Autonomicity is a golden feature when dealing with a high level of complexity. This complexity can be tackled partitioning huge systems in small autonomous modules, i.e., agents. Each agent then needs to be capable of extracting knowledge from its environment and to learn from it, in order to fulfill its goals: this could not be achieved without proper modeling techniques that allow each agent to gaze beyond its sensors. Unfortunately, the simplicity of agents and the complexity of modeling do not fit together, thus demanding for a third party to bridge the gap. Given the opportunities in the field, the main contributions of this work are twofold: (1) we propose a general methodology to model resource consumption trends and (2) we implemented it into MARC, a Cloud-service platform that produces Models-as-a-Service, thus relieving self-aware agents from the burden of building their custom modeling framework. In order to validate the proposed methodology, we set up a custom simulator to generate a wide spectrum of controlled traces: this allowed us to verify the correctness of our framework from a general and comprehensive point of view.

Multi-tenant virtualized infrastructures allow cloud providers to minimize costs through workload consolidation. One of the largest costs is power consumption, which is challenging to understand in heterogeneous environments. We propose a power modeling methodology that tackles this complexity using a divide-and-conquer approach. Our results outperform previous research work, achieving a relative error of 2% on average and under 4% in almost all cases. Models are portable across similar architectures, enabling predictions of power consumption before migrating a tenant to a different hardware platform. Moreover, we show the models allow us to evaluate colocations of tenants to reduce overall consumption.

In the context of mobile devices, the thermal problem is an emerging one, as it affects the user experience and involves factors that are both internal and external with respect to the device. In this paper, we present an evaluation of these factors, that consists of two parts. The first one is the analysis of thermal interactions between the internal components of the system, performed with an infrared camera. The second part consists in the analysis of the impact of external temperature on the performance of CPUs and batteries. We finally propose the VirtIRCamera app, a thermal simulator for Android devices, able to generate thermal maps relying on the thermal model proposed within this paper. As a characterization of the thermal phenomenon, this work is the first step in the creation of thermal management techniques that are specifically designed for mobile devices.

In the last decade we have witnessed the birth and dramatic growth of mobile devices, from cellular-to smart-phones. Despite the huge amount of information achievable from an always-connected reality, researchers that work in the mobile devices field fight against the impossibility to explore, inspect and test their work on such a vast set of possible environments, use case scenarios, hardware and software platforms the smart mobile world is composed of. This pushed the need of a wide open dataset of real world data coming from devices in their real usage context, properly anonymized and conveniently organized to be searchable and accessible. In this paper, we present a platform that brings such a dataset to researchers of the next generation of mobile devices.

In nowadays life, mobile phones are becoming a cheaper and smaller alternative to laptops for simple, everyday tasks. They experienced an astonishing growth in functionalities and, because of their constant presence in our life, mobile phones became fundamental for the interaction with information coming from the environment. Nevertheless, their resources are limited, both in terms of performance and power, and their availability can greatly vary over time. Especially when dealing with power consumption, mobile devices cannot disregard environment conditions and user habits. Both internal and external conditions are rapidly changing and may influence the response of the entire system, e.g., switching between network types may causes an unpredictable power consumption. In order to puzzle out all these issues, we regard the definition of a power/energy model for mobile devices as a first mandatory step. In literature, several attempts to do so are present, basing their approaches on techniques coming from different computer science fields. They differ in the way they consider hardware components, in the operating system they are suitable for and in the scope of their tests and experiments. Within this paper, we categorize techniques presented in the major works in the field, in order to be able to compare different methods, highlight open issues and give suggestions on future works.

Mobile devices take an important part in everyday life. They are now cheaper and widespread, but still a lot of time is spent by the users to configure them: users adapt to their own device, not vice versa. Can our smart phones do something smarter? In this work, we propose a framework to support the development of context aware applications for Android devices: the goal of such applications is to reduce as much as possible the interaction with the user, making use of automatic and intelligent components. Moreover, these components should consume as less power and computational resources as possible, being them part of a mobile ecosystem whose battery and hardware are highly constrained. The work implies the study of a methodology that fits the Android framework and the design of a highly extensible software architecture. An open source framework based on the proposed methodology is then described. Some use cases are finally presented, analyzing the performances and the limitations of the proposed methodology.