List of Topics:
Location Research Breakthrough Possible @S-Logix pro@slogix.in

Office Address

Social List

AI-Driven Auto-Scaling for Cloud-Native Applications

AI-Driven Auto-Scaling

AI-Driven Auto-Scaling for Cloud-Native Applications

  • Use Case: Cloud-native applications often experience unpredictable workloads (e.g., e-commerce flash sales, streaming services, or IoT event bursts). Traditional rule-based auto-scaling (CPU/Memory thresholds) may cause over-provisioning (waste of resources) or under-provisioning (poor user experience).An AI-driven auto-scaling system dynamically predicts demand and scales resources proactively.

Objective

  • Develop an AI/ML-based auto-scaling mechanism for cloud-native applications.

    Improve QoS (Quality of Service) and QoE (Quality of Experience) by reducing latency and failures during sudden load spikes.

    Optimize cloud cost vs. performance trade-off.

    Compare AI-driven scaling against traditional AWS Auto Scaling policies.

Project Description

  • Build a cloud-native microservices application (e.g., order processing or video streaming).

    Use AI/ML models (e.g., LSTM, Prophet, or Reinforcement Learning) to predict incoming traffic based on historical logs and metrics.

    Deploy predictive auto-scaling to dynamically provision/de-provision EC2 instances, containers (ECS/EKS), or serverless functions (Lambda).

    Continuously retrain the model with live workload patterns for real-time adaptation.

    Evaluate performance on throughput, latency, cost, and resource utilization.
  • AWS Services & Purpose :
    Service Purpose
    Amazon CloudWatch Collect application performance metrics (CPU, latency, request count) as training data for ML models.
    Amazon SageMaker Build, train, and deploy predictive scaling models (e.g., demand forecasting with ML/AI).
    AWS Auto Scaling Integrate AI-driven predictions with auto-scaling groups for EC2/ECS/EKS.
    Amazon EC2 / ECS / EKS Host cloud-native microservices that need dynamic scaling.
    Amazon Lambda Trigger scaling events or inference logic from SageMaker predictions.
    Amazon S3 Store historical logs, metrics, and ML model artifacts.
    AWS Step Functions Orchestrate the AI prediction and scaling workflow.
    AWS Cost Explorer Evaluate cost savings compared to traditional scaling.