This article addresses the profitability problem associated with auto-parallelization of
general-purpose distributed data stream processing applications. Auto-parallelization …

In this paper, we propose a framework for adaptive admission control and management of a
large number of dynamic input streams in parallel stream processing engines. The …

Distributed stream processing frameworks are designed to perform continuous computation
on possibly unbounded data streams whose rates can change over time. Devising solutions …

Carefully balancing load in distributed stream processing systems has a fundamental impact
on execution latency and throughput. Load balancing is challenging because real-world …

Elastic scaling allows a data stream processing system to react to a dynamically changing
query or event workload by automatically scaling in or out. Thereby, both unpredictable load …

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

We study the problem of load balancing in distributed stream processing engines, which is
exacerbated in the presence of skew. We introduce Partial Key Grouping (PKG), a new …

Stream Processing (SP) has evolved as the leading paradigm to process and gain value
from the high volume of streaming data produced, eg, in the domain of the Internet of Things …

Data Stream Processing (DSP) applications are widely used to timely extract information
from distributed data sources, such as sensing devices, monitoring stations, and social …

We describe an approach to elastically scale the performance of a data analytics operator
that is part of a streaming application. Our techniques focus on dynamically adjusting the …