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 …

User-facing latency-sensitive web services include numerous distributed,
intercommunicating microservices that promise to simplify software development and …

Function-as-a-Service (FaaS) platforms and" serverless" cloud computing are becoming
increasingly popular. Current FaaS offerings are targeted at stateless functions that do …

Serving ML prediction pipelines spanning multiple models and hardware accelerators is a
key challenge in production machine learning. Optimally configuring these pipelines to meet …

Cloud service providers conservatively provision excessive resources to ensure service
level objectives (SLOs) are met. They often set lower CPU utilization targets to ensure …

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 …

Training machine learning models requires feeding input data for models to ingest. Input
pipelines for machine learning jobs are often challenging to implement efficiently as they …

Stream processing has been an active research field for more than 20 years, but it is now
witnessing its prime time due to recent successful efforts by the research community and …

Microservices architecture have been widely adopted in designing distributed cloud
applications where the application is decoupled into multiple small components (ie" …

Scale-out stream processing engines (SPEs) are powering large big data applications on
high velocity data streams. Industrial setups require SPEs to sustain outages, varying data …