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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...

Context: The combination of distributed stream processing with microservice architectures is an emerging pattern for building data-intensive software systems. In such systems, stream processing frameworks such as Apache Flink, Apache Kafka Streams, Apache Samza, Hazelcast Jet, or the Apache Beam SDK are used inside microservices to continuously ...

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...

This work introduces ExaLogLog, a new data structure for approximate distinct counting, which has the same practical properties as the popular HyperLogLog algorithm. It is commutative, idempotent, mergeable, reducible, has a constant-time insert operation, and supports distinct counts up to the exa-scale. At the same time, as theoretically derived ...

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...

The distribution of keys to a given number of buckets is a fundamental task in distributed data processing and storage. A simple, fast, and therefore popular approach is to map the hash values of keys to buckets based on the remainder after dividing by the number of buckets. Unfortunately, these mappings are not stable when the number of buckets ch...

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...

In network monitoring applications, it is often beneficial to employ a fast approximate set-membership filter to check if a given packet belongs to a monitored flow. Recent adaptive filter designs, such as the Adaptive Cuckoo Filter, are especially promising for such use cases as they adapt fingerprints to eliminate recurring false positives. In ma...