Jonathan Johnson

Independent Software Architect

Jonathan is an independent software architect with a concentration on helping others unpack the riches in the cloud native and Kubernetes ecosystems.

Jonathan is halfway into his second score of engineering commercial software, driven by his desire to design helpful software to move us forward. His applications began with laboratory instrument software and managing its data. Jonathan was enticed by the advent of object-oriented design to develop personal banking software. Banking soon turned to the internet, and enterprise applications took off. Java exploded onto the scene, and since then he has inhabited that ecosystem. At 454 Life Sciences and Roche Diagnostics, Jonathan returned to laboratory software and leveraged Java-based state machines and enterprise services to manage the terabytes of data flowing out of DNA sequencing instruments. Then as a hands-on architect at Thermo Fisher Scientific, he applied the advantages of microservices, containers, and Kubernetes to their laboratory management platform.

 

Talks on Wurreka:

Kubernetes out of the box is a strong platform for running and coordinating large collections of services, containers, and applications. As is, Kubernetes is powerful for many solutions.

However, Istio is a supercharger for Kubernetes. Once you see it you will realize what we have been missing.

Istio is an open, platform-independent service mesh that manages communications between services in a transparent way. With a distributed architecture many things can fail, slow down and become less secure. Istio helps to shore up the weaknesses of a distributed platform.

From operating system on bare metal, to virtual machines on hypervisors, to containers orchestration platforms. How we run our code and bundle our applications continues to evolve. Serverless computing continues our evolutionary path for our architectures.

Kubernetes provides an ideal, vendor-agnostic platform for serverless computing. Serverless promises to developers that we can worry less about the cluster and focus more on their logic. Based on your Kubernetes knowledge we will discover the various contributors of serverless frameworks on Kubernetes. Specifically, we will unpack how two open source serverless frameworks, Kubeless and OpenFaaS, leverage Kubernetes to achieve their promises. We will explore how Knative is helping the serverless providers evolve to the next levels sophistication.

Kubernetes has become the de-facto orchestrator for containers and now is the best way to start engaging with portable distributed computing. This workshop is for software application developers who want to understand what Kubernetes is all about and how it works. It can be a seemingly complex ecosystem full of terms, architectures, and misinformation. We will break it down so you have a solid understanding of how it works so you can start writing applications that run on this distributed platform.

We will cover many topics such as:

  • Rise of Kubernetes and containers
  • Terms and architecture
  • What does it give me?
  • App Patterns on Kubernetes
  • Building, deploying, serving, and observing apps on Kubernetes.
  • Hands on labs

Three evolutionary ecosystems work well together Java, Containers, and Kubernetes.

Past versions of Java were never designed to be “container aware”. This has led some to stray away from the JVM and consider other shiny languages. But wait, before you go, let's discover what Java 9, 10, 11, 12, 13 (…) has done to get our applications into efficiently distilled containers that pack nicely into Kubernetes.

Topics covered:

  • Microframeworks
  • Java9+, advancing beyond the troubled past
  • Java runtime to container runtime - a paradigm shift for “run anywhere”
  • Distillation pattern · small containers with a reduced attack vector
  • Multi-stage Dockerfile
  • JLink, JDeps
  • GraalVM
  • Hands on example

Cloud native containers allow smaller teams to embrace polyglot. We can now choose the best languages and tools to solve problems instead of using the same language and tools across our whole organization. Put your big hammers down.

Writing applications in various languages and packaging them into neatly distilled containers can be challenging. Rust has its set of challenges and best practices. In this session we will look at applying the distillation pattern to Rust-based microservices, packaging them in container images, and running them as well-behaved applications on Kubernetes.

Cloud native containers allow smaller teams to embrace polyglot. We can now choose the best languages and tools to solve problems instead of using the same language and tools across our whole organization. Put your big hammers down.

Writing applications in various languages and packaging them into neatly distilled containers can be challenging. Rust has its set of challenges and best practices. In this session we will look at applying the distillation pattern to Rust-based microservices, packaging them in container images, and running them as well-behaved applications on Kubernetes.

A presentation on the terms and tools is followed by a hands-on example that will guide you to running Rust-based applications on Kubernetes. Little knowledge of Rust, containers, or Kubernetes is required.

Machine Learning with Distributed Computing are both relatively complex software architectures to wrap your head around. Through the years the solution stack has taken various forms, most of which have been difficult to setup and maintain. Today with the advent of tools like TensorFlow and Kubernetes, we can combine these technologies and stand on the shoulders of giants. Your ML solutions will not just be running, but will also be easier to maintain and observe.

The session will present the fundamentals of how these two work together for a complementary solution stack. We walk through a hand-on demonstration that you can later take and exercise for yourself and show to your peers.

Prerequisite: Be sure to attend Kubernetes Koncepts (at least part 1) as this presentation builds on those ideas.

Getting applications written in various languages into neatly distilled containers can be challenging. Python has its sets of challenges. In this session we will look applying the distillation pattern to Python container images and getting them to be well behaved microservices on Kubernetes.

A presentation for common grounding and a hands-on Katacoda to explain the steps. Little knowledge of Python, containers, or Kubernetes is required.

With the advent of containers, Kubernetes evolved as the defacto orchestration solution to coordinate hundreds of containers at scale across a datacenter. Kubernetes opens the door for developers to access the benefits of distributed computing. As compute capacity increases relative to price we have an explosion of Machine Learning applications moving to Kubernetes. Does anybody remember “Wonder Twin powers, activate!”

You will learn how Kubernetes offers to Machine Learning an ideal orchestration tool for hosting your clever applications. We will look at common practices, containers, and deployment architectures that are common for cloud native Machine Learning. Kubeflow is one of the dominating solutions, but there are others.

  • Architectural understanding of Kubernetes from a developer perspective
  • Deploying and monitoring application on Kubernetes
  • Understanding the architecture of Kubeflow

Hands-on exercises:

  • Best practices for containerizing Python applications
  • Scaling stateful Python applications
  • Using Kubeflow
  • Leverage ML performant hardware
  • Writing, Deploying, Running and Monitoring some Machine Learning solutions

Prerequisites:

  • Just a laptop with wi-fi and a browser
  • Machine learning fundamentals
  • Basic knowledge of Python and writing containers

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