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In this paper, we present a vision for a new generation of multimodal streaming systems that embed MLLMs as first-class operators, enabling real-time query processing across multiple modalities. Achieving this is non-trivial: while recent…
With the rapid growth in the number of devices of the Internet of Things (IoT), the volume and types of stream data are rapidly increasing in the real world. Unfortunately, the stream data has the characteristics of infinite and periodic…
Workflows are among the most commonly used tools in a variety of execution environments. Many of them target a specific environment; few of them make it possible to execute an entire workflow in different environments, e.g. Kubernetes and…
Today, we have to deal with many data (Big data) and we need to make decisions by choosing an architectural framework to analyze these data coming from different area. Due to this, it become problematic when we want to process these data,…
Recent advancements in data stream processing frameworks have improved real-time data handling, however, scalability remains a significant challenge affecting throughput and latency. While studies have explored this issue on local machines…
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…
Job schedulers are a key component of scalable computing infrastructures. They orchestrate all of the work executed on the computing infrastructure and directly impact the effectiveness of the system. Recently, job workloads have…
Event processing will play an increasingly important role in constructing enterprise applications that can immediately react to business critical events. Various technologies have been proposed in recent years, such as event processing,…
When a processing unit relies on data from external streams, we may face the problem that the stream data needs to be rearranged in a way that allows the unit to perform its task(s). On arrival of new data, we must decide whether there is…
The paper introduces PDSP-Bench, a novel benchmarking system designed for a systematic understanding of performance of parallel stream processing in a distributed environment. Such an understanding is essential for determining how Stream…
Runtime scheduling and workflow systems are an increasingly popular algorithmic component in HPC because they allow full system utilization with relaxed synchronization requirements. There are so many special-purpose tools for task…
Human beings keep exploring the physical space using information means. Only recently, with the rapid development of information technologies and the increasing accumulation of data, human beings can learn more about the unknown world with…
Edge computing has become a promising computing paradigm for building IoT (Internet of Things) applications, particularly for applications with specific constraints such as latency or privacy requirements. Due to resource constraints at the…
The main goal of parallel processing is to provide users with performance that is much better than that of single processor systems. The execution of jobs is scheduled, which requires certain resources in order to meet certain criteria.…
Stream computing is the use of multiple autonomic and parallel modules together with integrative processors at a higher level of abstraction to embody "intelligent" processing. The biological basis of this computing is sketched and the…
Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for…
Distributed Stream Processing (DSP) focuses on the near real-time processing of large streams of unbounded data. To increase processing capacities, DSP systems are able to dynamically scale across a cluster of commodity nodes, ensuring a…
Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice support for concurrent state access that eases the burden of…
Load balancing, operator instance collocations and horizontal scaling are critical issues in Parallel Stream Processing Engines to achieve low data processing latency, optimized cluster utilization and minimized communication cost…
Stream processing is mainstream (again): Widely-used stream libraries are now available for virtually all modern OO and functional languages, from Java to C# to Scala to OCaml to Haskell. Yet expressivity and performance are still lacking.…