  As more and more devices connect to Internet of Things, unbounded streams of
data will be generated, which have to be processed "on the fly" in order to
trigger automated actions and deliver real-time services. Spark Streaming is a
popular realtime stream processing framework. To make efficient use of Spark
Streaming and achieve stable stream processing, it requires a careful interplay
between different parameter configurations. Mistakes may lead to significant
resource overprovisioning and bad performance. To alleviate such issues, this
paper develops an executable and configurable model named SSP (stands for Spark
Streaming Processing) to model and simulate Spark Streaming. SSP is written in
ABS, which is a formal, executable, and object-oriented language for modeling
distributed systems by means of concurrent object groups. SSP allows users to
rapidly evaluate and compare different parameter configurations without
deploying their applications on a cluster/cloud. The simulation results show
that SSP is able to mimic Spark Streaming in different scenarios.
