Noisy neighbor issues are a common challenge for multi-tenant platforms, leading to resource contention, performance degradation, and costly downtime for other tenants sharing the same resources. Observability is the first step towards ensuring platform reliability by detecting noisy neighbors before they cause irreparable damage. In this presentation, I will share our experience and lessons learned with a case study from the Asset Management Platform at Netflix about how we detected and survived a noisy neighbor.
Topics covered in this presentation include:
- Effective observability to detect and address issues before they impact applications.
- Strategies for scaling the platform horizontally and vertically to accommodate growth and changing workloads.
- Fair resource allocation practices to ensure equitable access to resources for all tenants and prevent contention.
While we can't wholly avoid noisy neighbor issues, observability is the key to reducing the
impact and ensuring platform stability and reliability.
Staff Software Engineer @Netflix
Meenakshi Jindal is a seasoned Staff Software Engineer with over 15 years of experience in software design and implementation across multiple industries, including banking, insurance, travel, and media. She specializes in designing high-performance, scalable, and reliable distributed systems that facilitate seamless integrations within the Netflix studio and content ecosystem. Passionate about resolving complex challenges about cloud-distributed systems and NO-SQL databases like Cassandra and Elasticsearch. She holds a Master's in Computer Application and an MS in Information Systems and Management. She is a certified Associate AWS Solution Architect.