Autonomic parallel Data Stream Processing

Data Stream Processing (DaSP) is a recent and highly active research field, applied in various real world scenarios. Differently than traditional applications, input data is seen as transient continuous streams that must be processed “on the fly”, with critical requirements on throughput, latency and memory occupancy. A parallel solution is often advocated, but the problem of designing and implementing high throughput and low latency DaSP applications is complex per se and because of the presence of multiple streams characterized by high volume, high velocity and high variability. Moreover, parallel DaSP applications must be able to adapt themselves to data dynamics in order to satisfy desired QoS levels. The aim of our work is to study these problems in an integrated way, providing to the programmers a methodological framework for the parallelization of DaSP applications.