Parallel and Distributed Processing in the Context of Fog Computing: High Throughput Pattern Matching and Distributed Monitoring

Licentiate thesis, 2018

With the introduction of the Internet of Things (IoT), physical objects now have cyber counterparts that create and communicate data. Extracting valuable information from that data requires timely and accurate processing, which calls for more efficient, distributed approaches. In order to address this challenge, the fog computing approach has been suggested as an extension to cloud processing. Fog builds on the opportunity to distribute computation to a wider range of possible platforms: data processing can happen at high-end servers in the cloud, at intermediate nodes where the data is aggregated, as well as at the resource-constrained devices that produce the data in the first place.

In this work, we focus on efficient utilization of the diverse hardware resources found in the fog and identify and address challenges in computation and communication. To this end, we target two applications that are representative examples of the processing involved across a wide spectrum of computing platforms. First, we address the need for high throughput processing of the increasing network traffic produced by IoT networks. Specifically, we target the processing involved in security applications and develop a new, data parallel algorithm for pattern matching at high rates. We target the vectorization capabilities found in modern, high-end architectures and show how cache locality and data parallelism can achieve up to \textit{three} times higher processing throughput than the state of the art. Second, we focus on the processing involved close to the sources of data. We target the problem of continuously monitoring sensor streams \textemdash a basic building block for many IoT applications.  We show how distributed and communication-efficient monitoring algorithms can fit in real IoT devices and give insights of their behavior in conjunction with the underlying network stack.