Intelligent system optimizations

This talk introduces engineering and machine learning techniques for achieving optimized performance, resilience, availability, cost or other attributes of a large scale distributed system. Firstly, the presentation introduces the topic by discussing the complexities of large scale production system properties as well as important optimization techniques used in state of the art distributed systems, microservice architectures, databases and stream and fast data processing frameworks such as Akka Streams, Spark or Flink. As the next step, a framework using machine learning and artificial intelligence approaches - specifically supervised, reinforcement and meta deep learning - is introduced as a tool for optimization, continuous evolution, learning, and improvement of the specific system and in the specific runtime environment and conditions. The discussion includes details of the problems, data, techniques, code in Keras or TensorFlow, examples and experience with some other modern machine learning and data processing tools. The attendees will gain an understanding of optimization approaches, including some novel machine learning techniques, and ultimately will be able to apply some of the techniques to optimize their own system, whether general distributed systems written in Scala or any other language, systems based on Akka or any of the aforementioned technologies.