Usage of cloud-based storage systems gained a lot of prominence in fast few years. Every day millions of files are uploaded and downloaded from cloud storage. This data that cannot be handled by traditional databases and this is considered to be Big Data. New powerful platforms have been developed to store and organize big and unstructured data. These platforms are called Big Data systems. Some of the most popular big data platform are Mongo, Hadoop, and Cassandra. In this, we used Cassandra database management system because it is an open source platform that is developed in java. Cassandra has a masterless ring architecture. The data is replicated among all the nodes for fault tolerance. Unlike MySQL, Cassandra uses per-column basis technique to store data. Cassandra is a NoSQL database system, which can handle unstructured data. Most of Cassandra parameters are scalable and are easy to configure. Amazon provides cloud computing platform that helps a user to perform heavy computing tasks over remote hardware systems. This cloud computing platform is known as Amazon Web Services. AWS services also include database deployment and network management services, that have a non-complex user experience. In this document, a detailed explanation on Cassandra database deployment on AWS platform is explained followed by Cassandra performance tuning.    In this study impact on read and write performance with change Cassandra parameters when deployed on Elastic Cloud Computing platform are investigated. The performance evaluation of a three node Cassandra cluster is done. With the knowledge of configuration parameters a three node, Cassandra database is performance tuned and a draft model is proposed.             A cloud environment suitable for the experiment is created on AWS. A three node Cassandra database management system is deployed in cloud environment created. The performance of this three node architecture is evaluated and is tested with different configuration parameters. The configuration parameters are selected based on the Cassandra metrics behavior with the change in parameters. Selected parameters are changed and the performance difference is observed and analyzed. Using this analysis, a draft model is developed after performance tuning selected parameters. This draft model is tested with different workloads and compared with default Cassandra model. The change in the key cache memory and memTable parameters showed improvement in performance metrics. With increases of key cache size and save time period, read performance improved. This also showed effect on system metrics like increasing CPU load and disk through put, decreasing operation time and The change in memTable parameters showed the effect on write performance and disk space utilization. With increase in threshold value of memTable flush writer, disk through put increased and operation time decreased. The draft derived from performance evaluation has better write and read performance.

During the COVID-19 pandemic the questions of the efficiency around meetings has been in the forefront of some discussion inside companies. One way to measure efficiency is to measure the interactivity between different participants. In order to measure this the participants need to be identified. With the recent spike of Machine learning advancements, is this something that can be done using facial and voice recognition? Another field that has risen to the top is cloud computing. Can machine learning and cloud computing be used to evaluate and monitor a meeting, thus handling both audio and video streams in a real time environment? The conclusion of this thesis is that Artificial Intelligence(AI) can be used to monitor a meeting. To be able to do so Amazon Web Service (AWS) can be utilized. The choice of using a DeepLens was however not best choice. A hardware like DeepLens is required, but with better integration with cloud computing, as well with more freedom regarding the usage of several models for handling both feeds. With the usage of other models to automatic annotate data the time needed for training a new model can be reduced. The data generated during a single meeting is enough with the help of transfer learning from Amazon web service to build a model for facial identification and detection.

Det har blivit allt vanligare att inomhusverksamheter vill kunna bevaka antalet personer som befinner sig i deras lokaler. Att manuellt räkna antalet personer eller att använda sig utav rörelsesensorer har olika nackdelar. På grund av den anledningen är det lämpligt att utforska andra tekniska och mer automatiserade lösningar, som använder sig utav enkla komponenter. Litteraturstudien gav en förståelse om bildanalys och vilka tekniska verktyg som kan användas för att analysera bilder. Amazon Rekognition och OpenCV är två av de verktyg som användes för att kunna bygga en prototyp, som kan räkna antalet personer i en lokal i realtid. Resultatet visade att en lösning med OpenCV inte är möjlig, med de kunskaper litteraturstudien gav. Resultatet ifrån Amazon Rekognition indikerar att det är möjligt att räkna antalet personer med väldigt hög noggrannhet och precision. Precis som att en människa kan bli distraherad, kan även prototypen missa enstaka personer. Amazon Rekognition kunde även särskilja människor ifrån andra objekt, vilket en rörelsesensor inte kan göra. Resultatet visade även fåtal brister så som dålig responstid, men dessa brister hade kunnat åtgärdas ifall mer tid återstod. / It has become increasingly common for indoor businesses to be able to monitor the number of people who are in their premises. Manually counting the number of people or using motion sensors has various disadvantages. For this reason, it is advisable to explore other technical and more automated solutions, which use simple components. The literature study provided an understanding of image analysis and the technical tools that can be used to analyze images. Amazon Recognition and OpenCV are two of the tools used to build two prototypes that can count the number of people in a room in real time. The results showed that a solution with OpenCV is not possible, with the knowledge the literature study provided. The result from Amazon Recognition indicates that it is possible to count the number of people with very high accuracy and precision. Just as a human being can be distracted, the prototype can also miss individual people. Amazon Recognition could also distinguish people from other objects, which a motion sensor cannot do. The results also showed a few shortcomings such as poor response time, but these shortcomings could have been remedied if more time remained.

This thesis explores the creation of a scaling, containerized, advertisement server that will be used by Gold Town Games AB to better integrate ads into their application(s). The server is built as a Docker image that will be used to create server instances on AWS Elastic Container Service for automatic scaling and server resource configuration. The server was created with the intention that GTG will have full control over what advertisements are shown in their application(s) and to seamlessly integrate sponsored logos onto jerseys or sports fields. This will not only serve as a source of income with advertisers paying for ad space, but it will also make the game elements more realistic as we have come to expect teams and stadiums to be sponsored and plastered with company logos. Another important part when displaying advertisement is to track statistics for the ads, since without a way to show advertisers that their ads are shown and that they are generating engagement it is very hard to sell the ad space. / Detta examensarbete utforskar skapandet av en skalbar, containerbaserad, reklamserver som kommer användas av Gold Town Games AB för att integrera reklam i deras applikation(er). Servern är byggd som en Docker-bild som används för att skapa instanser på AWS Elastic Container Service för automatisk skalning och serverresurshantering. Servern är utvecklad med tanken att GTG ska ha full kontroll över vilken reklam som visas i deras applikation(er) och för att kunna lägga till sponsrade loggor på matchtröjor och i arenor. Detta är inte bara en extra form av inkomst, då annonsörer betalar för reklamplatser, utan hjälper även till att få delar av spelen att kännas mer realistiska då vi är vana att lag och arenor är sponsrade och fulla av företagsloggor. En annan viktig del när man visar reklam är att kunna spara statistik för den, eftersom det skulle vara väldigt svårt att sälja reklamplatser utan att kunna visa att folk faktiskt ser reklamen.

In a heavy vehicle, internet connection is not reliable, primarily because the truck often travels to a remote location where network might not be available. The data generated from the sensors in a vehicle might not be sent to the internet when the connection is poor and hence it would be appropriate to store and do some basic computation on those data in the heavy vehicle itself and send it to the cloud when there is a good network connection. The process of doing computation near the place where data is generated is called Edge computing. Scania has its own Edge computation solution, which it uses for doing computations like preprocessing of sensor data, storing data etc. Scania’s solution is compared with a commercial edge computing platform called as AWS (Amazon Web Service’s) Greengrass. The comparison was in terms of Data efficiency, CPU load, and memory footprint. In the conclusion it is shown that Greengrass solution works better than the current Scania solution in terms of CPU load and memory footprint, while in data efficiency even though Scania solution is more efficient compared to Greengrass solution, it was shown that as the truck advances in terms of increasing data size the Greengrass solution might prove competitive to the Scania solution.One more topic that is explored in this thesis is Digital twin. Digital twin is the virtual form of any physical entity, it can be formed by obtaining real-time sensor values that are attached to the physical device. With the help of sensor values, a system with an approximate state of the device can be framed and which can then act as the digital twin. Digital twin can be considered as an important use case of edge computing. The digital twin is realized with the help of AWS Device shadow. / I ett tungt fordonsscenario är internetanslutningen inte tillförlitlig, främst eftersom lastbilen ofta reser på avlägsna platser nätverket kanske inte är tillgängligt. Data som genereras av sensorer kan inte skickas till internet när anslutningen är dålig och det är därför bra att ackumulera och göra en viss grundläggande beräkning av data i det tunga fordonet och skicka det till molnet när det finns en bra nätverksanslutning. Processen att göra beräkning nära den plats där data genereras kallas Edge computing. Scania har sin egen Edge Computing-lösning, som den använder för att göra beräkningar som förbehandling av sensordata, lagring av data etc. Jämförelsen skulle vara vad gäller data efficiency, CPU load och memory consumption. I slutsatsen visar det sig att Greengrass-lösningen fungerar bättre än den nuvarande Scania-lösningen när det gäller CPU-belastning och minnesfotavtryck, medan det i data-effektivitet trots att Scania-lösningen är effektivare jämfört med Greengrass-lösningen visades att när lastbilen går vidare i Villkor för att öka datastorleken kan Greengrass-lösningen vara konkurrenskraftig för Scania-lösningen. För att realisera Edge computing används en mjukvara som heter Amazon Web Service (AWS) Greengrass.Ett annat ämne som utforskas i denna avhandling är digital twin. Digital twin är den virtuella formen av någon fysisk enhet, den kan bildas genom att erhålla realtidssensorvärden som är anslutna till den fysiska enheten. Med hjälp av sensorns värden kan ett system med ungefärligt tillstånd av enheten inramas och som sedan kan fungera som digital twin. Digital twin kan betraktas som ett viktigt användningsfall vid kantkalkylering. Den digital twin realiseras med hjälp av AWS Device Shadow.

Background: As cloud computing has risen over the last decades, there are several cloud services accessible on the market, users may prefer to select those that are more flexible and efficient. Based on the preceding, we chose to research to evaluate cloud services in terms of which would be better for the user in terms ofgetting the needed data from the chosen website and utilizing JMeter for performance testing. If we continue our thesis study by assessing the performance of different sample users using JMeter as the testing tool, it is appropriate for our thesis research subject. In this case, the user interfaces of GCP and AWS are compared while doing several compute engine-related operations. Objectives: This thesis aims to test the website performance after deploying in two distinct cloud platforms.After the creation of instances in AWS, a domain in GCP and also the bucket, the website files are uploaded into the bucket. The GCP and AWS instances are connected to the lifestyle store website. The performance testing on the selected website is done on both services, and then comparison ofthe outcomes of our thesis research using the testing tool Jmeter is done. Methods: In these, we choose experimentation as our research methodology,and in this, the task is done in two cloud platforms in which the website will be deployed separately. The testing tool with performance testing is employed. JMeter is used to test a website’s performance in both possible services and then to gather our research results, and the visualization of the results are done in an aggregate graph, graphs and summary reports. The metrics are Throughput, average response time, median, percentiles and standard deviation. Results: The results are based on JMeter performance testing of a selected web-site between two cloud platforms. The results of AWS and GCP can be shown in the aggregate graph. The graph results are based on the testing tool to determine which service is best for users to obtain a response from the website for requested data in the shortest amount of time. We have considered 500 and 1000 users, and based on the results, we have compared the metrics throughput, average response time, standard deviation and percentiles. The 1000 user results are compared to know which cloud platform performs better. Conclusions: According to the results from the 1000 users, it can be concluded that AWS has a higher throughput than GCP and a less average response time.Thus, it can be said that AWS outperforms GCP in terms of performance.