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Mixed Workload

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360 Smart Cloud
360 Smart Cloud
Jan 10, 2024 · Cloud Native

Mixed Workload Scheduling (混部) in Kubernetes: Challenges, Core Technologies, and Koordinator Enhancements

The article analyzes low CPU utilization in pure online Kubernetes clusters, introduces mixed‑workload (online + offline) scheduling to improve resource efficiency, explains core techniques, kernel QoS features, and details Koordinator‑based implementations such as node resource reservation and scheduling adjustments.

KoordinatorMixed WorkloadQoS
0 likes · 13 min read
Mixed Workload Scheduling (混部) in Kubernetes: Challenges, Core Technologies, and Koordinator Enhancements
iQIYI Technical Product Team
iQIYI Technical Product Team
Nov 17, 2023 · Big Data

Mixed Workload Co-location of Big Data and Online Services at iQIYI: Design, Implementation, and Results

iQIYI’s mixed‑workload system colocates Spark/Hive big‑data jobs with online video services by running YARN NodeManagers inside Kubernetes, using an Elastic YARN Operator, Koordinator‑driven CPU oversubscription, and remote shuffle, boosting online CPU utilization from ~9 % to over 40 % and saving tens of millions of RMB annually.

Mixed WorkloadResource Schedulingbig data
0 likes · 19 min read
Mixed Workload Co-location of Big Data and Online Services at iQIYI: Design, Implementation, and Results
HomeTech
HomeTech
Jan 13, 2022 · Cloud Native

AutoKH: A Mixed‑Workload Resource Management Solution on Kubernetes and Hadoop

AutoKH is a cloud‑native mixed‑workload framework that integrates Kubernetes and Hadoop to dynamically schedule online and offline tasks, improve CPU and memory utilization, enforce priority classes, and ensure service stability through operators, CronHPA, and resource‑control components.

CronHPAHadoopMixed Workload
0 likes · 19 min read
AutoKH: A Mixed‑Workload Resource Management Solution on Kubernetes and Hadoop
Baidu Geek Talk
Baidu Geek Talk
Jan 5, 2022 · Cloud Native

Baidu Cloud‑Native Mixed Workload (Offline Co‑location) Technology Overview

Baidu’s mixed‑workload approach co‑locates offline batch jobs with latency‑sensitive online services on shared nodes, using a dynamic resource view, priority‑based scheduling, cpuset/NUMA isolation, eBPF policies, and predictive profiling, boosting CPU utilization above 40 % and saving billions of RMB in total cost of ownership.

Mixed WorkloadOffline ComputingResource Scheduling
0 likes · 17 min read
Baidu Cloud‑Native Mixed Workload (Offline Co‑location) Technology Overview