Web services are typically stateless entities, that need to operate at scale at large. Functional paradigm can be used to model these web services work and offer several benefits like scalability, productivity, and correctness. This talk describes how to implement Event-Driven Microservices in functional programming languages with examples in F#.

Immutability allows infinite scalability as it eliminates the need to worry about a mutex, a lock, or a race. As functional code is much more terse compared to object-oriented code, it provides productivity benefits. Its strict typing makes writing correct code easy as mismatches of types are caught at compile time.

The objective of the talk is to show how to create a scalable & highly distributed web service in F#, and demonstrate how various characteristics of functional paradigm captures the behavior of such services architecture very naturally.

This workshop covers Knative, Kubernetes-based open-source platform to deploy and manage modern serverless workloads. It provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on-premises, in the cloud, or even in a third-party data center. Knative components are built on Kubernetes and codify the best practices shared by successful real-world Kubernetes-based frameworks.

Each of the components under the Knative project attempts to identify common patterns and codify the best practices shared by successful real-world Kubernetes-based frameworks and applications. Knative components focus on solving many mundane but difficult tasks such as deploying a container, orchestrating source-to-URL workflows on Kubernetes, routing and managing traffic with blue/green deployment, automatic scaling and sizing workloads based on demand, and binding running services to eventing ecosystems.

The workshop goes into details of how Knative enables you to focus just on writing interesting code without worrying about the boring but difficult parts of building, deploying, and managing an application. It shows how developers can even use familiar idioms, languages, and frameworks to deploy any workload: functions, applications, or containers. We also touch upon Cloud Run (a managed Knative offering) and software architecture patterns for modern serverless applications.

DateTime: This workshop is scheduled on Oct 17th from 9 AM to 1 PM IST and 18th from 10 AM to 12 PM

This introduces Knative & Cloud Run and shows how they can be used to run modern serverless workloads. Knative is a reference API & implementation and Cloud Run is a product built on the Knative specification.

Knative is a Kubernetes-based platform to build, deploy, and manage modern serverless workloads. It provides a set of middleware components that are essential to build modern, source-centric, and container-based applications. Knative components are built on Kubernetes and codify the best practices shared by successful real-world Kubernetes-based frameworks.

Cloud Run is a managed compute platform that is built upon Knative that automatically scales your stateless containers. Cloud Run is serverless: it abstracts away all infrastructure management. It is compatible with Knative, letting you choose to easily run your containers either fully managed with Cloud Run, or in your Google Kubernetes Engine cluster with Cloud Run on GKE.

F# is a relatively new primarily Functional programming language for the .NET platform. It is a statically typed managed functional language that is fully inter-operable with other .NET languages like C#, Visual Basic.NET etc. It builds on the power of Functional Paradigm and combines it with .NET Object-Oriented model enabling the developer to use the best approach for a given problem.

This workshop introduces Functional Programming in F# from ground up. No prior experience in Functional Programming or .NET is needed, familiarity with a mainstream programming language like C++/Java/C# should be enough.

Functional programming (FP) offers several benefits. The code tends to be terse which leads to enhanced developer productivity. FP encourages pure functions which are much easier to reason about and debug, as well as eliminates large class of bugs due to side effect free programming. Moreover, immutability leads to easy parallelization of the code. Algebraic Data Types can be used to express domain object conveniently and control state space explosion.

F# is great practical choice for developing reliable and highly scalable real-world system that are quick and easy to develop due to the design of the language itself combined with the ability of the language to use a large no. of 3rd party libraries designed for the .NET platform.

Unfortunately, support for multiple paradigms often leads to confusion. Newcomers tend to find the transition from object-oriented world to functional world difficult. Moreover, it often leads to abuse where developer tries to use the same old imperative style of coding in a functional language and is unable to take advantage of the features, the language has to offer.

Serverless computing is a cloud computing execution model in which the cloud provider dynamically manages the allocation of machine resources. Typically, applications consist of stateless custom code that's run in ephemeral containers (Function as a Service or "FaaS"), which be spun up on demand. As a result of it, serverless applications tend to be highly scalable.

The objective of the talk is to show how to build and deploy elastic-scale applications serverless applications. The code examples for the talk are in C# and using Azure Functions.

Azure Functions is Serverless offering by Microsoft and is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in Azure or third-party service as well as on-premises systems.

Modern software-based services are implemented as large scale, highly distributed systems running in cloud or data centers. Disruptive real-world events like hardware failures or software bugs can create turbulent conditions in the environments where these systems and can lead to unpredictable outcomes. Chaos Engineering is a study of system’s ability to withstand such disruptive turbulent conditions. It works by purposefully injecting failure into the production environment that mirrors the actual failure modes and monitors the recovery.

Chaos engineering uses experimentation to study effects of such disruptions. These experiments typically start by defining “steady state” of the system and come up with metrics that can be used to measure this steady state. Then various events that mirror the failure modes (aka “Chaos”) that are possible in our production environment (e.g. server crash), are injected systematically in the system in controlled environment.

Effect of the injected “Chaos” is observed by collecting and analyzing the metrics identified above. If the system is able to recover successfully, this builds confidence in system’s ability to handle an actual unplanned outage.

If a failure to recover is observed, then it becomes a target for improvement before that behavior manifests in the system at large. By continuing to run these simulations, it is possible to identify several such vulnerabilities. Fixing these vulnerabilities strengthens the system over a period of time. Extensive monitoring and logging is essential for the success of Chaos Engineering in its goal to improve the resiliency of the system.

Unikernels are specialized, single address space machine image constructed by using library operating systems and are gaining a lot of attention in the industry. Due to less amount of code deployed, they have smaller attack surface and hence improved security.

This talk describes what Unikernels are, what are their advantages & disadvantages and how are they different from containers & microkernels. It uses MirageOS as an example to show how the developer selects the minimal set of libraries which correspond to the OS constructs required for their application to run and these libraries are then compiled with the application to build sealed, fixed-purpose images which run directly on a hypervisor or hardware without an intervening OS.