mixed workloads

Xiaohongshu Tech REDtech

Nov 27, 2023 · Cloud Native

Mixed-Workload Scheduling and Resource Utilization Optimization in Xiaohongshu's Cloud-Native Platform

Xiaohongshu’s cloud‑native platform adopted a four‑stage mixed‑workload scheduling strategy—reusing idle nodes, whole‑machine time‑sharing, normal mixed pools, and a unified scheduler (Tusker) that coordinates CPU, GPU and memory across Kubernetes and YARN—boosting average cluster CPU utilization from under 20 % to over 45 % and delivering millions of low‑cost core‑hours while preserving QoS for latency‑sensitive, mid, and batch jobs.

CPU utilizationCloud NativeKubernetes

0 likes · 19 min read

Mixed-Workload Scheduling and Resource Utilization Optimization in Xiaohongshu's Cloud-Native Platform

Bilibili Tech

Aug 27, 2022 · Cloud Native

Mixed Workload Co-location Practices in Bilibili's Kubernetes Cloud Platform

Bilibili’s Kubernetes cloud platform boosts server utilization by co‑locating latency‑sensitive online services with batch‑oriented offline jobs on the same nodes, using custom schedulers, extended resources, dynamic CPU/memory isolation, and a management console, achieving average CPU usage around 35 % and significant cost savings.

Cloud NativeKubernetesResource Scheduling

0 likes · 17 min read

Mixed Workload Co-location Practices in Bilibili's Kubernetes Cloud Platform

DataFunTalk

Jun 20, 2020 · Cloud Native

Automated Elastic Scaling for Million‑Scale Core Services and Mixed Workloads on ByteDance's Private Cloud Platform

This article presents ByteDance's private cloud platform TCE architecture and explains how automated elastic scaling, dynamic over‑commit, and mixed‑workload deployment are used to improve resource utilization for millions of services, balancing online peak demand with offline batch tasks.

Cloud NativeKuberneteselastic scaling

0 likes · 25 min read

Automated Elastic Scaling for Million‑Scale Core Services and Mixed Workloads on ByteDance's Private Cloud Platform