SUKAMO_ORG
~/src/modules
Stable v4.2.0-rc

Hyper-Scheduler

Predictive resource allocation for batch processing using reinforcement learning.

Production Context

Integration with Mainframe Modernizer

Ensures 99.999% uptime during mainframe migration by predicting batch spikes before they occur.

View Commercial Solution

The Concurrency Problem

Legacy mainframes excel at I/O throughput. Modern cloud architectures excel at compute scaling. The mismatch between these two creates the "Batch Window Violation" problem during migration.

The Hyper-Scheduler uses a rolling window LSTM (Long Short-Term Memory) model to ingest 10 years of mainframe logs and predict resource contention.

Performance Metrics

SCHEDULER_DAEMON
01$> analyze_queue --depth=5000
02Detected spike in JOB_A77 (Finance Batch) at 02:00 UTC
03Pre-warming Kubernetes nodes... [OK]
04Allocating isolated IOPS buffer... [OK]
05Outcome: 0ms throttle on high-priority threads.
06Optimization delta: +40% throughput
07

Technical Implementation

Unlike standard Kubernetes Horizontal Pod Autoscalers (HPA) which react to CPU spikes, this module is proactive. It scales infrastructure before the job starts.

  1. Ingest: Syslog stream from z/OS.
  2. Predict: Time-series forecasting of job duration.
  3. Act: Pre-provision cloud resources via Terraform hooks.