# uber cpu stateful workloads

Published articles for uber cpu stateful workloads.

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## Uber Moves Beyond CPU Cores with Performance-Aware Scheduling for Stateful Workloads

DevFeed: [Uber Moves Beyond CPU Cores with Performance-Aware Scheduling for Stateful Workloads](<https://devfeed.tech/articles/uber-moves-beyond-cpu-cores-with-performance-aware-scheduling-for-stateful-workloads-102114.md>)

Original publisher: [Read original article](<https://www.infoq.com/news/2026/10/uber-cpu-stateful-workloads/>)

Author: Craig Risi

Published: 2026-10-27T12:00:00Z

Content type: article

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [scheduling](<https://devfeed.tech/topics/scheduling.md>), [Stateful](<https://devfeed.tech/topics/stateful.md>), [Distributed Systems & Parallel Computing](<https://devfeed.tech/topics/distributed-systems-parallel-computing.md>), [resource orchestration](<https://devfeed.tech/topics/resource-orchestration.md>), [Load Balancing](<https://devfeed.tech/topics/load-balancing.md>)

Tags: [1-4](<https://devfeed.tech/tags/1-4.md>), [abstraction](<https://devfeed.tech/tags/abstraction.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [apache-kafka](<https://devfeed.tech/tags/apache-kafka.md>), [arm](<https://devfeed.tech/tags/arm.md>), [automation-in-the-cloud-and-management-at-scale](<https://devfeed.tech/tags/automation-in-the-cloud-and-management-at-scale.md>), [aws](<https://devfeed.tech/tags/aws.md>), [balancing](<https://devfeed.tech/tags/balancing.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [capacity](<https://devfeed.tech/tags/capacity.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [compute](<https://devfeed.tech/tags/compute.md>), [core](<https://devfeed.tech/tags/core.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [cpu-time](<https://devfeed.tech/tags/cpu-time.md>), [data-centres](<https://devfeed.tech/tags/data-centres.md>), [devops](<https://devfeed.tech/tags/devops.md>), [fleet](<https://devfeed.tech/tags/fleet.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [make](<https://devfeed.tech/tags/make.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [news](<https://devfeed.tech/tags/news.md>), [performance](<https://devfeed.tech/tags/performance.md>), [scheduling](<https://devfeed.tech/tags/scheduling.md>), [server](<https://devfeed.tech/tags/server.md>), [state-machine](<https://devfeed.tech/tags/state-machine.md>), [stateful](<https://devfeed.tech/tags/stateful.md>), [uber](<https://devfeed.tech/tags/uber.md>), [uber-cpu-stateful-workloads](<https://devfeed.tech/tags/uber-cpu-stateful-workloads.md>), [units](<https://devfeed.tech/tags/units.md>)

### AI overview

Uber's Normalized Compute Units (NCUs) let Odin allocate comparable compute capacity across servers whose CPU performance varies substantially. Odin assigns each server type a performance factor, initially measured during hardware qualification and refined with production workload data, then uses it to translate workload requirements into different numbers of physical cores. This addresses a reliability risk for stateful workloads, which cannot readily shift requests away from a slower server; stateless services can more often use load balancing. Across about 1.4 million automatically scaled cores, Kafka's utilization gap between server types fell from roughly eight to three percentage points, and total Kafka CPU allocation dropped about 8%. Uber contrasts NCUs with Kubernetes CPU requests in cores or millicores, which represent CPU time without expressing relative processor performance.

### Source excerpt

Uber has introduced Normalized Compute Units (NCUs) to make CPU allocation more consistent across its highly heterogeneous server fleet. By Craig Risi