presentation
In this talk, we will outline what is a service mesh. A service mesh is a inter communication infrastructure that allows the traffic to be routed by configuring proxy running as a side car to each services. It’s a network for services, not for bytes.
Starting to do chaos engineering can seem like a daunting task if one has never practice that before. In this talk, we will outline what is a service mesh and how does it help us to do chaos engineering. A service mesh is a inter communication infrastructure that allows the traffic to be routed by configuring proxy running as a side car to each services.
Starting to do chaos engineering can seem like a daunting task if one has never practice that before. In this talk, we will outline what is a service mesh and how does it help us to do chaos engineering. A service mesh is a inter communication infrastructure that allows the traffic to be routed by configuring proxy running as a side car to each services.
It’s a network for services, not for bytes.
Starting to do chaos engineering can seem like a daunting task if one has never practice that before. In this talk, we will outline what is a service mesh and how does it help us to do chaos engineering. A service mesh is a inter communication infrastructure that allows the traffic to be routed by configuring proxy running as a side car to each services.
Starting to do chaos engineering can seem like a daunting task if one has never practice that before. In this talk, we will outline what is a service mesh and how does it help us to do chaos engineering. A service mesh is a inter communication infrastructure that allows the traffic to be routed by configuring proxy running as a side car to each services.
Sub title: Automating Chaos Engineering with a Service Mesh and the Chaos Toolkit.
In this talk, Julien and Sylvain will take you on the journey of performing Chaos Engineering exploration and automation of a simple set of services backed by Istio, a service mesh provider.
The two main workflows of machine learning are, first, train the model, then deploy the model. The time it takes to go from a Jupyter notebook to a deployed model in production can be months.The tooling around the training workflow is getting better but deploying is still cumbersome.That is why Kubeflow was created.The KubeFlow project is dedicated to making deployments of machine learning workflows on Kubernetes simple, portable and scalable. In this presentation, we are going to see why such a project exists and the challenges Machine Learning Operations (MLOps) brings to the table.
The journey from startup to international company is not so straight forward. In this talk, we will go through some of the most common pitfalls I’ve seen when scaling infrastructure and companies.There is no one size fit all, understanding the tradeoffs is important. This is what this talk is about: tradeoffs.We are going to peel down many layers of abstraction and estimate the total cost of ownership for the various services offered by GCP. Should we use kubernetes ? How to scale an application? How to scale a team? What tools helps you to scale? Is a microservices architecture good to start with?Those questions do not have a straight answer but we are going to see in which case it makes sense to use a particular technology.
Automating Chaos Engineering with a service mesh PLAN from code to service (mesh) chaos engineering automation with chaostoolkit Sylvain ( ChaosIQ ) https://chaostoolkit.org/
Julien Bisconti SRE / Data Engineer contact g.dev/julien slides: bisconti.cloud How long from monolith to microservices ? 8 fallacies of distributed computing The network is reliable. Latency is zero. Bandwidth is infinite. The network is secure. Topology doesn't change. There is one administrator. Transport cost is zero. The network is homogeneous.
CHAOS ENGINEERING with SERVICE MESH Julien Bisconti SRE / Data Engineer contact g.dev/julien slides: bisconti.cloud Outline Genesis Service mesh: architecture and features Demo of Envoy and Istio Chaos Engineering: concepts & origin Demo of fault-injection Q&A at the beginning there was an APP and the app was code
that needed to scale
👉 microservices
Deployment Containers: lightweight VMs 12 factor app easier deploy reproducible build but ... Deployment concerns Scaling up and down Redundancy Scheduling / Orchestration Service Discovery Resiliency Rolling out and back Health checks Secret and config ➡️ kubernetes but .