Httpd & Microservices Track

10:30 - 11:15, Not Your Daddy's Web Server

Jim Jagielski, Co-Founder, Apache Software Foundation

Although 20 years old, the Apache HTTP server is not your Daddy's web server. It includes functionality, performance and capability you may not know about. In this session you'll find out all about Apache httpd 2.4!.

11:25 - 12:10, Apache Web Server 2.4 - 10 Must-know Configuration Features

Rainer Jung, Co-Founder, Kippdata informationstechnologie GmbH

The Apache web server 2.4 provides many very useful configuration features that most admins still do not know well enough. Examples are the powerful configuration expression language that allows dynamic configuration, a macro language that makes it possible to reduce configuration repetition in big configurations and very fine grained control of logging. The talk shows the most important such features based on real-world configuration examples.

12:20 - 13:05, Securing Communications with Your Apache HTTP Server

Lars Eilebrecht, Co-Founder, Apache Software Foundation

This talk will introduce you to the fundamentals of securing communications of your Apache HTTP Server with HTTPS. We will start by explaining the basics of X.509 server and client certificates, certification authorities, and using the OpenSSL toolkit. The TLS/SSL protocol will be introduced and how it is used together with HTTP in order to provide for data encryption, integrity, and authentication. The basic configuration of the Apache HTTP Server will be explained. We will walk you through some standard use cases, common pitfalls, known SSL vulnerabilities, and issues when using HTTPS.

14:20 -15:05, Serverless Microservices are the New Black

Lorna Jane Mitchell, Developer Advocate, IBM Watson Data Platform

Microservices are a great way to build modern, event-driven applications. Come to this session and find out how serverless technology can serve as a solid basis of a microservice. With scalability baked in and a shallow learning curve for developers, these modern platforms are an excellent fit for all your API needs including microservices. Come and learn how to adapt your existing knowledge to build excellent microservices from Serverless platforms such as Apache OpenWhisk and allied technologies like API Gateway.

This session is recommended for developers of all levels.

15:15 - 16:00, The Cool and the Cruel of MicroService

Mark Struberg, Lifelong Learner & Software Architect

Over the last few years everyone has been raving about MicroServices and how they will make any developers life so much better. Did this promise deliver? What was the reason we invented MicroService in the first place? Where are the barriers and for which scenarios does it pay off to use MicroServices? And in which situations do you better resist the temptation to always use the latest and greatest new hyped tools like MicroServices? Let's find out!

In this talk I'll also like to share some feedback from various big real-world projects where this approach did sometimes work - and sometimes miserably failed.

16:30 - 17:10, Apache Ignite Service Grid: Backbone of Your Microservices-based Solution

Christos Erotocritou, Director, Solutions Architecture at GridGain Systems

Whether it is a Microservices-based solution that is used under high load and processes rapidly growing volumes of data, or an application that does not use Microservices, both usually face the same issues:

- Data are still stored in disk-backed databases that can no longer keep pace with the growing volumes of data that have to be stored and processed in parallel. As a result, conventional databases are becoming a performance bottleneck affecting the overall solution or application.

- In the past, a solution's high-availability guarantee was a nice-to-have feature. Today, high-availability of an application has become a de-facto requirement.

17:20 - 18:00, Distributed Inference using Apache MXNet and Apache Spark

Naveen Swamy, Software Developer, AWS

Deep Learning has become ubiquitous with abundance of data, commoditization of compute and storage. Pre-trained models are readily available for many use-cases. Distributed Inference has many applications such as pre-computing results offline, backfilling historic data with predictions from state-of-the-art models, etc.,. Inference on large scale datasets comes with many challenges prevalent in distributed data processing.

Attendees will learn how to efficiently run deep learning prediction on large data sets, leveraging Apache Spark and Apache MXNet (incubating). Outline: Basic Deep Learning Concepts. Apache MXNet(Incubating) Deep Learning Framework. Apache Spark Framework Distributed Inference using Apache MXNet and Apache Spark on Amazon EMR.