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An Entropy-Based Load Profiling and Custom Metric Tuning Framework for Kubernetes Autoscaling
编号：62 访问权限：仅限参会人 更新：2026-07-22 16:09:33 浏览：13次 Online

报告开始：2026年07月31日 11:25（Asia/Kolkata）

报告时间：15min

所在会场：[S6] Artificial Intelligence Use Cases [S6-4] Artificial Intelligence Use Cases

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摘要
Containerized microservices are increasingly unpredictable due to workload variations. Dynamic orchestration requires intelligent, high fidelity auto-scaling mechanisms to maintain performance and minimize operational costs. However, the current Kubernetes Horizontal Pod Autoscalers (HPAs) suffer from reactive lag, scaling oscillations, and imbalances in node resource utilization due to the reliance on standard resource metrics and unoptimized metric scraping intervals. This research combines maximum entropy based load profiling (for predictive capacity planning) and a custom metric tuning pipeline (to dynamically tune scaling behaviors). The proposed model reduces container overprovisioning and the cluster Total Cost of Ownership (TCO), and maintains Service-Level Objectives (SLOs) for latency. This work finally presents a strong traffic aware infrastructure model that bridges the gap between proactive traffic forecasting and reactive resource provisioning in modern edge and cloud computing environments.
 
关键词
Kubernetes HPA, Maximum Entropy, Custom Metrics, Load Profiling, TCO Optimization
报告人
Abhimanyu Bajaj
Technical leader Cisco Systems

稿件作者
Abhimanyu Bajaj Cisco Systems
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重要日期

  • 会议日期

    07月30日

    2026

    至

    08月01日

    2026

  • 07月28日 2026

    注册截止日期

  • 07月30日 2026

    初稿截稿日期

主办单位
The United Societies of Science
承办单位
Kongunadu College of Engineering and Technology
协办单位
IEEE Section
IEEE Madras Section
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