Case Study
A Global Technology Partner – Data Center Cost Optimization at Scale
A global technology leader needed to optimize its massive data center resources to reduce costs and improve efficiency.
- Inaccurate forecasting led to high capital risk from over-ordering hardware.
- Rigid resource allocation resulted in significant amounts of idle, unused assets.
- Inefficient workload packing caused sub-optimal utilization of existing servers.
Solution Implementation
- A sophisticated forecasting model was built for predictive hardware ordering.
- Idle compute time was transformed into sellable, tiered virtual resources.
- A reinforcement learning agent was used to intelligently "pack" workloads and maximize server utilization.
Technology Used
- Category : Technology/Platform/Methodology
- Forecasting Models : Hierarchical Ensemble Models, LSTM, ARIMA
- Optimization : Linear Programming, Reinforcement Learning (RL)
- Infrastructure : Internal Cluster Manager (Borg)
- Core Concepts : Event Embeddings, Virtual Resource Tiers, Preemptible Jobs, Spatial Flexibility
Results and Impact
- Predictive ordering dramatically reduced capital expenditure on hardware.
- Selling virtualized idle compute time created a new high-margin revenue stream.
- Intelligent resource pooling maximized the ROI on all existing assets.
- The partner shifted to a more agile and financially efficient planning model.