Titulo Estágio
Data Analysis for Detecting Patterns and Understanding Characteristics of Failure Modes in Linux OS
Local do Estágio
CISUC-SSE
Enquadramento
Linux operating systems (OS) have become integral to a wide range of computing environments, from personal computers and mobile devices to servers and supercomputers. However, like all complex software, they occasioanlly fail. These failures can manifest in various forms, from application crashes and system hangs to kernel panics and hardware malfunctions. In recent years, various tools and methodologies have been developed to predict system failures. Despite these advancements, a significant gap remains in our understanding of the underlying causes of these failures. Accurate predictors may indicate that a failure is imminent, but they often fall short in explaining the complex interactions and root causes that lead to these failures. Data analysis plays a role in discovering hidden patterns and insights within large datasets. Understanding the characteristics and symptoms of different failure modes in Linux OS is not only academically significant but also practically valuable. This thesis proposes a comprehensive study aimed at conducting data analysis on a large failure dataset for the Linux OS to detect patterns and gain knowledge on the characteristics and symptoms of different failure modes.
Objetivo
The learning objectives of this master internship are:
1) Dependability, fault tolerance, fault injection: study the subject of fault tolerance, focusing on fault injection, as means to improve the dependability of modern systems
2) Online Failure Prediction: understand the problem of OFP and how it can be used to predict and mitigate incoming failures
3) Data Analysis: study existing data analysis concepts and approaches, with a focus on multivariate timeseries data;
4) Research Design: understand how to design and execute an experimental process to address complex and open research issues
Plano de Trabalhos - Semestre 1
[09/09/2024 a 20/10/2024] Literature review
Study the concepts to be used in the internship, namely Online Failure Prediction, fault injection, timeseries multivariate data, and ML algorithms
[21/10/2024 a 05/11/2024] Analysis and selection of target techniques
Identification, analysis, and selection of which data analysis and machine learning techniques will be used
[06/11/2024 a 03/12/2024] Definition of the experimental process
Design and plan the experimental process that will be used to conduct the study
[04/12/2024 a 15/01/2025] Write the dissertation plan
Plano de Trabalhos - Semestre 2
[06/02/2025 a 6/03/2025] Set up the experimental testbed
Set up the testbed required to conduct the experiments
[7/03/2025 a 08/05/2025] Conduct the experiments and analyze the results
Use the testbed to conduct the experimental process. Process, explore and analyze the failure data using various data analysis techniques
[09/05/2025 a 05/06/2025] Write a scientific paper
[06/06/2025 a 08/07/2025] Write the thesis.
Condições
Depending on the evolution of the internship a studentship may be available to support the development of the work in the second semester. The work is to be executed at the laboratories of the CISUC’s Software and Systems Engineering Group.
Orientador
João R. Campos
jrcampos@dei.uc.pt 📩