The ever–growing expansion of smart devices and sensors increases the amount of data flows that have to be processed in real–time. This extends to a wide spectrum of applications with high socio–economic impact, like systems for healthcare, emergency management, surveillance, intelligent transportation and many others.<br>Data Stream Processing systems (DSPs) usually get in input high–volume of data at high frequency, and process the application queries by respecting strict performance requirements in terms of throughput and response time. The maintenance of these constraints is often fundamental despite an unplanned or unexpected workload variability or changes due to the dynamism of the execution environment.<br>High–volume data streams can be efficiently handled through the adoption of novel high–performance solutions targeting today’s highly parallel hardware. This comprises multicore–based platforms and heterogeneous systems equipped with GPU and FPGA co–processors, aggregated at rack level by low–latency/high–bandwidth networks. The capacity of these highly–dense/highly–parallel rack–scale solutions has grown remarkably over the years, offering tens of thousands of heterogeneous cores and multiple terabytes of aggregated RAM reaching computing, memory and storage capacity of a large warehouse–scale cluster of just few years ago.<br>However, despite this large computing power, high–performance data streaming solutions need to be equipped with flexible and autonomic logics in order to adapt the framework/application configuration to rapidly changing execution conditions and workloads. This turns out in mechanisms and strategies to adapt the queries and operator placement policies, intra–operator parallelism degree, scheduling strategies, load shedding rate and so forth, and fosters novel interdisciplinary approaches that exploit Control Theory and Artificial Intelligence methods.<br>The Auto–DaSP workshop is willing to attract contributions in the area of Data Stream Processing with particular emphasis on supports for highly parallel platforms and autonomic features to deal with variable workloads. A partial list of interesting topics of this workshop is the following:<br>◾Parallel models for streaming applications<br>◾Parallel sliding–window query processing<br>◾Streaming parallel patterns<br>◾Autonomic intra–operator parallel solutions<br>◾Strategies for dynamic operator and query placement<br>◾Elastic techniques to cope with burstiness and workload variations<br>◾Integration of elasticity support in stream processing frameworks<br>◾Stream processing on heterogeneous and reconfigurable hardware<br>◾Stream scheduling strategies and load balancing<br>◾Adaptive load shedding techniques<br>◾Techniques to deal with out–of–order data streams<br>◾Power– and energy–aware management of parallel stream processing systems<br>◾Applications and use cases in various domains including Smart Cities, Internet of Things, Finance, Social Media, and Healthcare<br><div><br></div>
Abbrevation
Auto-DaSP
City
Göttingen
Country
Germany
Deadline Paper
Start Date
End Date
Abstract