Data stream processing (DSP) has emerged over the years as the reference paradigm for
the analysis of continuous and fast information flows, which often have to be processed with …

Context Stream processing is a popular paradigm to continuously process huge amounts of
data. Runtime adaptation plays a significant role in supporting the optimization of data …

Big data analytics in datacenters often involve scheduling of data-parallel jobs. Traditional
scheduling techniques based on improving network resource utilization are subject to …

Data stream management systems (DSMSs) are scalable, highly available, and fault-tolerant
systems that aggregate and analyze real-time data in motion. To continuously perform …

Big data analytics in datacenters often involves scheduling of data-parallel job, which are
bottlenecked by limited bandwidth of datacenter networks. To alleviate the shortage of …

Many computer systems, such as Internet datacenters and cloud computing environments,
consist of a multitude of servers that process user requests. Incoming requests that find all …

Increasingly organizations are elastically scaling their stream processing applications into
the infrastructure as a service clouds. However, state-of-the-art approaches for elastic …

Many computing environments consist of a multitude of servers that process requests that
arrive from a population of customers. Incoming requests that find all servers busy have to …

The development of scalable, representative, and widely adopted benchmarks for graph
data systems have been a question for which answers has been sought for decades. We …

Stream processing is an emerging in-memory computing paradigm that ingests dynamic
data streams with a process-once-arrival strategy. It yields real-time insights by applying …