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.
What high-performance, cloud and edge computing does here.
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.
Capacity planning for peaks
Systems serving whole populations are sized for peak demand, such as benefit surges, with load testing and autoscaling verified.
Multi-site and multi-provider hosting
Critical services are designed to run across sites or providers so that one failure does not stop them.
Portability engineering
Systems use open standards and containerisation so that they can be moved between hosting environments when needed.
03Monitor
Compute workload.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Compute workload
Digital public infrastructure · Computing for DPI · Run R-3135
Channels reporting
92of 100Normal
Median latency
226ms
Records failing checks
0.5per 1,000Watch
Backlog
30min
Gaps on 4 channelsGCRI · Digital Public Infrastructure
Latency, p95
Events
06:46WarnChannel C-59 stale
07:56InfoSchema v4 accepted
08:13ErrorChecksum mismatch, batch quarantined
08:43DoneReplay complete
Compute workload: the screen's structure, terms and states for Digital public infrastructure.Example names and illustrative values; a design, not a record or a live system.
04Data
What the method consumes.
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
05Methods and evidence
How Computing for DPI is modelled, measured and tested.
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.
Datasets and provenance
The data classes behind Computing for DPI and what is recorded about each before it is used.
Data classes and the provenance fields recorded for each; a dataset is listed by name only once its licence admits the use.
FAIR guiding principles · GO FAIR and FORCE11W3C PROV · World Wide Web ConsortiumGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
06Validation
How results are checked.
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.
07Process
How the process runs.
Process explorer · Process
Computing for DPI: Build, sign and accept
10 steps·4 stages·2 gates
Process
Computing for DPI: Build, sign and accept
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.
01 Request and sources
01Admitted mission becomes a cycle projectRecord · Universe to FoundryAn admitted mission becomes a cycle project with a decision case.
02Qualify data sources and resourcesStep · the Global Centre for Risk and Innovation (GCRI)GCRI qualifies data sources and resource offers and builds the scenario models. An offer is not an allocation.
02 Build and verify
03Source snapshotRecord · NXS-FAC (blueprints, builds and signing)The snapshot pins the commit, dependencies and generator versions.
04Build contractRecord · NXS-FAC (blueprints, builds and signing)The build contract declares the artefact kind, environment and reproducibility envelope.
05Integrate and buildModel · GCRIGCRI integrates software and data, signs builds, and tests security and interoperability.
06Verification suiteGate · NXS-FAC (blueprints, builds and signing)Required and advisory checks stay separate. Reproducibility needs pinned inputs, declared nondeterminism and independent rebuilds.
03 Sign and admit
07Sign the exact artefactStep · NXS-FAC (blueprints, builds and signing)NXS-FAC builds, attests and requests a signature via a scoped signer identity. It holds no key material.
08Package admissionStep · NXS-NXQ (software, models and capability lifecycle)NXS-NXQ receives the signed package and keeps admission evidence that binds the task, data class, evaluation suite and expiration.
04 Accept
09Core Build ReportRecord · GCRIThe Core Build Report is the go or no-go record. Universe assembles the Core and holds the decision for the exercise.
10G3 · Build accepted for exerciseGate · The Global Risks ForumGRF records whether the build is accepted for exercise. A signed build or release candidate is not permission to deploy to production.
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.
What are the limits of high-performance, cloud and edge computing in this field?
Concentration in few providers or regions creates shared points of failure. Energy, water and supply chain constraints limit capacity growth.
How are results checked?
Systems are tested through benchmarks, load testing and failover exercises. Numerical reproducibility is checked across hardware and software configurations.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method's use, not a run; no result is reported here.
Reports is the Consortium's quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.
GCRIDigital Public InfrastructureStrategic SystemsExponential Technology
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Digital Public Infrastructure
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