Workload Optimization Services
Energy-aware scheduling, carbon-conscious placement, GPU optimization, and workload migration planning for maximum efficiency.
Energy-Aware Workload Scheduling
Intelligent scheduling that considers energy costs, carbon intensity, and thermal constraints when placing and migrating workloads.
- Reduce energy costs
- Lower carbon footprint
- Thermal balance
Carbon-Conscious Placement
Workload placement strategies that minimize carbon emissions by leveraging renewable energy availability and grid carbon intensity.
- Minimize emissions
- Meet ESG goals
- Renewable alignment
GPU Cluster Optimization
Specialized optimization for AI/ML workloads, maximizing GPU utilization while managing thermal and power constraints.
- Higher GPU utilization
- Reduced training costs
- Thermal management
Workload Migration Planning
Intelligent planning for workload migrations that minimizes energy impact and maintains performance SLAs.
- Zero-downtime migrations
- Energy-optimal timing
- SLA compliance
Demand Response Integration
Automated workload shifting in response to grid signals, enabling participation in demand response programs.
- Revenue generation
- Grid stability support
- Peak shaving
Case Studies
Real-world results from organizations that transformed their workload efficiency.
AI Research Laboratory
Challenge
GPU clusters running at 45% utilization with $2.4M annual energy costs and frequent thermal throttling
Solution
Implemented energy-aware scheduling with thermal-conscious GPU placement
Results
- GPU utilization increased to 78%
- Energy costs reduced by $680K annually
- Thermal throttling eliminated
Sustainable Cloud Provider
Challenge
Needed to achieve carbon-neutral operations while maintaining competitive pricing
Solution
Deployed carbon-conscious placement with renewable energy tracking
Results
- 85% of workloads matched to renewable energy
- Carbon intensity reduced by 62%
- Premium sustainability tier launched
Technical Specifications
Enterprise-grade workload optimization with native integration into your existing orchestration and scheduling infrastructure.
Platform Integration
- Kubernetes (custom scheduler, operator)
- VMware vSphere DRS integration
- OpenStack Nova scheduler
- Slurm for HPC workloads
- Custom API for proprietary systems
Frequently Asked Questions
How does energy-aware scheduling work with existing orchestrators?
Our platform integrates with Kubernetes, VMware vSphere, and OpenStack through native APIs and custom schedulers. We provide scheduling hints and constraints that work alongside your existing policies, adding energy and carbon awareness without replacing your orchestration platform.
Can you optimize GPU workloads specifically?
Yes. Our GPU optimization module understands the unique characteristics of AI/ML workloads including batch vs. interactive inference, training job checkpointing, and multi-GPU communication patterns. We optimize placement to minimize energy while respecting NVLink topology and thermal constraints.
How accurate is carbon-conscious placement?
We integrate real-time carbon intensity data from WattTime, ElectricityMap, and regional ISO feeds. Combined with your facility's renewable energy generation and PPA schedules, we achieve 95%+ accuracy in matching workloads to low-carbon energy sources.
What about latency-sensitive workloads?
Our scheduling respects latency SLAs as hard constraints. For latency-sensitive workloads, we optimize within the feasible placement set rather than compromising performance. You define the constraints; we optimize within them.