Cost-Effective and Intelligent Monitoring of Large-Scale Software Systems

This research project aims to tackle the challenge of ensuring the reliability of large-scale software systems, which are crucial for various applications such as online services and data processing. The project focuses on developing cost-effective and intelligent methods for monitoring these systems to anticipate and prevent runtime incidents, such as crashes or errors, which can disrupt operations and lead to costly downtimes.

Throughout the project, the goals will be achieved through a systematic approach involving data examination, statistical analysis, and machine learning techniques. By analyzing monitoring data and building predictive models, the research will successfully identify patterns indicative of potential runtime incidents, empowering proactive measures to mitigate risks and enhance system performance.

The project provides numerous benefits to all involved parties. For the student, it offers practical experience, skill development, mentorship, networking opportunities, and contributions to academia. The academic supervisors benefit from enriched collaboration, reputation enhancement, knowledge exchange, and potential publication opportunities. Additionally, the host institution gains from the student’s contributions to ongoing research efforts and potential future collaborations.

Faculty Supervisor:

Heng Li

Student:

Partner:

National Cheng Kung University

Discipline:

Computer science

Sector:

Information and Communications Technology; Artificial Intelligence

University:

Polytechnique Montréal

Program: