Job scheduling policies
Scheduling policies are simulated on recorded workloads to compare waiting times, utilisation and fairness between user groups.
GCRI · Data and Compute
Optimisation for computing
Optimisation and operations research schedule jobs, place workloads and plan capacity. In this field they improve utilisation of shared systems, reduce energy cost by shifting flexible work, and size future capacity against uncertain demand.
01Register
Optimisation for computing on the record.
Method recordSTEM method
- MethodOptimisation and operations research
- Applied inData and Compute
- Data3 inputs
- Checks3 validation steps
- Canonical pageSTEM Commons
02Application
What optimisation and operations research does here.
Optimisation and operations research develop mathematical methods for allocating resources and making decisions under constraints. They support planning and logistics in complex, interconnected systems. In data and compute and energy, optimisation supports prepositioning of supplies, network investment and recovery sequencing. Robust formulations help choose options that perform acceptably across many scenarios. The canonical description is on the STEM Commons.
Carbon-aware workload shifting
Flexible jobs are moved to times or places with lower-carbon electricity, and the effect on emissions and turnaround is estimated.
Capacity planning
Future demand scenarios are combined with hardware, energy and staff costs to choose system sizes and procurement timing.
03Monitor
Optimisation run.
The screen a team would use to follow this method: its measures, states and decisions. Names are examples and values are illustrative.
Optimisation run
Data and compute · Optimisation for computing · Run R-8158
Feasible, gap closingGCRI · Data and Compute
Convergence
Method card
- Method
- Optimisation and operations research
- Variables
- 57,078
- Optimality gap
- 0.5 pct
- Label
- MODELLED
Binding constraints
| Capacity | 0 | 36 | Binding |
| Budget | 0 | 0.5 | Binding |
| Service level | 7 | 0 | Slack |
04Data
What the method consumes.
Datasets and records used by optimisation and operations research on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.
| Data | Role | Label |
|---|---|---|
| Scheduler logs | Workload replay | OBSERVED |
| Grid carbon intensity data | Shifting signals | ESTIMATED |
| Demand projections | Planning | FORECAST |
05Methods and evidence
How optimisation for computing 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 optimisation for computing 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 |
- Lineage
- W3C PROV record from source to figure
- Principles
- FAIR: findable, accessible, interoperable, reusable
- Gaps
- shown as missing, never filled silently
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.
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
| Check | How it is done |
|---|---|
| Replay validation | Simulations are validated by reproducing recorded schedules |
| Fairness metrics | Policies are assessed for effects on small and new users |
| Scenario robustness | Capacity plans are tested across demand scenarios |
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
07Process
How the process runs.
Process explorer · Process
Optimisation for computing: N-1 contingency assessment
5 steps·3 stages
Process
Optimisation for computing: N-1 contingency assessment
The EU System Operation Guideline names contingency lists, analysis and handling; Nexus describes no N-1 process, so only these steps are shown.
01 Inputs
- 01Network state and modelRecord · The system operatorThe analysis starts from the operating state and network model of the system operator. Their content is set by that operator.
02 Assessment
- 02Contingency listRecord · The system operatorThe System Operation Guideline lists contingency lists as a named step. The list contents are set by the operator and are not stated here.
- 03Contingency analysisModel · The system operatorThe System Operation Guideline lists contingency analysis as a named step. The method, tools and limits are not stated here.
- 04Contingency handlingStep · The system operatorThe System Operation Guideline lists contingency handling as a named step. What it requires is not stated here.
03 Operator
- 05Operators decide; the programme controls no gridActor · The operatorWater and power operators decide any real-world change to their systems. Exercises give the programme no control of operational technology.
A national mission · Decision rightsOutline of an externally published process, not a Nexus process and not a record of operation. The published Nexus documents describe no N-1 assessment. No programme runs one and every programme is at service level R0.
08Connections
Topics that rely on this method.
09Stewardship
Who maintains the method.
Systems Science and Modelling stewards optimisation and operations research for the Consortium and reviews how it is applied across platforms.
10Connected
Where this page leads across the Consortium.
11Questions
Questions and answers.
What are the limits of optimisation and operations research in this field?
Optimal solutions can be fragile when assumptions are wrong. Objectives may omit values that are hard to quantify.
How are results checked?
Solutions are checked for feasibility, tested through simulation and compared with historical decisions. Sensitivity analysis examines how results change with parameters.
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.
Where else is this method applied?
The canonical method page lists every platform that applies it.
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