Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby
massive amounts of heterogeneous data are gathered from multiple sources, managed …

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 …

Serverless computing is an excellent fit for big data processing because it can scale quickly
and cheaply to thousands of parallel functions. Existing serverless platforms isolate …

Distributed stream processing systems need to support stateful processing, recover quickly
from failures to resume such processing, and reprocess an entire data stream quickly. We …

Modern Stream Processing Engines (SPEs) process large data volumes under tight latency
constraints. Many SPEs execute processing pipelines using message passing on shared …

The rise in popularity of machine learning, streaming, and latency-sensitive online
applications in shared production clusters has raised new challenges for cluster schedulers …

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 …

Distributed stateful stream processing enables the deployment and execution of large scale
continuous computations in the cloud, targeting both low latency and high throughput. One …

Recent studies have shown a range of co-residency side channels that can be used to
extract private information from cloud clients. Unfortunately, addressing these side channels …

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 …