Netflix-Skunkworks/service-capacity-modeling
Feb 2025 – Aug 2025
6 Months active
Languages Used
Python
Technical Skills
Backend DevelopmentCapacity ModelingCapacity PlanningPerformance ModelingPerformance OptimizationPerformance Tuning
Akashdeep Goel
PROFILE
Akashdeep Goel
Akashdeep Gill developed and refined capacity modeling features for the Netflix-Skunkworks/service-capacity-modeling repository, focusing on backend development and system design using Python. Over six months, Akashdeep delivered dynamic EVCache replication sizing, integrated buffer calculations for CPU, memory, network, and disk, and centralized buffer logic to improve maintainability and accuracy. He enhanced per-node resource utilization modeling, removed deprecated parameters, and stabilized test suites to ensure reliable validation of capacity planning logic. By tuning memory buffers and correcting disk space estimation, Akashdeep addressed both performance and reliability, enabling more precise, scalable capacity planning and reducing the risk of over- or under-provisioning.
Overall Statistics
Feature vs Bugs
63%Features
Repository Contributions
14Total
Bugs
3
Commits
14
Features
5
Lines of code
564
Activity Months6
Your Network
111 people
Work History
August 2025
3 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Focused on stabilizing EVCache capacity modeling and tightening disk-space estimation to improve capacity planning, reliability, and scalability. Implemented memory accounting corrections and tuned RAM buffers to ensure EVCache instances have sufficient headroom. Enhanced disk space estimation with zero-need reporting for classic deployments and added validations to reject configurations lacking required ephemeral drives. These changes reduce deployment risk, improve resource planning accuracy, and support scalable growth in the Netflix-Skunkworks service-capacity-modeling repo.
3 Commits • 1 Features
Aug 1, 2025Month: 2025-08 — Focused on stabilizing EVCache capacity modeling and tightening disk-space estimation to improve capacity planning, reliability, and scalability. Implemented memory accounting corrections and tuned RAM buffers to ensure EVCache instances have sufficient headroom. Enhanced disk space estimation with zero-need reporting for classic deployments and added validations to reject configurations lacking required ephemeral drives. These changes reduce deployment risk, improve resource planning accuracy, and support scalable growth in the Netflix-Skunkworks service-capacity-modeling repo.
August 2025
June 2025
1 Commits
Jun 1, 2025June 2025 — Focused on validating capacity-modeling tests by calibrating per-node utilization assumptions. Delivered a targeted test fix that adjusts disk, memory, and network utilization values to accurately reflect per-node calculations, strengthening test reliability under varied utilization scenarios. This enhances production readiness and confidence in capacity planning by reducing false negatives in the validation suite.
June 2025
1 Commits
Jun 1, 2025June 2025 — Focused on validating capacity-modeling tests by calibrating per-node utilization assumptions. Delivered a targeted test fix that adjusts disk, memory, and network utilization values to accurately reflect per-node calculations, strengthening test reliability under varied utilization scenarios. This enhances production readiness and confidence in capacity planning by reducing false negatives in the validation suite.
May 2025
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Netflix-Skunkworks/service-capacity-modeling delivered a refined EVCache Capacity Modeling capability that improves capacity planning accuracy by computing per-node memory, network, and disk utilization while accounting for the number of cluster instances per node. The change removes an unnecessary check for instance drive presence in the EVCache model and updates CPU power thresholds in EVCache tests to reflect realistic per-node load patterns. This work directly supports scalable capacity planning and efficient resource provisioning for on-demand workloads. Commit 06625ff4c6a9597c1fcd74a1e275e58cae95ab68.
1 Commits • 1 Features
May 1, 2025May 2025 monthly summary: Netflix-Skunkworks/service-capacity-modeling delivered a refined EVCache Capacity Modeling capability that improves capacity planning accuracy by computing per-node memory, network, and disk utilization while accounting for the number of cluster instances per node. The change removes an unnecessary check for instance drive presence in the EVCache model and updates CPU power thresholds in EVCache tests to reflect realistic per-node load patterns. This work directly supports scalable capacity planning and efficient resource provisioning for on-demand workloads. Commit 06625ff4c6a9597c1fcd74a1e275e58cae95ab68.
May 2025
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 | Repository: Netflix-Skunkworks/service-capacity-modeling. Delivered a refactor of the Capacity Planning Model to centralize buffer computations and cleanly separate deployment calculations from optimization logic in vitals calculation, improving maintainability, reliability, and operational decision speed. No bugs fixed this period; focus was on code quality, correctness, and clearer separation of concerns.
April 2025
2 Commits • 1 Features
Apr 1, 2025Month: 2025-04 | Repository: Netflix-Skunkworks/service-capacity-modeling. Delivered a refactor of the Capacity Planning Model to centralize buffer computations and cleanly separate deployment calculations from optimization logic in vitals calculation, improving maintainability, reliability, and operational decision speed. No bugs fixed this period; focus was on code quality, correctness, and clearer separation of concerns.
March 2025
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling. Key work focused on advancing EVCache capacity planning through buffer integration and stabilizing the capacity planner tests. These efforts improved modeling accuracy, reduced risk of under/over-provisioning, and strengthened the foundation for data-driven capacity decisions across CPU, memory, network, and disk resources.
5 Commits • 1 Features
Mar 1, 2025March 2025 monthly summary for Netflix-Skunkworks/service-capacity-modeling. Key work focused on advancing EVCache capacity planning through buffer integration and stabilizing the capacity planner tests. These efforts improved modeling accuracy, reduced risk of under/over-provisioning, and strengthened the foundation for data-driven capacity decisions across CPU, memory, network, and disk resources.
March 2025
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for Netflix-Skunkworks/service-capacity-modeling. Delivered a dynamic EVCache replication sizing and cost modeling feature that replaces zones_per_region with copies_per_region, aligns replication factor with available zones, and removes hardcoded defaults and deprecated parameters to improve capacity modeling accuracy and cost predictability. The work enhances region-aware capacity planning, reduces modeling drift, and establishes a solid foundation for cost-controlled scaling across regions.
February 2025
2 Commits • 1 Features
Feb 1, 2025February 2025 monthly performance summary for Netflix-Skunkworks/service-capacity-modeling. Delivered a dynamic EVCache replication sizing and cost modeling feature that replaces zones_per_region with copies_per_region, aligns replication factor with available zones, and removes hardcoded defaults and deprecated parameters to improve capacity modeling accuracy and cost predictability. The work enhances region-aware capacity planning, reduces modeling drift, and establishes a solid foundation for cost-controlled scaling across regions.
Activity
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Quality Metrics
Correctness80.0%
Maintainability88.6%
Architecture77.2%
Performance72.8%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Backend DevelopmentCapacity ModelingCapacity PlanningPerformance ModelingPerformance OptimizationPerformance TuningSystem DesignSystem ModelingSystem Performance TuningTestingUnit Testing
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