Stream processing is a critical technique to process huge amount of data in real-time manner.
Cloud computing has been used for stream processing due to its unlimited computation
resources. At the same time, we are entering the era of Internet of Everything (IoE). The emerging
edge computing benefits low-latency applications by leveraging computation resources at
the proximity of data sources. Billions of sensors and actuators are being deployed worldwide
and huge amount of data generated by things are immersed in our daily life. It has become
essential for organizations to be able to stream and analyze data, and provide low-latency analytics
on streaming data. However, cloud computing is inefficient to process all data in a centralized
environment in terms of the network bandwidth cost and response latency. Although
edge computing offloads computation from the cloud to the edge of the Internet, there is not
a data sharing and processing framework that efficiently utilizes computation resources in the
cloud and the edge. Furthermore, the heterogeneity of edge devices brings more difficulty to the development of collaborative cloud-edge applications.
To explore and attack the challenges of stream processing system in collaborative cloudedge
environment, in this dissertation we design and develop a series of systems to support
stream processing applications in hybrid cloud-edge analytics. Specifically, we develop an
hierarchical and hybrid outlier detection model for multivariate time series streams that automatically
selects the best model for different time series. We optimize one of the stream
processing system (i.e., Spark Streaming) to reduce the end-to-end latency. To facilitate the
development of collaborative cloud-edge applications, we propose and implement a new computing
framework, Firework that allows stakeholders to share and process data by leveraging
both the cloud and the edge. A vision-based cloud-edge application is implemented to demonstrate
the capabilities of Firework. By combining all these studies, we provide comprehensive
system support for stream processing in collaborative cloud-edge environment
