defense date: 2024-09-12

Subject areas: Computer engineering

Student: Benabdelkrim Zakan, Usama

Department: Enginyeria Informàtica i Matemàtiques

Creation date in repository: 2025-10-23

Keywords: Distributed systems, Machine learning, Monitoring

Title in original language: Intelligent optimization of distributed systems: performance analysis with Lithops and machine learning

Access Rights: info:eu-repo/semantics/openAccess

Project director: García López, Pedro Antonio