abstract: Continuous monitoring of sensor data from smart phones to identify human activities and gestures, puts a heavy load on the smart phone's power consumption. In this research study, the non-Euclidean geometry of the rich sensor data obtained from the user's smart phone is utilized to perform compressive analysis and efficient classification of human activities by employing machine learning techniques. We are interested in the generalization of classical tools for signal approximation to newer spaces, such as rotation data, which is best studied in a non-Euclidean setting, and its application to activity analysis. Attributing to the non-linear nature of the rotation data space, which involve a heavy overload on the smart phone's processor and memory as opposed to feature extraction on the Euclidean space, indexing and compaction of the acquired sensor data is performed prior to feature extraction, to reduce CPU overhead and thereby increase the lifetime of the battery with a little loss in recognition accuracy of the activities. The sensor data represented as unit quaternions, is a more intrinsic representation of the orientation of smart phone compared to Euler angles (which suffers from Gimbal lock problem) or the computationally intensive rotation matrices. Classification algorithms are employed to classify these manifold sequences in the non-Euclidean space. By performing customized indexing (using K-means algorithm) of the evolved manifold sequences before feature extraction, considerable energy savings is achieved in terms of smart phone's battery life. / Dissertation/Thesis / M.S. Electrical Engineering 2014

Abstract This bachelor thesis discusses existing performance analysis techniques for ARM based architecture processors. This includes a comparison between couple of performance analysis applications installed on two Android test devices. Each application monitored CPU performance of the device in three test scenarios. Each test was done in five iterations. The results were compared for each test and for each application. The results of these iterations were compared to find the most stable application among the rest.

The popularity that mobile games gained recently gives the opportunity to develop more mobile games. Limited by the scarce resources on mobile phones, developing good games becomes critical and requires special optimization while choosing the design approach.   Object-oriented Design (OOD) and Data-oriented Design (DOD) are two programming paradigms that have different ways of defining and structuring data. The purpose of this student thesis is to investigate the CPU and Memory performance differences between the two approaches.   To answer the research questions an experiment is conducted where two identical mobile games are built, one according to OOD and the other to DOD to collect empirical quantitative data and compare the results. The study limits the scope by  running the games on Android mobile phones.   The results of comparing the CPU Usage show significant differences especially when the amount of data is large. For instance, in the DOD version of the game, the CPU spends 20.9% of the time on updating data, while it spends 69.2% of the time on the same action in the OOD version of the game. No significant differences are observed regarding the total Memory allocated for the games in both versions. It can therefore be concluded that when the number of objects/data is big, a more optimized code should be written following the Data oriented Design approach with regard to better CPU and Memory Usage and    better game performance.

With the constant advances in technology, people are using more wireless products, such as earphones or speakers whereas many of them use Bluetooth. With the current advances in Bluetooth technology, consumers and manufacturers have a hard time keeping up with the pace. Thus, when it comes to factors such as battery drainage, CPU usage, and range there is missing knowledge. This study is conducted to find out what effect the different codecs have on these factors, by comparing the two most commonly used codecs SBC and AAC. Using a codec that has lower battery drainage whilst still having a good enough audio quality can have a positive impact on our society and environment. Needing less electricity, lessens the overall energy consumption and directly lowers the energy production. Our results indicate that there is a significant difference in CPU usage but not in battery drainage or range.

Dagens applikationer hanterar mer och mer data vilket resulterar i att de blir allt mer resurskrävande och kräver mer av hårdvaran. Vilket i förlängningen kan innebär att hårdvaran måste bytas ut med jämna mellanrum för att kunna köra mjukvaran på ett för användaren tillfredsställande sätt. Detta arbete undersöker om det genom att byta designteknik är möjligt att utveckla mindre resurskrävande applikationer. Arbetet presenterar en jämförelse mellan objektorienterad design (även kallad objektorienterad programmering, OOP) och data orienterad design (DOD). Detta genom att dels ta upp kända för- och nackdelar med respektive designteknik samt genom att utföra en mätning på respektive teknik. Det som anses vara de främsta fördelarna med OOP är återanvändning av kod, att koden är lätt att underhålla, säkerhet i form av inkapsling samt att objekten som används reflekterar den mänskliga verkligheten. Dessa fördelar är dock även något som bidrar till det som anses vara den främsta nackdelen med OOP, nämligen att den är prestandakrävande. När det gäller DOD så anses de främsta fördelarna vara att det medför en cachevänligare kod som leder till färre cachemissar. Det anses även vara lättare att parallellisera koden i jämförelse med OOP. Den nackdelen som tas upp med DOD är att de tar tid att lära sig och kräver en del övning. Dock är DOD väldigt okänt vilket resulterade i ett svagt underlag. Två simuleringar utvecklades i Unity varav den ena använder sig av den nya teknikstacken DOTS som är dataorienterad. Resultatet av mätningarna indikerar på att DOD använder mindre av hårdvaruresurserna vid prestandakrävande applikationer. Om applikationen ej är prestandakrävande märks dock ingen skillnad mellan de olika teknikerna vid fråga om processoranvändning. / Today, applications handle more and more data, which results in them becoming increasingly resource-intensive and requiring more of the hardware. Which in the long run may cause that the hardware must be replaced at regular intervals to be able to run the software in a way that is satisfactory for the user. This thesis investigates whether it is possible to get less resource-intensive applications by changing the design technology. The paper presents a comparison between object-oriented design (also known as object-oriented programming, OOP) and data-oriented design (DOD). This is performed by addressing the known advantages and disadvantages of each design technique and by measuring each technique in the matter of performance. What was considered to be the main advantages of OOP is the reuse of code, that the code is easy to maintain, security in the form of encapsulation and that the objects that are used reflect human reality. On the other hand, these advantages also contribute to what is considered to be the main disadvantage of OOP, namely that it is performance-intensive. When it comes to DOD, the main advantages are considered to be that it results in a more cache-friendly code that leads to fewer cache misses. DOD is also considered easier to parallelize the code compared to OOP. The disadvantage of DOD is that it is time consuming to learn and requires some practice. Though, DOD is very unknown which resulted in a narrow basis. Two simulations were developed in Unity, one of which uses the new technology stack DOTS, which is data-oriented. The results of the measurements indicate that DOD uses less of the hardware resources in performance-intensive applications. If the application is not performance-intensive, though, no difference is noticed between the different technologies when it comes to CPU-usage.

FundaÃÃo Cearense de Apoio ao Desenvolvimento Cientifico e TecnolÃgico / O escalonamento de recursos à um processo chave para a plataforma de ComputaÃÃo em Nuvem, que geralmente utiliza mÃquinas virtuais (MVs) como unidades de escalonamento. O uso de tÃcnicas de virtualizaÃÃo fornece grande flexibilidade com a habilidade de instanciar vÃrias MVs em uma mesma mÃquina fÃsica (MF), modificar a capacidade das MVs e migrÃ-las entre as MFs. As tÃcnicas de consolidaÃÃo e alocaÃÃo dinÃmica de MVs tÃm tratado o impacto da sua utilizaÃÃo como uma medida independente de localizaÃÃo. à geralmente aceito que o desempenho de uma MV serà o mesmo, independentemente da MF em que ela à alocada. Esta à uma suposiÃÃo razoÃvel para um ambiente homogÃneo, onde as MFs sÃo idÃnticas e as MVs estÃo executando o mesmo sistema operacional e aplicativos. No entanto, em um ambiente de ComputaÃÃo em Nuvem, espera-se compartilhar um conjunto composto por recursos heterogÃneos, onde as MFs podem variar em termos de capacidades de seus recursos e afinidades de dados. O objetivo principal deste trabalho à apresentar uma arquitetura que possibilite a padronizaÃÃo da representaÃÃo do poder de processamento das MFs e MVs, em funÃÃo de Unidades de Processamento (UPs), apoiando-se na limitaÃÃo do uso da CPU para prover isolamento de desempenho e manter a capacidade de processamento das MVs independente da MF subjacente. Este trabalho busca suprir a necessidade de uma soluÃÃo que considere a heterogeneidade das MFs presentes na infraestrutura da Nuvem e apresenta polÃticas de escalonamento baseadas na utilizaÃÃo das UPs. A arquitetura proposta, chamada FairCPU, foi implementada para trabalhar com os hipervisores KVM e Xen, e foi incorporada a uma nuvem privada, construÃda com o middleware OpenNebula, onde diversos experimentos foram realizados para avaliar a soluÃÃo proposta. Os resultados comprovam a eficiÃncia da arquitetura FairCPU em utilizar as UPs para reduzir a variabilidade no desempenho das MVs, bem como para prover uma nova maneira de representar e gerenciar o poder de processamento das MVs e MFs da infraestrutura. / Resource scheduling is a key process for cloud computing platform, which generally uses virtual machines (VMs) as scheduling units. The use of virtualization techniques provides great flexibility with the ability to instantiate multiple VMs on one physical machine (PM), migrate them between the PMs and dynamically scale VMâs resources. The techniques of consolidation and dynamic allocation of VMs have addressed the impact of its use as an independent measure of location. It is generally accepted that the performance of a VM will be the same regardless of which PM it is allocated. This assumption is reasonable for a homogeneous environment where the PMs are identical and the VMs are running the same operating system and applications. Nevertheless, in a cloud computing environment, we expect that a set of heterogeneous resources will be shared, where PMs will face changes both in terms of their resource capacities and as also in data affinities. The main objective of this work is to propose an architecture to standardize the representation of the processing power by using processing units (PUs). Adding to that, the limitation of CPU usage is used to provide performance isolation and maintain the VMâs processing power at the same level regardless the underlying PM. The proposed solution considers the PMs heterogeneity present in the cloud infrastructure and provides scheduling policies based on PUs. The proposed architecture is called FairCPU and was implemented to work with KVM and Xen hypervisors. As study case, it was incorporated into a private cloud, built with the middleware OpenNebula, where several experiments were conducted. The results prove the efficiency of FairCPU architecture to use PUs to reduce VMsâ performance variability, as well as to provide a new way to represent and manage the processing power of the infrastructureâs physical and virtual machines.

Private and public clouds require users to specify requests for resources such as CPU and memory (RAM) to be provisioned for their applications. The values of these requests do not necessarily relate to the application’s run-time requirements, but only help the cloud infrastructure resource manager to map requested virtual resources to physical resources. If an application exceeds these values, it might be throttled or even terminated. Consequently, requested values are often overestimated, resulting in poor resource utilization in the cloud infrastructure. Autoscaling is a technique used to overcome these problems. In this research, we formulated two new predictive CPU autoscaling strategies forKubernetes containerized applications, using time-series analysis, based on Holt-Winters exponential smoothing and long short-term memory (LSTM) artificial recurrent neural networks. The two approaches were analyzed, and their performances were compared to that of the default Kubernetes Vertical Pod Autoscaler (VPA). Efficiency was evaluated in terms of CPU resource wastage, and insufficient CPU percentage and amount for container workloads from Alibaba Cluster Trace 2018, and others. In our experiments, we observed that Kubernetes Vertical Pod Autoscaler (VPA) tended to perform poorly on workloads that periodically change. Our results showed that compared to VPA, predictive methods based on Holt- Winters exponential smoothing (HW) and Long Short-Term Memory (LSTM) can decrease CPU wastage by over 40% while avoiding CPU insufficiency for various CPU workloads. Furthermore, LSTM has been shown to generate stabler predictions compared to that of HW, which allowed for more robust scaling decisions. / Privata och offentliga moln kräver att användare begär mängden CPU och minne (RAM) som ska fördelas till sina applikationer. Mängden resurser är inte nödvändigtvis relaterat till applikationernas körtidskrav, utan är till för att molninfrastrukturresurshanteraren ska kunna kartlägga begärda virtuella resurser till fysiska resurser. Om en applikation överskrider dessa värden kan den saktas ner eller till och med krascha. För att undvika störningar överskattas begärda värden oftast, vilket kan resultera i ineffektiv resursutnyttjande i molninfrastrukturen. Autoskalning är en teknik som används för att överkomma dessa problem. I denna forskning formulerade vi två nya prediktiva CPU autoskalningsstrategier för containeriserade applikationer i Kubernetes, med hjälp av tidsserieanalys baserad på metoderna Holt-Winters exponentiell utjämning och långt korttidsminne (LSTM) återkommande neurala nätverk. De två metoderna analyserades, och deras prestationer jämfördes med Kubernetes Vertical Pod Autoscaler (VPA). Prestation utvärderades genom att observera under- och överutilisering av CPU-resurser, för diverse containerarbetsbelastningar från bl. a. Alibaba Cluster Trace 2018. Vi observerade att Kubernetes Vertical Pod Autoscaler (VPA) i våra experiment tenderade att prestera dåligt på arbetsbelastningar som förändras periodvist. Våra resultat visar att jämfört med VPA kan prediktiva metoder baserade på Holt-Winters exponentiell utjämning (HW) och långt korttidsminne (LSTM) minska överflödig CPU-användning med över 40 % samtidigt som de undviker CPU-brist för olika arbetsbelastningar. Ytterligare visade sig LSTM generera stabilare prediktioner jämfört med HW, vilket ledde till mer robusta autoskalningsbeslut.