CloudNativeCon + KubeCon 2016

Managing thousands containers can be challenging, but if you want to know how Kubernetes will behave at scale we might be able to provide an answer. In this talk, we share the data we collected in our scale lab, which consists of 500 physical nodes. Using virtual machines, we can simulate up to 5000 Kubernetes minions running actual workloads, and our tests are designed to reveal how Kubernetes behaves while managing a complex application (in this case, OpenStack services) at large scale.

After the talk you will understand:
1. How Kubernetes performs rolling-updates, from a time and performance perspective
2. How fast one can roll-out containers on 500 nodes with specific constraints
3. How traffic flows between services, and what networking performance one should expect
4. How a single Service can facade 1000+ Pods with or without Autoscaler, and any limits involved
5. How many Services 1000-5000 Minions Kubernetes can support
6. How long it takes to deploy Pods for a single Service via Autoscaler to handle 1000 workloads
7. How long it takes to deploy Pods for a single Service via RC to handle 1000 workloads

I hate to say it, but there’s a lot of bad advice out there on how to monitor your containers in production. In this talk I hope to add some much-needed clarity on how to best monitor containers to manage the health of your applications.

My advice is built off our experience creating container monitoring solutions, and the information we’ve gotten from working with hundreds of customers deploying Docker. I’ll cover key questions like:

*Why is it so hard to get visibility into Docker containers?
*How far can you get with the Docker stats API?
*What metrics really matter for your containerized applications?
*How does Kubernetes improve monitoring? How does it get in the way?
*What open source tools can help with these challenges?

In addition to this theory, I’ll get into some real examples that will ground our discussion. You’ll walk away with a new appreciation of what it takes to monitor your environment right, as well as a few ideas that you can put into practice right away.

Many organizations run Kubernetes clusters in a single public cloud like GCE or AWS, and as a result have reasonably homogenous infrastructure needs. In these situations deploying Kubernetes clusters is relatively straightforward. Some organizations, however, have diverse infrastructure needs and as a result need to automate infrastructure deployment for Kubernetes across multiple clouds and data centers. This gives rise to a few challenges:

1. How to ensure Kubernetes clusters in different clouds and data centers can communicate with each other, or in some cases even have a single Kubernetes cluster span multiple data centers.
2. How to automate the deployment of multiple Kubernetes clusters.
3. How to incorporate the new Kubernetes multi-zone clusters (f.k.a. Ubernetes) into multi cloud and multi datacenter deployments.
4. How to manage the health of Kubernetes cluster itself, including, for example, how to detect and recover from etcd node problems.
5. How to automate the upgrade of Kubernetes master and Kubelets, and how to handle multiple versions of Kubernetes clusters that exist in a single deployment.

In the last 6 months, we have worked with several enterprise IT organizations to solve these problems. We will share our experience on how to automate and simplify Kubernetes deployments.

Windows Server, and .NET, currently host 30-50% of enterprise workloads in the Global 2000. For Kubernetes to provide a single distributed application fabric to all enterprise workloads, it must have full integration with Windows Server 2016 and Windows Server containers. In KubeCon London 2016 Apprenda announced that it would lead this development effort and teamed up with Red Hat to extend Kubernetes to the Microsoft ecosystem. In this session we will demo Kubernetes on Windows Server 2016 and discuss its features.

In this session, I want to briefly present the current state of cluster federation in Kubernetes mainly focusing on what we aimed to accomplish, where we are today and where we want to go. After that I want to open the floor for discussion. The goal of the session is to discuss about the potential use-cases, challenges that people face while running cross-cluster workloads and the challenges that are hindering Kubernetes Cluster Federation adoption today. I would also like to listen to the feedback from our current users and hear about their experiences.

Summary: Building Kubernetes clusters with large numbers of nodes, high pod start rates, or with needs for high availability? Large, high-performance and reliable kubernetes clusters require engineering the control plane components for demands beyond the defaults. In this talk we will cover the relationship between the various components that make up the kubernetes control plane and how to design and size those components. Where are likely system bottlenecks, and what are approaches for managing those bottlenecks?

Most large enterprise companies have vastly different hurdles to hop over in comparison to small startups and independent tech firms. Bloomberg’s WebOps team is tasked with performing devops tasks at an ever growing scale- see how we are changing our mentality and leveraging the power of Kubernetes to pave the way for the future of web at Bloomberg. We’ll dive into some key choices we made, and take a look at the tools we are using to improve development and deployment speed while improving security and enforcing standards.

A talk about the challenges we faced and the solutions we came up with to utilise kubernetes to deliver an enterprise mission critical system for ourselves and our customers that delivers 100% functional uptime; including high availability, monitoring, multi cloud, multi tenancy, lifecycle management, testing and more.

Learn tips and tricks on how to best configure and tune your container infrastructure for maximum performance and scale. The Performance Engineering Group at Red Hat is responsible for performance of the complete container portfolio, including Docker, RHEL Atomic, Kubernetes and OpenShift. We will share: - Latest Performance Features in OpenShift, Docker and RHEL Atomic, tips and tricks on how to best configure and tune your system for maximum performance and scale - Latest performance and scale test results, using RHEL Atomic, OpenvSwitch, Cockpit multi-server container management - DevOps, Agile approach to Performance Analysis of OpenShift, Kubernetes, Docker and RHEL Atomic - Test harness code and example scripts
Audience

The audience is anyone interested in deploying containers to run performance sensitive workloads, as well as architecting highly scalable distributed systems for hosting those workloads. This includes workloads that require NUMA awareness, direct hardware access and kernel-bypass I/O.

Kubernetes clusters dedicated to a single organization are becoming common, either run by the organizations that use them or hosted by others. Less common is a multi-tenant use of a single cluster.

There are problems to be solved in managing a multi-tenanted Kubernetes cluster in production. At Apigee, we are building a new Kubernetes-based platform that hosts applications for our clients and ourselves on a single, shared cluster.

This talk will cover:
- Securely routing traffic to the correct tenant
- Isolating tenant network environments
- Authenticating and authorizing management API calls using our own and our customers' identity providers and access control policies
- Creating a multi-tenanted build and deploy flow

Now that we have PetSet, let's deploy some databases on it! Version 1.3's PetSet was designed to answer a lot of the needs of people deploying databases and other stateful services on Kubernetes. In this talk, we'll demonstrate how to use PetSet features in order to support deploying both single-master and clustered PostgreSQL. As a bonus, these database applications will be deployed to my portable micro-cluster.

In the past year, Kubernetes proved itself as a powerful tool for creating and managing service based architectures. However, as with anything new, it is easy to find yourself lost when the complexity of a service increases and you enter into uncharted territory. Using our team’s work with Jenkins, Snap, and Cassandra in Kubernetes as a backdrop, this presentation will deep dive into specific pain points and how we solved them. Coupled with code examples and demonstrations, this presentation will show a pattern of how to solve undocumented issues and invite others to the discussion of documenting solutions to similar problems.

Kubernetes 1.3 introduced the PetSet abstraction, which represents a collection of Pods with unique identities represented by an index, along with associated persistent storage and network identities. PetSet makes it easy to run stateful services on Kubernetes, including clustered applications like ZooKeeper and Redis. In this talk we discuss the design and implementation of PetSet, show how to use PetSet to deploy some common off-the-shelf stateful applications, and discuss our future plans for PetSet. We also describe “init containers,” which allow you to set up the user environment of a pod before the main container starts executing. We demonstrate how you can use this feature to configure the relationship between master and peers in a clustered application that is managed by a PetSet, and how new replicas are automatically configured when the application scales up.