A recurrent challenge in real‐world applications is autonomous management of the
executions at run‐time. In this vein, stream processing is a class of applications that compute …

Health data science systems are becoming key for supporting healthcare decision-making
processes. However, these systems should achieve continuous data processing and adapt …

In this work, we propose a multi-tier architectural model to separate functionality and security
concerns for distributed cyber-physical systems. On the line of distributed computing, such …

This paper addresses the problem of auto-parallelization of streaming applications. We
propose an online parallelization optimization algorithm that adjusts the degree of pipeline …

We present the scalable, elastic operator scheduler in IBM Streams 4.2. Streams is a
distributed stream processing system used in production at many companies in a wide …

Atualmente, uma parte significativa dos sistemas computacionais e aplicações do mundo
real demandam paralelismo para acelerar suas execuções. Embora a programação …

The efficient mapping of stream processing applications to parallel hardware architectures is
a difficult problem. While parallelization is often highly desirable as it reduces the overall …

Ongoing efforts exist to exploit cloud elasticity for processing efficiently data streams
generated by a variety of data sources. To contribute to these efforts an overview of existing …

Distributed applications have been developed using thread pool system (TPS) in order to
improve system performance. The dynamic optimization and overload management of TPS …

Sudden surges in the incoming workload can cause adverse consequences on the run-time
performance of data-flow applications. Our work addresses the problem of limiting CPU …