I am currently appointed as a Temporary Demonstrator in Information & Communication Technology in the Department of Information & Communication Technology, Faculty of Technological Studies, University of Vavuniya, effective from 24 August 2026. Earlier, I worked as a Software Engineer Intern in Software Engineering and Project Management at E Zone Technologies (Pvt) Ltd, where I developed production web solutions and supported project coordination and client requirements across technology-led initiatives.
I hold a BSc (Hons) in Information Technology from the University of Vavuniya, with First Class honours and a GPA of 3.810/4.00, and My academic and professional journey has been complemented by a Diploma in Human Resource Management from IMBS Green Campus, which has enhanced my understanding of organizational dynamics and team management.
I enjoy building, understanding, and improving technology whether that means writing software, designing experiences, coordinating a project, or exploring a research problem.
Best performance in BSc (Hons) in Information Technology, University of Jaffna
Best overall performance in BSc in Information Technology, University of Vavuniya
Most Dedicated Sports Council Member of the Year, University of Vavuniya
Badminton
Tools & Technologies
BSc (Hons) in Information Technology
First Class, GPA 3.810/4.00
Current academic programme with relevant coursework in project management, HCI, software engineering, QA, web programming, e-commerce, and database management.
Diploma in Human Resource Management
Reading
Currently reading this diploma while continuing academic and professional work.
Higher National Diploma (HND) in Information Technology
Completed
Foundation for advanced studies in information technology and software practice.
This research examines how the correctness of Kubernetes Horizontal Pod Autoscaler (HPA) scaling decisions can be evaluated beyond conventional infrastructure-level metrics such as CPU and memory utilization.
Autoscaling systems often appear healthy when utilization metrics look stable, but the actual decisions may not align with workload demand or service correctness. The project addresses this gap by focusing on observability-based evaluation of autoscaling behaviour.
Main Objective
To evaluate the correctness of HPA scaling decisions using observability signals and to develop metrics that reflect whether scaling actions are appropriate, timely, and effective.
Key Contribution
The research introduces and evaluates two correctness-oriented metrics—Scale Reaction Delay (SRD) and Scale Effectiveness Score (SES)—alongside a rule-based classifier to assess scaling decisions using observability data without modifying the autoscaler itself.
Technologies & Tools
Research Status
Completed as part of final-year research
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© 2026 Chamodi Indrejith. All rights reserved.