An Intelligent Framework for Energy-Aware Mobile Computing Subject to Stochastic System Dynamics

abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices.Dissertation/ThesisDoctoral Dissertation Computer Engineering 201

Tracking Visual Scanning Techniques in Training Simulation for Helicopter Landing

Research has shown no consistent findings about how scanning techniques differ between experienced and inexperienced helicopter pilots depending on mission demands. To explore this question, 33 military pilots performed two different landing maneuvers in a flight simulator. The data included scanning data (eye tracking) as well as performance, workload, and a self-assessment of scanning techniques (interviews). Fifty-four percent of scanning-related differences between pilots resulted from the factor combination of expertise and mission demands. A comparison of eye tracking and interview data revealed that pilots were not always clearly aware of their actual scanning techniques. Eye tracking as a feedback tool for pilots offers a new opportunity to substantiate their training as well as research interests within the German Armed Forces

Characterizing Popularity Dynamics of User-generated Videos: A Category-based Study of YouTube

Understanding the growth pattern of content popularity has become a subject of immense interest to Internet service providers, content makers and on-line advertisers. This understanding is also important for the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis of the popularity pattern of YouTube videos is performed, considering the categories of videos. Metadata and request patterns were collected by employing category-specific YouTube crawlers. The request patterns were observed for a period of five months. Results confirm that the time varying popularity of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and Film. While for some categories views at early ages can be used to predict future popularity, for some others predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution technique along with a new video recommendation method

Online Contextual System Tuning with Bayesian Optimization and Workload Forecasting

L'ottimizzazione dei moderni sistemi software può essere estremamente impegnativa: l'elevata quantità di parametri di configurazione e le loro complesse dipendenze rendono estremamente tediosa e dispendiosa la ricerca della configurazione ottimale. Inoltre, la configurazione ottimale dipende dal carico di lavoro a cui è soggetto il sistema. Questa tesi presenta il lavoro svolto per estendere un preesistente ottimizzatore di prestazioni in modo da ottimizzare direttamente il sistema di produzione sfruttando il carico di lavoro reale percepito dal sistema, ovvero mentre sta servendo i suoi clienti. Questo approccio evita la necessità di analizzare e replicare il carico di lavoro su una replica del sistema, ma pone nuove sfide. Per applicare l'ottimizzatore direttamente ai sistemi di produzione sono stati sviluppati due principali moduli: un modulo di previsione del carico di lavoro, basato su tecniche all'avanguardia che riducono al minimo la necessità del lavoro manuale, e un modulo di verifica della stabilità, utilizzato per decidere quando sperimentare nuove configurazioni. Con questi due moduli si riduce la probabilità di esaminare una nuova configurazione possibilmente errata durante un cambiamento nel carico di lavoro, che potenzialmente ridurrebbe la qualità dei servizi del sistema. Inoltre, ottimizzando direttamente il sistema di produzione si riduce la mole di lavoro necessaria per poter applicare l'ottimizzatore su sistemi distinti. La soluzione proposta è stata verificata svolgendo 20 esperimenti di ottimizzazione di due modelli di database, evidenziando che l'integrazione di tecniche di previsione migliora la sicurezza del processo di ottimizzazione mantenendo l'efficacia dell'ottimizzatore originale.Tuning modern software systems can be tremendously challenging: the huge number of configuration parameters and their complex dependencies make the manual research for the optimal configuration tedious and time-consuming. Furthermore, such optimal configuration depends on the workload under which the system is running. This thesis presents the work done to extend an existing performance tuner to be directly applied to a production environment exploiting the real workload perceived by the system, i.e. while it is serving its clients, hence the term Online System Tuning. This approach avoids the necessity of analyzing and replicating the workload on a replica of the system but poses new challenges. To apply the tuner directly to production environments, two main modules were developed: a workload forecasting module, based on state-of-the-art techniques that minimize the necessity of manual work, and a stability finder module, used to decide when to perform tuning experiments. With these two modules, the probability of testing a new and possibly mistaken software configuration during a workload change is reduced, which would cause the system clients to suffer Quality of Service losses. Moreover, by directly tuning the production system the effort of running the tuner is reduced, meaning that it is easier and faster to apply to different scenarios. The proposed solution was tested on two DBMS models with 20 scenarios, highlighting that the integration of forecasting techniques improves the safety of the tuning process while keeping the effectiveness of the original tuner

Wearable in-ear pulse oximetry: theory and applications

Wearable health technology, most commonly in the form of the smart watch, is employed by millions of users worldwide. These devices generally exploit photoplethysmography (PPG), the non-invasive use of light to measure blood volume, in order to track physiological metrics such as pulse and respiration. Moreover, PPG is commonly used in hospitals in the form of pulse oximetry, which measures light absorbance by the blood at different wavelengths of light to estimate blood oxygen levels (SpO2). This thesis aims to demonstrate that despite its widespread usage over many decades, this sensor still possesses a wealth of untapped value. Through a combination of advanced signal processing and harnessing the ear as a location for wearable sensing, this thesis introduces several novel high impact applications of in-ear pulse oximetry and photoplethysmography. The aims of this thesis are accomplished through a three pronged approach: rapid detection of hypoxia, tracking of cognitive workload and fatigue, and detection of respiratory disease. By means of the simultaneous recording of in-ear and finger pulse oximetry at rest and during breath hold tests, it was found that in-ear SpO2 responds on average 12.4 seconds faster than the finger SpO2. This is likely due in part to the ear being in close proximity to the brain, making it a priority for oxygenation and thus making wearable in-ear SpO2 a good proxy for core blood oxygen. Next, the low latency of in-ear SpO2 was further exploited in the novel application of classifying cognitive workload. It was found that in-ear pulse oximetry was able to robustly detect tiny decreases in blood oxygen during increased cognitive workload, likely caused by increased brain metabolism. This thesis demonstrates that in-ear SpO2 can be used to accurately distinguish between different levels of an N-back memory task, representing different levels of mental effort. This concept was further validated through its application to gaming and then extended to the detection of driver related fatigue. It was found that features derived from SpO2 and PPG were predictive of absolute steering wheel angle, which acts as a proxy for fatigue. The strength of in-ear PPG for the monitoring of respiration was investigated with respect to the finger, with the conclusion that in-ear PPG exhibits far stronger respiration induced intensity variations and pulse amplitude variations than the finger. All three respiratory modes were harnessed through multivariate empirical mode decomposition (MEMD) to produce spirometry-like respiratory waveforms from PPG. It was discovered that these PPG derived respiratory waveforms can be used to detect obstruction to breathing, both through a novel apparatus for the simulation of breathing disorders and through the classification of chronic obstructive pulmonary disease (COPD) in the real world. This thesis establishes in-ear pulse oximetry as a wearable technology with the potential for immense societal impact, with applications from the classification of cognitive workload and the prediction of driver fatigue, through to the detection of chronic obstructive pulmonary disease. The experiments and analysis in this thesis conclusively demonstrate that widely used pulse oximetry and photoplethysmography possess a wealth of untapped value, in essence teaching the old PPG sensor new tricks.Open Acces