Cloud computing stands as a revolution in IT world in recent years. This technology facilitates resource sharing by reducing hardware costs for business users and promises energy efficiency and better resource utilization to the service providers. CPU utilization is a key metric considered in resource management across clouds. The main goal of this thesis study is directed towards investigating CPU utilization behavior with regard to host and guest, which would help us in understanding the relationship between them. It is expected that perception of these relationships would be helpful in resource management. Working towards our goal, the methodology we adopted is experi- mental research. This involves experimental modeling, measurements and observations from the results. The experimental setup covers sev- eral complex scenarios including cloud and a standalone virtualization system. The results are further analyzed for a visual correlation. Results show that CPU utilization in cloud and virtualization sce- nario coincides. More experimental scenarios are designed based on the first observations. The obtaining results show the irregular behav- ior between PM and VM in variable workload. CPU utilization retrieved from both cloud and a standalone system is similar. 100% workload situations showed that CPU utilization is constant with no correlation co-efficient obtained. Lower workloads showed (more/less) correlation in most of the cases in our correlation analysis. It is expected that more number of iterations can possibly vary the output. Further analysis of these relationships for proper resource management techniques will be considered.

Context. In the field of computer science, the concept of cloud computing plays a prominent role which can be hosted on the internet to store, manage and also to process the data. Cloud platforms enables the users to perform large number of computing tasks across the remote servers. There exist several cloud platform providers like Amazon, Microsoft, Google, Oracle and IBM. Several conventional databases are available in cloud service providers in order to handle the data. Cassandra is a NoSQL database system which can handle the unstructured data and can scale large number of operations per second even across multiple datacentres. Objectives. In this study, the performance evaluation of NoSQL database in AWS cloud service provider has been performed. The performance evaluation of a three node Cassandra cluster is performed for different configuration of EC2 instances. This performance has been evaluated using the metrics throughput and CPU utilization. The main aim of this thesis was to evaluate the performance of Cassandra under various configurations with the YCSB benchmarking tool. Methods. A literature review has been conducted to gain more knowledge about the current research area. The metrics required to evaluate the performance of Cassandra were identified through literature study. The experiment was conducted to compute the results for throughput and CPU utilization under the different configurations t2.micro, t2.medium and t2.small for 3 node and 6 node cluster using YCSB benchmarking tool. Results. The results of the experiment include the metrics, throughput and CPU utilization which were identified in the literature review. The results calculated were plotted as graphs to compare their performance for three different configurations. The results obtained were segregated as two different scenarios which were for 3 node and 6 node clusters. Conclusions. Based on the obtained values of throughput the optimal or sub-optimal configuration of a data centre running multiple instances of Cassandra such that the specific throughput requirements are satisfied.

Evolutionary algorithms are often used for hard optimization problems. Solving time of this problems is long, so we want effective parallelization for this algorithms. Unfortunately, classical methods of parallelization do not work very well in cases where the individual evaluations of problems take significantly different times. In this project, we will try to extend the evolu- tionary algorithm with interleaving generations, which offers a better use of computational resources than classical parallel evolutionary algorithms, by speculative evaluation. Speculative evaluation means the estimation of an in- dividual's fitness function and the prediction of the following steps, which we will use later in the case of a correct estimate. We compare the algorithm with speculative evaluation with the original version in a series of experi- ments and we look at the effect of accuracy in the speculative step on the performance of the algorithm. 1

<p>Vizz3D is a powerful 3D visualization system. The current version is neither perfect nor up-to-date. Furthermore, some important features are missing. In order to keep the tool valuable it needs to be maintained. I implemented a new feature allowing to save and load the view port in the graph to control the camera position. I also improved the CPU utilization and the navigation system to solve the limitations in Vizz3D and to improve the overall performance.</p>

Vizz3D is a powerful 3D visualization system. The current version is neither perfect nor up-to-date. Furthermore, some important features are missing. In order to keep the tool valuable it needs to be maintained. I implemented a new feature allowing to save and load the view port in the graph to control the camera position. I also improved the CPU utilization and the navigation system to solve the limitations in Vizz3D and to improve the overall performance.

The Hybrid approach of development has for a long time been the dominating way to develop cross-platform applications targeting both the web and mobile. In recent years, a new combination of technology has appeared called Progressive Web Application (PWA) which aims to combine Native capabilities with best practices of the web to deliver a new Native-like experience to users without the need of Native wrappers. So far PWAs have proven to be the inferior choice when it came to performance and platform support. The purpose of this study is to compare the two technologies based on a literature review and evaluate the current performance across three parameters in an experiment - battery consumption, CPU utilization and time to first activity. Two applications were developed using each respective technique, with the Apache Cordova framework being used for the Hybrid approach and the React framework being used to implement PWA features. The results showed that the Hybrid approach is better in the majority of tests, offering more in terms of platform API access and providing better performance while only being slower when it came to time it took to first activity; but something to consider is that the PWA approach was not far behind. The conclusion this study arrived at was that PWAs have developed significantly since previous studies and is almost able to match Hybrid apps in terms of APIs and performance, but that Hybrid apps are still the preferred choice when it comes to performance. Further development and a wider adaptation of the PWA specification could very well change the way developers choose to approach mobile app development in the future as well as a potential for bringing the web closer to the mobile platform.

OpenStack is currently the most popular open source platform for Infrastructure as a Service (IaaS) clouds. OpenStack lets users deploy virtual machines and other instances, which handle different tasks for managing a cloud environment on the fly. A lot of cloud platform offerings, including the Ericsson Cloud System, are based on OpenStack. Despite the popularity of OpenStack, there is currently a limited understanding of how much resource is consumed/needed by components of OpenStack under different operating conditions such as number of compute nodes, number of running VMs, the number of users and the rate of requests to the various services. The master thesis attempts to model the resource demand of the various components of OpenStack in function of different operating condition, identify correlations and evaluate how accurate the predictions are. For this purpose, a physical OpenStack is setup with one strong controller node and eight compute nodes. All the experiments and measurements were on virtual OpenStack components on top of the main physical one. In conclusion, a simple model is generated for idle behavior of OpenStack, starting and stopping a Virtual Machine (VM) API calls which predicts the total CPU utilization based on the number of Compute Nodes and VMs.

The core idea of this thesis is to reduce the workload of manual inspection when the performance analysis of an updated software is required. The Central Process- ing Unit (CPU) utilization, which is one of the essential factors for evaluating the performance, is analyzed. The purpose of this work is to apply machine learning techniques that are suitable for detecting the state of the CPU utilization and any changes in the test environment that affects the CPU utilization. The detection re- lies on a Markov switching model to identify structural changes, which are assumed to follow an unobserved Markov chain, in the time series data. A historical behav- ior of the data can be described by a first-order autoregression. Then, the Markov switching model becomes a Markov switching autoregressive model. Another ap- proach based on a non-parametric analysis, a distribution-free method that requires fewer assumptions, called an E-divisive method, is proposed. This method uses a hi- erarchical clustering algorithm to detect multiple change point locations in the time series data. As the data used in this analysis does not contain any ground truth, the evaluation of the methods is analyzed by generating simulated datasets with known states. Besides, these simulated datasets are used for studying and compar- ing between the Markov switching autoregressive model and the E-divisive method. Results show that the former method is preferable because of its better performance in detecting changes. Some information about the state of the CPU utilization are also obtained from performing the Markov switching model. The E-divisive method is proved to have less power in detecting changes and has a higher rate of missed detections. The results from applying the Markov switching autoregressive model to the real data are presented with interpretations and discussions.

Multi Protocol Label Switching (MPLS) är en routingteknik som blir allt vanligare i dagens nätverk. Vid implementation av MPLS i nätverk är det grundläggande att hårdvaran har tillräckligt hög prestanda för att hantera tekniken. Om så inte är fallet kan det leda till negativa konsekvenser såsom paketförluster eller i värsta fall att routern havererar. Syftet med arbetet var att ta reda på hur processorbelastningen på en router skiljer sig när MPLS var implementerat jämfört med traditionell IP-routing. Routingprotokollen BGP, OSPF och RIP användes tillsammans med de olika routingteknikerna för att se vilket av dessa som krävde minst processorkraft. För att ta reda på hur processorbelastningen påverkades skapades en experimentmiljö. Varje routingprotokoll konfigurerades tillsammans med MPLS respektive traditionell IP-routing, vilket gav oss sex olika scenarion. Trafik skickades sedan genom nätverket med hjälp av applikationen Ostinato för att belasta enheterna. Resultatet av testerna visade att processorbelastningen blev högre för MPLS än för traditionell IP-routing. Det gällde för samtliga routingprotokoll. Det routingprotokoll som tillsammans med MPLS lyckades hålla processorbealstningen lägst var BGP, medans OSPF orsakade högst processorbelastning. Skillnaden mellan routingprotokollen OSPF och RIP var marginell. / Multi Protocol Label Switching (MPLS) is a growing routing technology. It is getting more common in today’s modern networks. When implementing MPLS, it is essential that the hardware is capable of handling such technology. If not, this could lead to packet loss or even network outage. The purpose of this study was to examine differences between MPLS and traditional IP-routing in aspect of CPU utilization. Routing protocols BGP, OSPF and RIP were configured together with the technologies in order to see which of the mentioned routing protocols that was the most efficient in terms of CPU utilization. An environment was created for the experiment. Each routing protocol was configured together with MPLS and traditional IP-routing respectively, which lead to a total of six scenarios. Traffic was sent through the network with an application called Ostinato with the purpose of increasing the load of the devices. The results showed higher CPU utilization with MPLS compared to traditional IP-routing. This was the case with each routing protocol. BGP had the lowest CPU utilization of the routing protocols in conjunction with MPLS whilst OSPF had the highest. The result between OSPF and RIP was slightly different.

Cloud storage services allow users to store and access data in a secure and flexible manner. In recent years, cloud storage services have seen rapid growth in popularity as well as in technological progress and hundreds of millions of users use these services to store thousands of petabytes of data. Additionally, the synchronization of data that is essential for these types of services stands for a significant amount of the total internet traffic. In this thesis, seven cloud storage applications were tested under controlled experiments during the synchronization process to determine feature support and measure performance metrics. Special focus was put on comparing applications that perform client side encryption of user data to applicationsthat do not. The results show a great variation in feature support and performance between the different applications and that client side encryption introduces some limitations to other features but that it does not necessarily impact performance negatively. The results provide insights and enhances the understanding of the advantages and disadvantages that come with certain design choices of cloud storage applications. These insights will help future technological development of cloud storage services.