Smart Observability Middleware For Adaptive Microservice Application Monitoring
A cleaner way to understand complex systems
ObserviQ helps teams understand metrics, logs, alerts, and anomalies across distributed services through a cleaner observability experience.
Domain
A research direction focused on making observability more interpretable, structured, and system-aware.
Literature survey
Review the existing academic and industry work related to observability, monitoring systems, anomaly detection, and intelligent system interpretation.
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Research gap
Current observability platforms expose metrics, logs, alerts, and anomalies, but they still rely heavily on engineers to interpret what those signals mean across the system.
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Research problem
Define the central challenge of helping teams move from raw observability data to meaningful system understanding with less manual interpretation.
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Research objectives
Present the goals of the project, including building a clearer observability workflow, improving interpretability, and structuring multi-signal analysis.
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Methodology
ObserviQ is designed as an intelligent middleware layer that structures, analyzes, and connects different observability signals through specialized agents.
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Technologies used
The solution combines a modern web interface, observability workflows, agent-driven analysis, and system-level service modeling to present a clearer operational picture.
Read More →Milestones
Key academic checkpoints across the project timeline.
This section outlines the major project assessments, along with their dates, allocated marks, and a short description of what each milestone covers.
Initial proposal submission covering the project idea, scope, research direction, objectives, and early planning.
Documents
A structured index of project documents and deliverables.
This section lists the major project documents that have been produced or are planned for submission, with direct links for viewing when available.
Slides
Presentation decks across the project journey.
A collection of the presentation slides used for proposal, progress reviews, and final academic submissions.
About us
Meet the team behind the project.
This section introduces the four group members, along with identification photographs, e-mail details, and relevant project contributions.
Rusiru De Silva
Metric & Signal Discovery Lead
rusiru.zyner@gmail.comLeads research and development for metrics and signal processing. Contributes to defining signal strategies for the project.
Nimasha Piyumini
Log Structure & Enrichment Lead
nimasha.piyumini@gmail.comFocused on structuring and enriching the system's logs, improving data flow and enhancing insights derived from system activities.
Nayanahari Kusalanjani
Adaptive Alert Tuning Lead
nayanaharikusalanajani@gmail.comDeveloped and fine-tuned adaptive alerting mechanisms to improve the system's responsiveness to critical events.
Yomith Gamage
Anomaly Detection & Insight Lead
lavinduyomith2016@gmail.comManaged the detection of anomalies in system behavior and generated insights to support critical decision-making.
Prof. Sanvitha Kasthuriarachchi
Supervisor
sanvitha.k@sliit.lkProvided overall guidance and supervision for the project, ensuring alignment with academic standards and project goals.
Ms. Akshi De Silva
Co-Supervisor
akshi.d@sliit.lkProvided co-supervision and support for the project, contributing to its overall success and academic rigor.
Contact us
How can we help?
Get in touch for project questions, presentation requests, document access, or general academic communication.
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General inquiries
Reach out for project-related questions, or general information about the system.