Adriano Vogel

Nowadays, several software systems rely on stream processing architectures to deliver scalable performance and handle large volumes of data in near real time. Stream processing frameworks facilitate scalable computing by distributing the application's execution across multiple machines. Despite performance being extensively studied, the measurement...

Parallel computing is very important to accelerate the performance of computing applications. Moreover, parallel applications are expected to continue executing in more dynamic environments and react to changing conditions. In this context, applying self-adaptation is a potential solution to achieve a higher level of autonomic abstractions and runt...

Parallel computing is very important to accelerate the performance of software systems. Additionally, considering that a recurring challenge is to process high data volumes continuously, stream processing emerged as a paradigm and software architectural style. Several software systems rely on stream processing to deliver scalable performance, where...

Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. This paper introduces ShuffleBench, a novel benchmark to evaluate the performance of modern stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where s...

Several software systems are built upon stream processing architectures to process large amounts of data in near real-time. Today's distributed stream processing systems (DSPSs) spread the processing among multiple machines to provide scalable performance. However, high-performance and Quality of Service (QoS) in distributed stream processing are c...

Distributed stream processing frameworks help building scalable and reliable applications that perform transformations and aggregations on continuous data streams. We outline our ongoing research on designing a new benchmark for distributed stream processing frameworks. In contrast to other benchmarks, it focuses on use cases where stream processin...

Self-adaptation is a potential alternative to provide a higher level of autonomic abstractions and run-time responsiveness in parallel executions. However, the recurrent problem is that self-adaptation is still limited in flexibility and efficiency. For instance, there is a lack of mechanisms to apply adaptation actions and efficient decision-makin...