Key Skills

Job Description

When should a system resolve an incident on its own and when is human intervention the safer choice? In this master’s thesis, you will investigate how risk-aware decisions can enable reliable self-healing across hybrid cloud environments. Organizations increasingly operate applications and infrastructure across both public and private cloud environments. While this offers flexibility and scalability, it also introduces operational complexity. These platforms offer different monitoring capabilities, management interfaces and recovery options. A significant portion of operational incidents are repetitive and follow known resolution patterns. Examples include storage capacity issues, failed background jobs, stopped services, expired certificates and unresponsive processes. Although these incidents are often straightforward to resolve, they still require manual intervention by operations engineers. This increases operational costs, extends recovery times and can result in unnecessary disruptions outside business hours. Automating incident recovery appears to be a logical solution. However, autonomous intervention also introduces risks. An automated action may address a symptom rather than the root cause, conceal structural problems, or introduce changes that conflict with an organization’s configuration management practices. Research in autonomic computing, Site Reliability Engineering (SRE), and AIOps has demonstrated the potential of self-healing systems. However, most existing research focuses on relatively homogeneous environments. Less attention has been paid to heterogeneous hybrid cloud environments where systems provide different levels of control and automation capabilities. In addition, limited research exists on determining when autonomous recovery is appropriate and how risks should influence that decision. This highlights the need for a framework that can support risk-aware decisions about automated incident remediation across heterogeneous public and private cloud environments. The Assignment The goal of this research is to design and evaluate a risk-aware decision model for self-healing in hybrid cloud environments. The research should investigate which factors determine whether an incident is suitable for automated recovery, how to weigh these factors, and what level of autonomy is appropriate. A central challenge is deciding when automated recovery is beneficial and when human intervention remains necessary. The model should balance operational efficiency, reliability, safety and maintainability across heterogeneous public and private cloud environments. A limited prototype or simulation may be used to evaluate the model, but building a complete self-healing system is not the focus of the research. The outcome should provide both practical guidance for applying risk-aware self-healing and a scientifically evaluated decision model that contributes to research on autonomous cloud operations. About Info Support Info Support specializes in custom software, data/AI solutions, management, and training and is active in the Finance, Industry, Agriculture, Food & Retail, Mobility & Public, and Healthcare sectors. We provide solid and innovative solutions for complex and critical software issues. Our headquarters are located in Veenendaal (NL) and Mechelen (BE). At present, approximately 500 employees are employed by Info Support. Info Support’s working method is characterized by a number of core values: solidity, integrity, craftsmanship, and passion. These core values are intertwined in our work and the way we interact with each other. To ensure that all employees are always up to date with the latest developments, Info Support has an in-house IT Academy that eagerly satisfies the hunger for more or different knowledge and skills. B2 language proficiency in Dutch is required.

Core Responsibilities

Design and evaluate a risk-aware decision model for determining when incidents across heterogeneous public and private cloud environments are suitable for automated recovery and what level of autonomy is appropriate. Develop practical guidance and, optionally, a limited prototype or simulation to assess the model while balancing efficiency, reliability, safety, and maintainability.

Requirements

This is a master's thesis internship focused on self-healing operations in hybrid cloud environments. Dutch proficiency at B2 level is required; no specific prior work experience or technical qualifications are stated.