Decentralized Self-adaptation in Large-scale Distributed Systems

The evolution of technology is leading to a world where computational systems are made of a huge number of components spread over a logical network: these components operate in a highly dynamic and unpredictable environment, joining or leaving the system and creating connections between them at runtime. This scenario poses new challenges to software engineers that have to design and implement such complex systems. We want to address this problem, designing and developing an infrastructure, GRU, that uses self-adaptive decentralized techniques to manage large-scale distributed systems. GRU will help developers to focus on the functional part of their application instead of the needed self-adaptive infrastructure. We aim to evaluate our project with concrete case studies, providing evidence on the validity of our approach, and with the feedback provided by developers that will test our system. We believe this approach can contribute to fill the gap between the theoretical study of self-adaptive systems and their application in a production context

Combining Cloud and sensors in a smart city environment

International audienceIn the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones, sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume (data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or "things" in the Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a "Cloud of Things". In the Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences in the "appliances" being sensed, smart sensors are very heterogeneous in terms of communication technologies, sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive infrastructure where new generation services interact with the surrounding environment, thus creating new opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT. Smart cities are assumed as the reference scenario

Self-Aggregation Algorithms for Autonomic Systems.

Abstract One of the today issues in software engineering is to find new effective ways to deal intelligently with the increasing complexity of distributed computing systems. In particular, one of the aspects under study in the field of autonomic computing concerns the way such systems can autonomously reach a configuration that allows the entire system to work in a more efficient and effective way. In this paper we investigate how it is possible to obtain selfaggregation of distributed components. We have used existing self-aggregation algorithms as a starting point, and, after an analysis phase, we have discovered some aspects that could be improved. Finally we have derived new algorithms that showed improved self-aggregating performances in most of the situations. This work has been done in cooperation with Prof. Elisabetta Di Nitto and Prof. Raffaela Mirandola in the context of the CASCADAS European project

Efficient Learning Machines

Computer scienc