Practical Container Scheduling: Optimizations, Guarantees, and Trade-Offs at Netflix - Lecture
Offered By: Linux Foundation via YouTube
Course Description
Overview
Syllabus
Intro
Reactive stream processing: Mantis
Container deployment: Titus
What the cluster needs to support - Heterogeneous mix of workload
Why juggle at all?
Scheduling challenge in large clusters
Our initial goals for a cluster scheduler • Multi goal optimization for task placement . Cluster autoscaling • Extensibility
Multi goal task placement
Security
Capacity guarantees
Fenzo scheduling strategy
Fitness functions we use • CPU, memory, and network in packing
Hard constraints we use • GPU server matching
Soft constraints we use • Specified by individual jobs at submittime • Balance tasks of a job across availability zones
Mixing fitness with soft constraints
Our queues setup
Sizing agent clusters for capacity
Reasoning about allocation failures
What's next?
Questions?
Taught by
Linux Foundation
Tags
Related Courses
Deep Learning for Natural Language ProcessingUniversity of Oxford via Independent Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
DeepLearning.AI via Coursera Deep Learning Part 1 (IITM)
Indian Institute of Technology Madras via Swayam Deep Learning - Part 1
Indian Institute of Technology, Ropar via Swayam Logistic Regression with Python and Numpy
Coursera Project Network via Coursera