Quality of Service in Distributed Stream Processing for large scale Smart Pervasive Environments

The wide diffusion of cheap, small, and portable sensors integrated in an unprecedented large variety of devices and the availability of almost ubiquitous Internet connectivity make it possible to collect an unprecedented amount of real time information about the environment we live in. These data streams, if properly and timely analyzed, can be exploited to build new intelligent and pervasive services that have the potential of improving people's quality of life in a variety of cross concerning domains such as entertainment, health-care, or energy management. The large heterogeneity of application domains, however, calls for a middleware-level infrastructure that can effectively support their different quality requirements. In this thesis we study the challenges related to the provisioning of differentiated quality-of-service (QoS) during the processing of data streams produced in pervasive environments. We analyze the trade-offs between guaranteed quality, cost, and scalability in streams distribution and processing by surveying existing state-of-the-art solutions and identifying and exploring their weaknesses. We propose an original model for QoS-centric distributed stream processing in data centers and we present Quasit, its prototype implementation offering a scalable and extensible platform that can be used by researchers to implement and validate novel QoS-enforcement mechanisms. To support our study, we also explore an original class of weaker quality guarantees that can reduce costs when application semantics do not require strict quality enforcement. We validate the effectiveness of this idea in a practical use-case scenario that investigates partial fault-tolerance policies in stream processing by performing a large experimental study on the prototype of our novel LAAR dynamic replication technique. Our modeling, prototyping, and experimental work demonstrates that, by providing data distribution and processing middleware with application-level knowledge of the different quality requirements associated to different pervasive data flows, it is possible to improve system scalability while reducing costs.

Automated service provisioning in programmable network infrastructures

Modern networks are undergoing a fast and drastic evolution, with software taking a more predominant role. Virtualization and cloud-like approaches are replacing physical network appliances, reducing the management burden of the operators. Furthermore, networks now expose programmable interfaces for fast and dynamic control over traffic forwarding. This evolution is backed by standard organizations such as ETSI, 3GPP, and IETF. This thesis will describe which are the main trends in this evolution. Then, it will present solutions developed during the three years of Ph.D. to exploit the capabilities these new technologies offer and to study their possible limitations to push further the state-of-the-art. Namely, it will deal with programmable network infrastructure, introducing the concept of Service Function Chaining (SFC) and presenting two possible solutions, one with Openstack and OpenFlow and the other using Segment Routing and IPv6. Then, it will continue with network service provisioning, presenting concepts from Network Function Virtualization (NFV) and Multi-access Edge Computing (MEC). These concepts will be applied to network slicing for mission-critical communications and Industrial IoT (IIoT). Finally, it will deal with network abstraction, with a focus on Intent Based Networking (IBN). To summarize, the thesis will include solutions for data plane programming with evaluation on well-known platforms, performance metrics on virtual resource allocations, novel practical application of network slicing on mission-critical communications, an architectural proposal and its implementation for edge technologies in Industrial IoT scenarios, and a formal definition of intent using a category theory approach.