Understand the data movement
Measure input volumes, processing needs, output size and the cost of moving data between locations.
Proximity compute
Put processing closer to the work.
Assess local compute for data-intensive, time-sensitive or connectivity-constrained operations. Choose the location around the workload and its dependencies.
Discuss a proximity workload
Deployable pod
Compute at the point of operation.
A transportable modular environment for local applications, processing and campaign operations, with relocation planned into the lifecycle.
Explore our deployment systems ↗Some data is more useful close to where it is created.
Moving every image, sensor feed or processing job to a distant environment can add network demand and delay. Selected workloads may benefit from processing near the source, with results and approved data shared onwards.
Where it fits
- Local image, video and sensor processing
- Operational analytics and data preprocessing
- Selected inference near equipment and users
What the service needs to establish
Test performance where it matters
Define acceptable latency and network dependencies for the actual application rather than assuming all edge workloads are alike.
Integrate with the wider environment
Connect suitable local processing to existing cloud, identity, applications and operational systems under agreed policies.
Questions worth asking.
Does proximity mean everything stays on site?
No. A hybrid design can keep selected processing local while approved results, backups or other workloads use regional or cloud services.
Can satellite connectivity support it?
Sometimes. Suitability depends on data movement, latency, availability and the application. Connectivity options require workload and site validation.
How is this different from operational compute?
Operational compute describes what runs. Proximity describes why its location matters. One service may meet both needs.
Connected needs
Discuss proximity compute.
Bring the systems, location and service requirement.