Migrating Large-Scale Interactive Compute Workloads to Kubernetes Without Disruption

Table of Contents

1. Introduction
2. Challenges in Migrating Large-Scale Interactive Compute Workloads to Kubernetes
3. Efficient Configurations for Migration
4. Novel Package Monitoring Methods
5. Conclusion

1. Introduction

Uber DSW supports thousands of Jupyter and RStudio notebook sessions, making it essential to migrate these workloads to Kubernetes efficiently without disrupting operations. This paper explores the strategies and techniques used by Uber to achieve this migration seamlessly.

2. Challenges in Migrating Large-Scale Interactive Compute Workloads to Kubernetes

Migrating large-scale interactive compute workloads poses various challenges, such as ensuring minimal downtime, managing dependencies, and preserving user data integrity. Uber faced these challenges and developed solutions to address them effectively.

3. Efficient Configurations for Migration

Uber implemented efficient configurations during the migration process to Kubernetes, optimizing resource allocation, load balancing, and network configurations. These configurations helped in streamlining the migration process and ensuring smooth operations post-migration.

4. Novel Package Monitoring Methods

To monitor package dependencies and ensure compatibility during the migration, Uber developed novel package monitoring methods. These methods involved tracking package versions, detecting conflicts, and resolving issues proactively to prevent disruptions in notebook sessions.

5. Conclusion

Through effective strategies, efficient configurations, and novel package monitoring methods, Uber successfully migrated large-scale interactive compute workloads to Kubernetes without disruption. This case study demonstrates the importance of meticulous planning and innovative solutions in executing complex migrations at scale.