Netflix-Skunkworks/service-capacity-modeling
Mar 2025 – Mar 2025
1 Month active
Languages Used
JavaPython
Technical Skills
Capacity PlanningPerformance AnalysisPython DevelopmentSystem ModelingTesting
Nick Mahilani
PROFILE
Nick Mahilani
Nikhil Mahilani enhanced the Netflix-Skunkworks/service-capacity-modeling repository by developing a live cluster CPU utilization–based feature for Kafka capacity planning. He refactored the core estimation logic to prioritize real-time cluster CPU data, improving the accuracy and responsiveness of resource provisioning. When live data is unavailable, the model seamlessly falls back to previous calculation methods. Nikhil introduced a standardized target_cpu_utilzation function and created comprehensive tests to validate the new workflow. His work leveraged Python and Java, applying skills in capacity planning, performance analysis, and system modeling to deliver a robust, data-driven solution that supports cost efficiency and operational reliability.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
139
Activity Months1
Your Network
118 peopleSame Organization
@netflix.com95
Shared Repositories
23
Work History
March 2025
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a live cluster CPU utilization–based enhancement to the Kafka capacity model in Netflix-Skunkworks/service-capacity-modeling. The model now uses current cluster CPU utilization to compute needed cores, with a fallback to the previous calculation when live data is unavailable. Introduced a target_cpu_utilzation function and updated the estimation logic to prioritize live data. Added test_plan_certain to validate the new behavior. This work improves capacity planning accuracy and responsiveness, enabling better resource provisioning, cost efficiency, and reliability in dynamic environments. Demonstrated strengths in data-driven modeling, live data integration, test planning, and clean refactoring.
1 Commits • 1 Features
Mar 1, 2025March 2025: Delivered a live cluster CPU utilization–based enhancement to the Kafka capacity model in Netflix-Skunkworks/service-capacity-modeling. The model now uses current cluster CPU utilization to compute needed cores, with a fallback to the previous calculation when live data is unavailable. Introduced a target_cpu_utilzation function and updated the estimation logic to prioritize live data. Added test_plan_certain to validate the new behavior. This work improves capacity planning accuracy and responsiveness, enabling better resource provisioning, cost efficiency, and reliability in dynamic environments. Demonstrated strengths in data-driven modeling, live data integration, test planning, and clean refactoring.
March 2025
Activity
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Quality Metrics
Correctness90.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
JavaPython
Technical Skills
Capacity PlanningPerformance AnalysisPython DevelopmentSystem ModelingTesting
Repositories Contributed To
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