Context-Aware Computing e Tecnologie Wearable

Quest' ultimo ventennio ha visto una vera e propria rivoluzione dei dispositivi, partendo dal computer desktop, passando ai laptop fino ad arrivare agli smartphone. Oggi giorno invece si parla di computer indossabili, i dispositivi stanno diventando sempre più piccoli e integrati in oggetti di moda come possono essere degli orologi, occhiali e orecchini.Questi sono connessi in rete con migliaia di dispositivi e con computer più grandi, con i quali, gli utenti nel corso della giornata interagiscono continuamente senza nemmeno rendersene conto scambiandosi migliaia di piccole informazioni: quando si cammina per strada, in centro città quando si fanno compere, quando si è in casa a guardare la TV. Questo ha portato quindi alla nascita di una nuova tipologia di sistemi, in risposta ai cambiamenti portati da questa rivoluzione, i così detti "Sistemi Context-Aware".Il context di un utente può essere descritto come la relazione che vi è tra i suoi dispositivi elettronici, e l' ambiente che lo circonda, a seconda di dove si trova esso dovrà dare delle risposte opportune, e compiere quindi autonomamente certe azioni, tal volta ad insaputa dell' utente. Le applicazioni che usano quindi questo sistema, vengono continuamente messe a conoscenza dei cambiamenti che vengono apportati all' ambiente circostante, regolandosi e reagendo di conseguenza in autonomia. Ad esempio, il nostro dispositivo scopre tramite la rete, la presenza di un amico nelle vicinanze, mentre stiamo passeggiano per strada, allora potrebbe inviarci un messaggio mostrandoci chi è, e dove si trova, con il tragitto da percorrere per raggiungerlo. Le migliaia di informazioni che vengono quindi scambiate in rete andranno a creare “un ambiente intelligente”, con il quale gli utenti interagiscono inviando informazioni sul proprio conto, senza nemmeno accorgersene, in modo da avere una risposta personalizzata, da parte dell' ambiente.

Towards Energy Consumption Measurement in a Cloud Computing Wireless Testbed

The evolution of the Next Generation Networks, especially the wireless broadband access technologies such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX), have increased the number of "all-IP" networks across the world. The enhanced capabilities of these access networks has spearheaded the cloud computing paradigm, where the end-users aim at having the services accessible anytime and anywhere. The services availability is also related with the end-user device, where one of the major constraints is the battery lifetime. Therefore, it is necessary to assess and minimize the energy consumed by the end-user devices, given its significance for the user perceived quality of the cloud computing services. In this paper, an empirical methodology to measure network interfaces energy consumption is proposed. By employing this methodology, an experimental evaluation of energy consumption in three different cloud computing access scenarios (including WiMAX) were performed. The empirical results obtained show the impact of accurate network interface states management and application network level design in the energy consumption. Additionally, the achieved outcomes can be used in further software-based models to optimized energy consumption, and increase the Quality of Experience (QoE) perceived by the end-users.

Design and Development of an FPGA-based Distributed Computing Processing Platform

This thesis presents two frameworks- a software framework and a hardware core manager framework- which, together, can be used to develop a processing platform using a distributed system of field-programmable gate array (FPGA) boards. The software framework providesusers with the ability to easily develop applications that exploit the processing power of FPGAs while the hardware core manager framework gives users the ability to configure and interact with multiple FPGA boards and/or hardware cores. This thesis describes the design and development of these frameworks and analyzes the performance of a system that was constructed using the frameworks. The performance analysis included measuring the effect of incorporating additional hardware components into the system and comparing the system to a software-only implementation. This work draws conclusions based on the provided results of the performance analysis and offers suggestions for future work.

Memory resource balancing for virtualized computing

Virtualization has become a common abstraction layer in modern data centers. By multiplexing hardware resources into multiple virtual machines (VMs) and thus enabling several operating systems to run on the same physical platform simultaneously, it can effectively reduce power consumption and building size or improve security by isolating VMs. In a virtualized system, memory resource management plays a critical role in achieving high resource utilization and performance. Insufficient memory allocation to a VM will degrade its performance dramatically. On the contrary, over-allocation causes waste of memory resources. Meanwhile, a VM’s memory demand may vary significantly. As a result, effective memory resource management calls for a dynamic memory balancer, which, ideally, can adjust memory allocation in a timely manner for each VM based on their current memory demand and thus achieve the best memory utilization and the optimal overall performance. In order to estimate the memory demand of each VM and to arbitrate possible memory resource contention, a widely proposed approach is to construct an LRU-based miss ratio curve (MRC), which provides not only the current working set size (WSS) but also the correlation between performance and the target memory allocation size. Unfortunately, the cost of constructing an MRC is nontrivial. In this dissertation, we first present a low overhead LRU-based memory demand tracking scheme, which includes three orthogonal optimizations: AVL-based LRU organization, dynamic hot set sizing and intermittent memory tracking. Our evaluation results show that, for the whole SPEC CPU 2006 benchmark suite, after applying the three optimizing techniques, the mean overhead of MRC construction is lowered from 173% to only 2%. Based on current WSS, we then predict its trend in the near future and take different strategies for different prediction results. When there is a sufficient amount of physical memory on the host, it locally balances its memory resource for the VMs. Once the local memory resource is insufficient and the memory pressure is predicted to sustain for a sufficiently long time, a relatively expensive solution, VM live migration, is used to move one or more VMs from the hot host to other host(s). Finally, for transient memory pressure, a remote cache is used to alleviate the temporary performance penalty. Our experimental results show that this design achieves 49% center-wide speedup.