Define the new internet.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
Look up the words people use online, add the ones we missed, and help make the internet easier to understand.
2,337 definitions
Borrador de traduccion automatica (Spanish) for "CPU Workload Priority": CPU Workload Priority is a compute scheduling signal that tells the platform which work matters most when capacity is constrained for general-purpose processor scheduling. It uses priority classes, preemption rules, and fairness limits so teams can protect critical paths while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used CPU Workload Priority when the service hit a compute ceiling, so the team could protect critical paths before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Edge Cold Start Budget": Edge Cold Start Budget is a compute latency target that limits startup delay for newly scheduled execution for globally distributed runtime. It uses prewarming, smaller packages, and runtime tuning so teams can keep first requests responsive while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Edge Cold Start Budget when the request arrived near a user, so the team could keep first requests responsive before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Virtual Machine Cold Start Budget": Virtual Machine Cold Start Budget is a compute latency target that limits startup delay for newly scheduled execution for isolated guest compute. It uses prewarming, smaller packages, and runtime tuning so teams can keep first requests responsive while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Virtual Machine Cold Start Budget when the VM migrated hosts, so the team could keep first requests responsive before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Release Artifact Signature": Release Artifact Signature is a devops supply-chain record that proves that an artifact came from an expected build path for versioned delivery of code or content. It uses cryptographic signatures, provenance, and verification so teams can trust deployed packages while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Release Artifact Signature when the release notes were generated, so the team could trust deployed packages before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Edge Capacity Forecast": Edge Capacity Forecast is a compute planning model that estimates future resource needs for globally distributed runtime. It uses traffic history, growth assumptions, and utilization trends so teams can avoid surprise shortages while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Edge Capacity Forecast when the request arrived near a user, so the team could avoid surprise shortages before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Release Trace Link": Release Trace Link is a devops observability link that connects a deployment or workflow to runtime evidence for versioned delivery of code or content. It uses trace IDs, span metadata, and release identifiers so teams can debug production changes faster while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The DevOps team used Release Trace Link when the release notes were generated, so the team could debug production changes faster before the deployment window opened.”
Borrador de traduccion automatica (Spanish) for "Edge Resource Quota": Edge Resource Quota is a compute limit that sets how much compute a workload may consume for globally distributed runtime. It uses policy, reservations, and usage tracking so teams can protect shared capacity while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Edge Resource Quota when the request arrived near a user, so the team could protect shared capacity before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Serverless Capacity Forecast": Serverless Capacity Forecast is a compute planning model that estimates future resource needs for event-driven function execution. It uses traffic history, growth assumptions, and utilization trends so teams can avoid surprise shortages while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Serverless Capacity Forecast when the function received a traffic burst, so the team could avoid surprise shortages before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Memory Backpressure Control": Memory Backpressure Control is a compute stability pattern that slows incoming work when downstream capacity is limited for volatile runtime storage. It uses queues, retry budgets, and admission control so teams can avoid overload cascades while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Memory Backpressure Control when the process approached its memory limit, so the team could avoid overload cascades before the workload scaled up.”
Borrador de traduccion automatica (Spanish) for "Container Autoscaling Policy": Container Autoscaling Policy is a compute control loop that changes capacity based on demand signals for packaged application runtime. It uses metrics, thresholds, and cooldowns so teams can match resources to load while keeping evidence, reliability, and public-safe operational boundaries clear.
“Ejemplo en borrador: The platform engineering team used Container Autoscaling Policy when the image started on a new node, so the team could match resources to load before the workload scaled up.”