Capacity planning for peaks
Systems serving whole populations are sized for peak demand, such as benefit surges, with load testing and autoscaling verified.
GCRI · Digital Public Infrastructure
High-performance, cloud and edge computing host the public digital infrastructure that whole populations use. In this field the questions are capacity, availability, hosting choices, concentration and the ability to move systems between providers.
High-performance, cloud and edge computing provide the processing and storage capacity for simulation, analytics and digital services. They are both an enabler of risk analysis and a critical dependency for society. In digital public infrastructure and cyber and digital resilience, computing capacity determines how fast and at what resolution risks can be modelled. Its concentration is itself a systemic dependency for finance, health and government services. The canonical description is on the STEM Commons.
Systems serving whole populations are sized for peak demand, such as benefit surges, with load testing and autoscaling verified.
Critical services are designed to run across sites or providers so that one failure does not stop them.
Systems use open standards and containerisation so that they can be moved between hosting environments when needed.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Digital public infrastructure · Computing for DPI · Run R-3135
Datasets and records used by high-performance, cloud and edge computing on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Load test results | Capacity | OBSERVED |
| Hosting inventories | Dependencies | DECLARED |
| Availability monitoring | Performance | OBSERVED |
The model, its assumptions, its data and its uncertainty, in the form a scientist or engineer would check. Structures follow published methods. The values drawn are illustrative, not findings.
The data classes behind Computing for DPI and what is recorded about each before it is used.
| Data class | Provenance recorded | Resolution stated | Licence checked |
|---|---|---|---|
| Satellite imagery, optical and radar | sensor, processing level, acquisition date | pixel size and revisit | per scene |
| Reanalysis and climate model output | model, experiment, member, version | grid and time step | per release |
| In-situ sensors and gauges | station, instrument, calibration record | point and sampling rate | per network |
| Population and asset exposure | method, vintage, input sources | cell size and unit | per release |
| Boundaries and reference geography | producer, edition, date | scale | per edition |
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
| Check | How it is done |
|---|---|
| Failover testing | Failover between sites is tested regularly |
| Peak verification | Capacity is verified against realistic peak scenarios |
| Portability trial | Moving a component to another environment is trialled |
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
Process explorer · Process
10 steps·4 stages·2 gates
Process
An admitted mission becomes a cycle project; the build is pinned, verified and signed, and the Global Risks Forum (GRF) records acceptance for exercise, not permission to deploy.
The programme year · NXS-FAC · UniverseDesigned programme process, not a record of operation. No cycle has run and every programme is at service level R0. A gate is a programme decision, never a public-authority approval, certification or deployment authorisation. The record order follows the Builds and Releases room lists of NXS-FAC.
Data and Compute stewards high-performance, cloud and edge computing for the Consortium and reviews how it is applied across platforms.
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