Auto-Scaling for Embedded Python Workloads in IRIS

Integrate native auto-scaling capabilities for embedded Python workloads within InterSystems IRIS. This feature would dynamically allocate or deallocate resources (CPU, memory) to Python processes running inside IRIS containers based on real-time demand, without manual intervention. It would leverage Kubernetes-style orchestration logic but operate seamlessly within standalone IRIS deployments. 2️⃣ Target Audience

• Developers building AI/ML microservices using IRIS Embedded Python.

• System Administrators managing IRIS infrastructure for variable workloads.

• Enterprises running resource-intensive Python workloads (e.g., real-time analytics, NLP, predictive modeling).

3️⃣ Problem Solved

Problem: Python workloads in IRIS (e.g., PyTorch inference, pandas data processing) often experience unpredictable spikes in demand. Manually scaling resources is inefficient, leading to:

• Over-provisioning: Wasted resources during low-usage periods.

• Under-provisioning: Performance degradation or failures during peak loads.

• Operational Overhead: DevOps teams must constantly monitor and adjust resources.

4️⃣ Impact on Product Efficiency, Stability, and Reliability

• Efficiency: Optimizes resource usage (cost savings of 30–50% for cloud deployments).

• Stability: Prevents crashes due to memory/CPU exhaustion during traffic surges.

• Reliability: Ensures consistent performance for time-sensitive workloads (e.g., healthcare analytics).

• Scalability: Enables hands-free handling of workload fluctuations.

5️⃣ Specific Use Case Scenario

Scenario: A hospital uses IRIS to process real-time patient data from IoT devices. Python scripts analyze ECG streams for anomalies using TensorFlow.

• Without Auto-Scaling:
  At 9:00 AM, 1,000 patients trigger simultaneous monitoring. Python processes overload, causing delays in critical alerts. Administrators scramble to manually allocate resources.

• With Auto-Scaling:

  1. Low Demand (3:00 AM):

     • 5 active patient monitors → IRIS runs 2 Python pods (low CPU/memory).

  2. Peak Demand (9:00 AM):

     • 1,000 patients trigger monitoring → IRIS detects load spike.

     • Automatically spins up 20 Python pods in <10 seconds.

  3. Post-Peak (10:00 AM):

     • Pods scale down to 5, freeing resources.

Outcome: ECG alerts are processed in real-time, resource costs drop by 40%, and clinical staff receive uninterrupted insights.

Why This Idea Stands Out

• Real-World Alignment: Targets booming AI/ML use cases in IRIS.

• Cloud-Native: Aligns with DevOps trends (Kubernetes, serverless).

• Tangible ROI: Reduces costs while boosting reliability.