MCS Thesis Examination: Seyed Pouria Zahraei

Abstract

    In this research work, we propose an underlay based on Kubernetes to enhance the scalable fault tolerance of the General Intensional Programming System's distributed run-time demand-driven backend to gather digital evidence from GitHub repositories and encode them in Forensic Lucid for further analysis in the integrated OpenTDIP environment.

    We developed a solution so that forensic investigators could use GitHub to gather a dataset to investigate program flaws and vulnerabilities related to security from GitHub projects written in different programming languages. For this purpose, we design and implement a JSON demand-driven encoder to perform a Forensic Lucid conversion pipeline (data extraction, format conversion, and file compilation). In order to distribute the execution, we utilized the GIPSY distributed computing system.

    We also integrated Kubernetes with GIPSY distributed computing system in order to improve the configuring, starting up and registering GIPSY nodes, so that GIPSY nodes could get registered automatically without any manual configuration. In addition, provide a mechanism to have a scalable fault-tolerant system so that when a GIPSY node dies, it will handle reallocation, configuration and registration of the GIPSY nodes automatically.

Examining Committee

• Dr. Todd Eavis (Chair) 
• Dr. Joey Paquet & Serguei Mokhov (Supervisor)
  
• Dr. Weiy (Ian) Shang (Examiner)
• Dr. Todd Eavis (Examiner)