Distributed data stream processing systems (DSPSs) such as Storm, Flink, and Spark Streaming are now routinely used to process continuous data streams in (near) real-time. However, achieving the low latency and high throughput demanded by today's streaming applications can be a daunting task, especially since the performance of DSPSs highly depends on a large number of system parameters that control load balancing, degree of parallelism, buffer sizes, and various other aspects of system execution. This tutorial offers a comprehensive review of the state-of-the-art automatic performance tuning approaches that have been proposed in recent years. The approaches are organized into five main categories based on their methodologies and features: cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. The categories of approaches will be analyzed in depth and compared to each other, exposing their various strengths and weaknesses. Finally, we will identify several open research problems and challenges related to automatic performance tuning for DSPSs.

Automatic identification system (AIS) data provides a wealth of information regarding vessel traffic and is used for a variety of applications such as collision detection and avoidance, route prediction and optimization, search and rescue operations, etc. However, several challenges exist when working with AIS data including huge volume and velocity (as AIS signals are sent by vessels every few seconds), message duplication, various types of data irregularities, as well as the need for real-time processing and analysis. This paper presents a new framework for collecting, processing, storing, and analyzing AIS data in real time plus a set of algorithms for doing so in an efficient and scalable way. At the same time, a set of intelligent services are provided as building blocks for improving and creating new AIS data driven applications. This framework has been operational for the past few years in Cyprus, and has collected and processed around one billion AIS messages from the Eastern Mediterranean Sea.

Database replication can be employed for scaling transactional workloads while maintaining strong consistency semantics. However, past approaches suffer from various issues such as limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. Hihooi is a new replication-based master-slave middleware system that is able to overcome the aforementioned limitations. The novelty of Hihooi lies in its modern architecture as well as its replication and transaction routing algorithms. In particular, Hihooi replicates all write statements asynchronously and applies them in parallel at the replica nodes, while ensuring replica consistency. At the same time, a fine-grained transaction routing algorithm ensures that all read transactions are load balanced to the replicas consistently. This demonstration will showcase the key functionalities of Hihooi, including (i) practical management of system components and databases (e.g., add a new replica node), (ii) increased scalability compared to state-of-the-art approaches, and (iii) support for elasticity by suspending and resuming database replicas online without service interruption.