Big data processing systems (eg, Hadoop, Spark, Storm) contain a vast number of
configuration parameters controlling parallelism, I/O behavior, memory settings, and …

Stream processing is an emerging paradigm to handle data streams upon arrival, powering
latency-critical application such as fraud detection, algorithmic trading, and health …

In recent years, there has been an explosion of large-scale real-time analytics needs and a
plethora of streaming systems have been developed to support such applications. These …

When using distributed machine learning (ML) systems to train models on a cluster of worker
machines, users must configure a large number of parameters: hyper-parameters (eg the …

Microservice, an architectural design that decomposes applications into loosely coupled
services, is adopted in modern software design, including cloud-based scientific workflow …

Over the last decade, several interconnected disruptions have happened in the large scale
distributed and parallel computing landscape. The volume of data currently produced by …

Latency-sensitive edge-native applications may be the key to commercial success of edge
infrastructure. However, success in the form of widespread deployment of such applications …

Exploiting on-the-fly computation, Data Stream Processing (DSP) applications are widely
used to process unbounded streams of data and extract valuable information in a near real …

We are currently living in the big data era, in which it has become more necessary than ever
to develop “smart” schedulers. It is common knowledge that the default Storm scheduler, as …

In a stream data analytics system, input data arrive continuously and trigger the processing
and updating of analytics results. We focus on applications with real-time constraints, in …