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1
Intro
2
JD Retail Technological Infrastructure Group
3
JDOS (JD Data Center OS) Architecture
4
Resource Management: Challenges
5
A Typical JDOS Cluster
6
Bottleneck Analysis and Optimization
7
Customization and Optimization
8
Preemption and Admission Control
9
Group Scheduling and Binding
10
A Closed Loop System
11
Advanced Scheduling
12
Right Sizing of CPU Resource
13
Resource Usage and Performance Tradeoff
14
Memory Scheduling for Online Services
15
Memory Utilization of Online Services
16
Host Selection Optimization
17
Mixed Workload Placement
18
Hybrid Resource Pools
19
June 18 Anniversary Sale (2019)
20
Acknowledgements
Description:
Explore cost-effective scheduling strategies for massive container deployments in Kubernetes through this conference talk by Yuan Chen from JD.com. Learn about JD's experience running one of the world's largest Kubernetes clusters in production, supporting diverse workloads from e-commerce to big data and machine learning. Discover how JD overcomes scalability and cost-effectiveness challenges through advanced scheduling techniques, including fine-grained resource usage monitoring, machine learning-driven allocation, mixed workload co-scheduling, and millisecond-level elastic scaling. Gain insights into Archimedes, JD's Kubernetes scheduling system, and its performance during JD's June 18 anniversary sale event, handling $24.7 billion in transactions. Delve into topics such as JDOS architecture, resource management challenges, bottleneck analysis, customization, preemption, admission control, group scheduling, and advanced scheduling techniques for CPU and memory optimization.

Cost-Effective Scheduling of Massive Containers in Kubernetes

Linux Foundation
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