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Integrating Predictive Real-Time Feedback Loops with Federated AI Models for Latency-Aware Resource Allocation in Multi-Cluster Kubernetes Environments

Overview

This research focuses on enhancing resource allocation in Kubernetes environments by integrating predictive real-time feedback loops and federated AI models. The goal is to optimize latency-sensitive workloads while maintaining efficient resource utilization across multiple Kubernetes clusters.

Research Motivation

Identified Gaps:

  1. Limited Integration of Predictive Feedback Loops
    • Existing resource allocation strategies in Kubernetes rely on static or periodic approaches rather than real-time, predictive models.
  2. Underutilization of Federated AI
    • While federated AI is used in edge computing, its application in Kubernetes for resource allocation is minimal.
  3. Latency-Aware Resource Allocation is Underexplored
    • Existing solutions optimize either latency or resource allocation but rarely both in multi-cluster Kubernetes environments.
  4. Lack of Multi-Cluster Scheduling with Real-Time Adaptation
    • Kubernetes federation supports multi-cluster management but lacks dynamic, predictive workload balancing mechanisms.
  5. Insufficient Integration of Edge-Cloud Resources
    • Studies focus on either edge or cloud management but not their seamless integration for latency-critical applications.
  6. Lack of Holistic Evaluation Frameworks
    • Benchmarking frameworks for predictive feedback, federated AI, and latency-aware allocation in Kubernetes are scarce.
  7. Security and Privacy Challenges in Federated Learning
    • Research lacks a security-focused approach for federated AI in Kubernetes resource management.
  8. Trade-Off Between Computational Overhead and Latency Optimization
    • Balancing computational efficiency with latency-sensitive Kubernetes workloads needs further exploration.

Proposed Solution

Key Contributions:

  • Predictive Feedback Loops: Implement real-time monitoring with machine learning models to predict resource demands dynamically.
  • Federated AI Integration: Enable clusters to collaboratively learn workload patterns while preserving data privacy.
  • Latency-Aware Scheduling: Develop a Kubernetes scheduler that integrates latency metrics for efficient task placement.
  • Multi-Cluster Optimization: Utilize Kubernetes federation to balance workloads between edge and cloud environments for improved performance and scalability.

Repository Structure

📂 research-k8s-predictive-feedback
│── 📂 docs             # Documentation and research papers
│── 📂 code          # ML models for predictive feedback loops
│── 📄 README.md        # Overview of the research and repository

Research Roadmap

  • Implement predictive feedback loops with real-time monitoring
  • Integrate federated learning models across multiple clusters
  • Develop a latency-aware custom Kubernetes scheduler
  • Benchmark the proposed solution against existing Kubernetes schedulers

Contributions

Contributions are welcome! Please open an issue or submit a pull request with detailed information.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any inquiries or collaboration opportunities, feel free to reach out.

🚀 Advancing Kubernetes Resource Allocation with AI-Driven Predictive Models!