Expand description
Advanced Quantum Job Scheduling with SciRS2 Intelligence
This module implements sophisticated scheduling algorithms that leverage SciRS2’s machine learning, optimization, and statistical analysis capabilities to provide intelligent job scheduling for quantum computing workloads.
§Features
- Multi-objective Optimization: Uses SciRS2 to balance throughput, cost, energy, and fairness
- Predictive Analytics: Machine learning models predict queue times and resource needs
- Dynamic Load Balancing: Real-time adaptation to platform performance and availability
- SLA Management: Automatic SLA monitoring and violation prediction with mitigation
- Cost and Energy Optimization: Intelligent resource allocation considering costs and sustainability
- Reinforcement Learning: Self-improving scheduling decisions based on historical performance
- Game-theoretic Fairness: Advanced fairness algorithms for multi-user environments
Structs§
- Advanced
Quantum Scheduler - Advanced Quantum Scheduler with SciRS2 Intelligence
- Backend
Score - Cost
Optimization Report - Energy
Optimization Report - Fairness
Report - JobMetrics
- JobRequirements
- Mitigation
Strategy - Performance
Anomaly - Platform
Metrics - SLACompliance
Report - Spending
Analysis - User
Analysis
Enums§
- Mitigation
Urgency - Mitigation urgency levels