The increased demand for using large databases that scale easily and stay consistent requires service providers to find new solutions for storing data in databases. One solution that has emerged is cloud-native databases. Service providers who effectively can transit to cloud-native databases will benefit from new enterprise applications, industrial automation, Internet of Things (IoT) as well as consumer services, such as gaming and AR/VR. This consequently changes the requirements on a database's architecture and infrastructure in terms of being compatible with the services deployed in a cloud-native environment - this is where CockroachDB comes into the picture. CockroachDB is relatively new and is built from the ground up to run in a cloud-native environment. It is built up with nodes that work as individual machines, and these nodes form a cluster. The authors of this report aim to evaluate the characteristics of the Cockroach database to get an understanding of what it offers to companies that are in a cloud-infrastructure transition phase. For the scope of characteristics, this report is focusing on performance, throughput, stress-test, version hot-swapping, horizontal-/vertical scaling, and node disruptions. To do this, a CockroachDB database was deployed on a Kubernetes cluster, in which simulated traffic was conducted. For the throughput measurement, the TPC-C transaction processing benchmark was used. For scaling, version hot-swapping, and node disruptions, an experimental method was performed. The result of the study confirms the expected outcome. CockroachDB does in fact scale easily, both horizontally and vertically, with minimal effort. It also shows that the throughput remains the same when the cluster is scaled up and out since CockroachDB does not have a master write-node, which is the case with some other databases. CockroachDB also has built-in functionality to handle configuration changes like version hot-swapping and node disruptions. This study concluded that CockroachDB lives up to its promises regarding the subjects handled in the report, and can be seen as a robust, easily scalable database that can be deployed in acloud-native environment.

Visualization is an important tool for making data understandable. Visualization can be used for many different purposes, such as charts on the web used to visualize a dataset. Render time is important for websites since slow response times can cause users to leave the site. When creating a website with fast render times in mind, the selection of JavaScript library may be crucial. This work aims to determine if dataset size and/or chart type affects the render time of charts created by different JavaScript libraries. The comparison is done by a literature search to identify suitable libraries, followed by an experiment in which 40 websites are created to compare the performance of selected JavaScript libraries for rendering selected chart types. The results show that while both dataset size and chart type affect the render time in most cases, the libraries scale differently depending on the dataset size.

Abstract [en] Background. Penetration testing is a good technique for finding web vulnerabilities. Vulnerability scanners are often used to aid with security testing. The increased scope is becoming more difficult for scanners to handle in a reasonable amount of time. The problem with vulnerability scanners is that they rely on fuzzing to find vulnerabilities. A problem with fuzzing is that: it generates a lot of network traffic; scans can be excruciatingly slow; limited vulnerability detection if the output string is modified due to filtering or sanitization. Objectives. This thesis aims to investigate if an XSS vulnerability scanner can be made more scalable than the current state-of-the-art. The idea is to examine how reflected parameters can be detected, and if a different methodology can be applied to improve the detection of XSS vulnerabilities. The proposed vulnerability scanner is named Mantis. Methods. The research methods used in this thesis are literature review and experiment. In the literature review, we collected information about the investigated problem to help us analyze the identified research gaps. The experiment evaluated the proposed vulnerability scanner with the current state-of-the-art using the dataset, OWASP benchmark. Results. The result shows that reflected parameters can be reliably detected using approximate string matching. Using the parameter mapping, it was possible to detect reflected XSS vulnerabilities to a great extent. Mantis had an average scan time of 78 seconds, OWASP ZAP 95 seconds and Arachni 17 minutes. The dataset had a total of 246 XSS vulnerabilities. Mantis detected the most at 213 vulnerabilities, Arachni detected 183, and OWASP ZAP 137. None of the scanners had any false positives. Conclusions. Mantis has proven to be an efficient vulnerability scanner for detecting XSS vulnerabilities. Focusing on the set of characters that may lead to the exploitation of XSS has proven to be a great alternative to fuzzing. More testing of Mantis is needed to determine the usability of the vulnerability scanner in a real-world scenario. We believe the scanner has the potential to be a great asset for penetration testers in their work.

"Microservices" is an architectural and organizational style in software design and development in which there is a composition mechanism for independent microservices to call, communicate, and message each other within an application. The microservices composition approach makes design easier to scale, faster to develop, and can accelerate the introduction of new features into applications. To satisfy business requirements, selecting the proper composition style is important for software development; otherwise, application development may fail. The objective of this research is to investigate the hybrid method for composing microservices and compare it with other composition approaches (choreography and orchestration), using quality metrics from the software engineering and business process modeling literature. More precisely, we make use of coupling, cohesion, and scalability metrics to analyze BPMN models representing e-commerce scenarios modeled as microservice compositions. This thesis follows the five steps: research problem identification and objectives, requirement analysis and system design, model design and development, model testing and deployment, and evaluate our BPMN models representing microservice compositions. We develop multiple BPMN workflows as artifacts to analyze choreography, orchestration, and hybrid styles for the microservices composition of e-commerce scenarios. We propose several hybrid models by integrating orchestrations and choreographies in the same workflow. We created a series of small, mid-sized, and end-to-end workflows of e-commerce scenarios. At the tool level, we use the Camunda Modeler, Camunda Platform 8 (as the automation process engine), and Amazon Web Services (AWS) to design, develop, and deploy our models. Finally, we use our calculations of the coupling, cohesion, and scalability measures to reveal an understanding of modeling microservices choreography, orchestration, and hybrid approaches, and we discuss when to use a specific approach for microservices composition. We have found from the evaluation that our proposed models are less tightly coupled compared to those modeled using orchestration and choreography. However, we also discovered that the orchestration style offers better scalability and a lower ratio of coupling and cohesion compared to choreography and hybrid approaches.

This thesis presents Web Utility Kit (WUT): a platform that helps to simplify the process of creating modern web applications. It addresses the need to simplify the web development process through the creation of a hosted service that provides access to a unified set of resources. The resources are made available through a variety of protocols and formats to help simplify their consumption. It also provides a uniform model across all of its resources making multi-resource development an easier and more familiar task. WUT saves the time and cost associated with deployment, maintenance, and hosting of the hardware and software in which resources depend. It has a relatively low overhead averaging 123 ms per request and has been shown capable of linear scaling with each application server capable of handling 120+ requests per minute. This important property of being able to seamlessly scale to developer's needs helps to eliminate the expensive scaling process. Initial users of the platform have found it to be extremely easy to use and have paved the way for future developments.

RDMA-capable networks are gaining traction with datacenter deployments due to their high throughput, low latency, CPU efficiency, and advanced features, such as remote memory operations. However, efficiently utilizing RDMA capability in a common setting of high fan-in, fan-out asymmetric network topology is challenging. For instance, using RDMA programming features comes at the cost of connection scalability, which does not scale with increasing cluster size. To address that, several works forgo some RDMA features by only focusing on conventional RPC APIs. In this work, we strive to exploit the full capability of RDMA, while scaling the number of connections regardless of the cluster size. We present FLOCK, a communication framework for RDMA networks that uses hardware provided reliable connection. Using a partially shared model, FLOCK departs from the conventional RDMA design by enabling connection sharing among threads, which provides significant performance improvements contrary to the widely held belief that connection sharing deteriorates performance. At its core, FLOCK uses a connection handle abstraction for connection multiplexing; a new coalescing-based synchronization approach for efficient network utilization; and a load-control mechanism for connections with symbiotic send-recv scheduling, which reduces the synchronization overheads associated with connection sharing along with ensuring fair utilization of network connections. / M.S. / Internet is one of the great discoveries of our time. It provides access to enormous knowledge sources, makes it easier to communicate across the globe seamlessly with other countless advantages. Accessing the internet over the years, it is noticeable that the latency of services like web searches and downloading files has gone down sharply. A download that used to take minutes during the 2000s can complete within seconds in present times. Network speeds have been improving, facilitating a faster and smoother user experience. Another factor contributing to the improved internet experience is the service providers like Google, Amazon, and others that can process user requests in a fraction of time what used to take before. Web services such as search, e-commerce are implemented using a multi-layer architecture with layer containing hundreds to thousands of servers. Each server runs one or more components of the web service application. In this architecture, user requests are received in the upper layer and processed by the lower layers. Servers in different layers communicate over an ultrafast network like Remote Direct Memory Access (RDMA). The implication of the multi-layer architecture is that a server has to communicate with multiple other servers in the upper and lower layers. Unfortunately, due to its inherent limitations, RDMA does not perform well when network communication takes place with a large number of servers. In this thesis, a new communication framework for RDMA networks, FLOCK is proposed to overcome the scalability limitations of RDMA hardware. FLOCK maintains scalability when communicating with many servers and it consistently provides better performance compared to the state-of-the-art. Additionally, FLOCK utilizes the network bandwidth efficiently and reduces the CPU overheads incurred due to network communication.

Målet med studien var att undersöka skalning av tidsåtgång och minnesanvändning utifrån tre stycken algoritmer som procedurellt genererar banor. De algoritmerna som används är Binary Space Partitioning (BSP), Shortest Path (SP) och Delaunay Triangulation (DT). Skalningen utvärderas genom att se hur tidsåtgången och minnesanvändningen påverkas då algoritmerna ska hantera större banor. Värdena för tid och minne sammanställdes sedan för att avgöra hur de skalade, till vilken grad de var användbara och vilken av algoritmerna som presterade bäst. Utvärderingen visade att BSP presterade bäst i båda kategorierna med relativt jämna värden. SP hade generellt väldigt spretiga tidsvärden. DT var långsammast av de tre algoritmerna i avseende på tid men presterade bättre än SP när det kom till minnesanvändning. Skalning av minne visade sig vara ett mindre problem än förväntat vilket inte är något problem för plattformar som inte är begränsade inbäddade system, exempelvis mikroprocessorer. Framtida studier hade kunnat testa andra algoritmer. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p><p>There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.</p>

This thesis aims to explore the use of a predictive scaling algorithm to scale a microservices-based system according to a predicted system load. A scalable system along with a predictive scaling algorithm is developed and tested by applying a periodic load to the system. The developed scaling algorithm is a combination of a reactive and a predictive algorithm, where the reactive algorithm is used to scale the system when no significant load changes are predicted. The results show that the periodical load is predicted by the algorithm, that the system can be scaled preemptively, and that the algorithm has room for improvement in terms of accuracy. / Detta examensarbete siktar på att utforska möjligheten att använda förutsägande skalningsalgoritmer för att skala ett microservices-baserat system enligt en förutspådd belastning på systemet. Ett skalbart system utvecklas tillsammans med en förutsägande skalningsalgoritm, och testas genom att applicera en periodisk belastning på systemet. Den utvecklade skalningsalgoritmen är en kombination av en reaktiv och förutsägande algoritm, där den reaktiva algoritmen används för att skala systemet då inga signifikanta belastningar förutspås. Resultaten visar att systemets belastning kan förutspås och att systemet kan skalas med hjälp av den förutspådda belastningen, samt att algoritmen har utrymme för förbättringar.

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