|M.Sc Student||Yankovitch Maor|
|Subject||Hypersonic: A Hybrid Parallelization Approach for Scalable|
Complex Event Processing
|Department||Department of Computer Science||Supervisor||PROF. Assaf Schuster|
The ability to promptly and efficiently detect arbitrarily complex patterns in massive real-time data streams is a crucial requirement in many modern applications. The ever-growing scale of these applications and the sophistication of the patterns involved make it imperative to employ advanced solutions that can optimize pattern detection.
An effective way to achieve the
above goal is to apply complex event processing (CEP) systems. These systems
all detect the pattern using an algorithm that incrementally performs the
detection but obviously differ in the many possible optimization avenues to do
One of the most prominent and well-established optimization routes is to apply CEP in a parallel manner, using a multi-core and/or a distributed environment. However, the inherent tightly coupled nature of CEP severely limits the scalability of the parallelization methods currently available.
In this thesis, we introduce a
novel parallelization mechanism for efficient complex event processing over
data streams. This mechanism is based on a hybrid two-tier model combining
multiple layers of parallelism. By employing a fine-grained load balancing
model, this multi-layered approach leads to a substantial increase in event
detection throughput, while at the same time reducing the latency and the
An extensive experimental evaluation on multiple real-life datasets shows that our approach consistently outperforms state-of-the-art CEP parallelization methods by a factor of two to three orders of magnitude.