Conference:  KubeCon + CloudNativeCon Europe 2023

Authors: Nicole van der Hoeven

2023-04-19

The presentation discusses emergence and its application in load testing. It also explores the concept of generative code and its potential benefits. The speaker emphasizes the importance of diverse and quality input in training AI for emergence.

• Emergence is the evolution of the whole beyond its parts in unexpected ways
• Generative code sets the when, not just the what or how
• Imperative testing is still prevalent, but there is potential for declarative testing
• To encourage emergence in load testing, define affordances, assign roles, rate tests, and allow imperfect evolution
• Quality and diverse input is crucial in training AI for emergence

Conference:  KubeCon + CloudNativeCon North America 2022

Authors: Srinivasan Parthasarathy, Shubham Chaudhary

2022-10-27

You have a principled process for releasing your Kubernetes app that involves load testing, benchmarking and validation of service-level objectives (SLOs). But, will your app perform well when your cluster is subject to compute, memory, i/o, or network stress? In this talk, we will explore a novel approach that combines chaos injection for probing weaknesses in your Kubernetes infrastructure, with load testing, benchmarking and performance validation with SLOs for your app. The core thrust of our approach will be flexibility combined with simplicity. Your app may be cluster-local or externally exposed, may implement an HTTP or a gRPC endpoint, may have been specified using built-in or custom Kubernetes resources, may use any type of horizontal or vertical autoscaling, may use any CD/GitOps process for deployment, and you may be interested in probing your cluster by injecting compute, memory, i/o, network, or any other types of chaos. Regardless of these variations, this talk will demonstrate a dead simple way to automatically launch the unified “chaos + performance validation" experiment whenever the app is updated, and automatically notify an event receiver with metrics and SLO validation results once the experiment is completed.