Background:  Cloud computing technology has come a long way with various technological advancements in the past few years. It has been accelerated with the evolution of various virtualization technologies. Currently almost every social platform and small-scale applications look towards cloud to deploy their services successfully and provide maximum satisfaction to their end-user. Thus, virtualizing their services becomes utmost important to deploy and develop their applications. This alone emphasizes the importance of Docker containers in the development world. Docker containers right now are playing a very important role in the field of cloud computing. Since Multimedia plays a huge role in our day to day lives and most people crave for faster and efficient responses, it is essential to develop our applications with better Real time communication capabilities. Thus, we are determining which container orchestration tool serves best for Real time communication applications.  A multimedia application is developed and deployed using WebRTC based Kurento media server and the performance of the server is measured when the application is deployed. We have chosen Kubernetes and Docker Swarm as container platforms for this thesis. The Servers and Clients are virtualized and metrics such as CPU Utilization, Network Traffic, Container overhead, Memory Utilization are measured. These metrics provide the performance overhead in different scenarios for each orchestration technology. This will be helpful to analyze and understand the effect of Kurento server on these technologies. Thus, the results are expected to determine which orchestration technology serves best for RTC applications. Objectives: The objectives of this project are:  • To implement WebRTC based Kurento server in a container orchestrated environment.  • To extract performance metrics such as Network Traffic, CPU and Memory Utilization while server is running.  • To compare WebRTC based Kurento server in Kubernetes and Docker Swarm.   Method: Kubernetes and Docker Swarm environments are setup and then docker images with video conferencing application(One-to-One call and One-to-Many call) using Kurento media server is deployed in them. Once either of the applications is running, experiments are performed for analyzing performance metrics like CPU Utilization, Memory Utilization, Network Traffic and overhead using monitoring tool, Prometheus. Along with Kubernetes and Docker Swarm, Kurento server is also deployed on a stand-alone container to estimate the performance overhead. Later, statistical analysis(ANOVA and differences of Standard error) is done over these metrics and conclusions are drawn.  Results: Based on the performed experiments and the extracted metrics, for One-to-One call application, Kubernetes showed better resource utilization for CPU and Network Traffic while it consumed more memory over Docker Swarm. Similar behaviour is observed for One-to-Many application. When application is scaled, the percent of resource utilization increase in Kubernetes is higher when compared to Docker Swarm, but overall resource utilization of Kubernetes is much lower than that of Docker Swarm.  Conclusions: WebRTC based Kurento media server is investigated in  Kubernetes and Docker Swarm. From the detailed analysis there is significant overhead in Docker Swarm than in Kubernetes for CPU Utilization and Network Traffic. For Memory Utilization, this is opposite. Packet Loss resulted in 0 percent as network transfer is within the same network . By considering all the metrics and providing evidence that numbers obtained in this thesis are statistically significant and not by fluctuations(ANOVA and post-hoc analysis), we can better recommend Kubernetes over Docker Swarm for Web based Real Time Communication.   However, not all applications need the complex deployment, scheduling, and scaling services (or the overhead) that Kubernetes offers. But to meet the increasing demand for seamless Real time communications, and to suffice user requirements, the overheard offered by it is acceptable.

Inovácia na finančných trhoch poskytuje nové príležitosti. Algoritmické obchodovanie je vhodný spôsob využitia týchto príležitostí. Táto práca sa zaoberá návrhom a implementáciou systému, ktorý by dovoľoval svojím uživateľom vytvárať vlastné obchodovacie stratégie, a pomocou nich obchodovať na burzách. Práca kladie dôraz na návrh distribuovaného systému, ktorý bude škálovatelný, pomocou technológií cloud computingu.

Diplomová práce popisuje vytvoření aplikace pro monitorování síťových zařízení. Výsledky jsou zobrazené jako grafické uživatelské rozhraní společně s vykreslenou topologií. Aplikace je z velké části napsána v jazyce Python. Pro získávání informací z topologie jsou využity protokoly SNMP a SSH. Hlavní cíl je vytvořit aplikaci, která monitoruje síťová zařízení a vykresluje tuhle topologii do grafického uživatelského rozhraní. Tato aplikace reaguje dynamicky na změny v monitorovací topologii.

Cieľom tejto práce je navrhnúť službu pre automatizované testovanie študentských programovacích projektov na základe požiadaviek a následne implementovať túto službu za použitia technológií OpenShift, Python a Git. Vytvorenie takejto služby stavia základ pre zjednotený proces testovania študentských projektov, ktorý zahŕňa spúšťanie testovacích sád v oddelených Linuxových kontajneroch. Vylepšený testovací proces má viesť ku zjednodušeniu známkovania vyučujúcimi a taktiež zlepšeniu výsledkov študentov pri týchto úlohách.   Táto diplomová práca vysvetľuje základy testovania softvéru, pričom sa sústredí na testovanie založené na požiadavkách, poskytuje náhľad do technológie kontajnerov a objasňuje, ako boli tieto témy zahrnuté pri návrhu služby a taktiež, ako sa ich použitie odrazilo na požiadavkách na ňu. Okrem toho je implementácia tejto služby podrobená detailnej analýze, ktorá má slúžiť ako referenčný materiál pre jej akékoľvek budúce rozšírenia.   Implementovaná služba je schopná vykonávať základné operácie, zahřňajúce paralelné testovanie študentských projektov v oddelených kontajneroch, vytvorenie kontajnerizovaného ladiaceho prostredia, alebo automatické zostavenie kontajnerového obrazu pre konkrétne zadanie.

Elasticsearch has evolved from an experimental, open-source, NoSQL database for full-text documents to an easily scalable search engine that canhandle a large amount of documents. This evolution has enabled companies todeploy Elasticsearch as an internal search engine for information retrieval (logs,documents, etc.). Later on, it was transformed as a cloud service and the latestdevelopment allows a containerized, serverless deployment of the application,using Docker and Kubernetes.This research examines the behaviour of the system by comparing the length and appearance of single-term and multiple-terms queries, the scaling behaviour and the security of the service. The application is deployed on Google Cloud Platform as a Kubernetes cluster hosting containerized Elasticsearch images that work as databasenodes of a bigger database cluster. As input data, a collection of JSON formatted documents containing the title and abstract of published papersin the field of computer science was used inside a single index. All the plots were extracted using Kibana visualization software. The results showed that multiple-term queries put a bigger stress on thesystem than single-term queries. Also the number of simultaneous users querying in the system is a big factor affecting the behaviour of the system. By scaling up the number of Elasticsearch nodes inside the cluster, indicated that more simultaneous requests could be served by the system.

In the digital age that we live in today, we are dependent on numerous web applications or services, from dealing with banking, booking air flights, and handling our taxes. We expect these applications and services to support high availability, data loss prevention, and fast response time. Microservices is a design pattern to support faster software change, and it also supports other non-functional attributes such as scalability and high availability. One way to deploy your software as microservices is to use containers and deploy them on a container cluster such as Kubernetes. The public opinion about writing Kubernetes deployment files is that it is complex and repetitive writing. This project aims to see how model-driven development can assist with the creation of the Kubernetes deployment files. To see how model-driven development can assist in the creation of Kubernetes files. The project will implement a domain-specific modeling language for Kubernetes, and the language should be able to model the application's desired states. And by using model transformation, the tool can generate Kubernetes deployable files.

Infrastructure is a common word used to express the basic equipment and structures that are needed e.g.  for a country or organisation to function properly. The same concept applies in the field of computer science, without infrastructure one would have problems operating software at scale. Provisioning and maintaining infrastructure through manual labour is a common occurrence in the "iron age" of IT. As the world is progressing towards the "cloud age" of IT, systems are decoupled from physical hardware enabling anyone who is software savvy to automate provisioning and maintenance of infrastructure. This study aims to determine how a generic environment can be created for applications that can run on Unix platforms and how that underlying infrastructure can be provisioned effectively. The results show that by utilising OS-level virtualisation, also known as "containers", one can deploy and serve any application that can use the Linux kernel in the sense that is needed. To further support realising the generic environment, hardware virtualisation was applied to provide the infrastructure needed to be able to use containers. This was done by provisioning a set of virtual machines on different cloud providers with a lightweight operating system that could support the container runtime needed. To manage these containers at scale a container orchestration tool was installed onto the cluster of virtual machines. To provision the said environment in an effective manner, the principles of infrastructure as code (IaC) were used to create a “blueprint" of the infrastructure that was desired. By using the metric mean time to environment (MTTE) it was noted that a cluster of virtual machines with a container orchestration tool installed onto it could be provisioned under 10 minutes for four different cloud providers.

abstract: Traumatic injuries are the leading cause of death in children under 18, with head trauma being the leading cause of death in children below 5. A large but unknown number of traumatic injuries are non-accidental, i.e. inflicted. The lack of sensitivity and specificity required to diagnose Abusive Head Trauma (AHT) from radiological studies results in putting the children at risk of re-injury and death. Modern Deep Learning techniques can be utilized to detect Abusive Head Trauma using Computer Tomography (CT) scans. Training models using these techniques are only a part of building AI-driven Computer-Aided Diagnostic systems. There are challenges in deploying the models to make them highly available and scalable. The thesis models the domain of Abusive Head Trauma using Deep Learning techniques and builds an AI-driven System at scale using best Software Engineering Practices. It has been done in collaboration with Phoenix Children Hospital (PCH). The thesis breaks down AHT into sub-domains of Medical Knowledge, Data Collection, Data Pre-processing, Image Generation, Image Classification, Building APIs, Containers and Kubernetes. Data Collection and Pre-processing were done at PCH with the help of trauma researchers and radiologists. Experiments are run using Deep Learning models such as DCGAN (for Image Generation), Pretrained 2D and custom 3D CNN classifiers for the classification tasks. The trained models are exposed as APIs using the Flask web framework, contained using Docker and deployed on a Kubernetes cluster. The results are analyzed based on the accuracy of the models, the feasibility of their implementation as APIs and load testing the Kubernetes cluster. They suggest the need for Data Annotation at the Slice level for CT scans and an increase in the Data Collection process. Load Testing reveals the auto-scalability feature of the cluster to serve a high number of requests. / Dissertation/Thesis / Masters Thesis Software Engineering 2020

Background: Nowadays lot of companies are enjoying the benefits of kubernetes by maintaining their containerized applications over it. AWS is one of the leading cloud computing service providers and many well-known companies are their clients. Many researches have been conducted on kubernetes, docker containers, cloud computing platforms but a confusion exists on how to deploy the applications in Kubernetes. A research gap about the impact created by CPU limits and requests while deploying the Kubernetes application can be found. So, through this thesis I want to analyze the performance of the CPU intensive containerized application. It will help many companies avoid the confusion while deploying their applications over kubernetes. Objectives: We measure the scalability of kubernetes under CPU intensive containerized application running over AWS and we can study the impact created by changing CPU limits and requests while deploying the application in Kubernetes. Methods: we choose a blend of literature study and experimentation as methods to conduct the research. Results and Conclusion: From the experiments it is evident that the application performs better when we allocate more CPU limits and less CPU requests when compared to equal CPU requests and CPU limits in the deployment file. CPU metrics collected from SAR and Kubernetes metrics server are similar. It is better to allocate pods with more CPU limits and CPU requests than with equal CPU requests and CPU limits for better performance. Keywords: Kubernetes, CPU intensive containerized application, AWS, Stress-ng.

Containerization has gained popularity for deploying applications in a lightweight environment. Kubernetes and Docker have gained a lot of dominance for scalable deployments of applications in containers. Usually, kubernetes clusters are deployed within a single shared network. For high availability of the application, multiple kubernetes clusters are deployed in multiple regions, due to which the number of kubernetes clusters keeps on increasing over time. Maintaining and managing mul-tiple kubernetes clusters is a challenging and time-consuming process for system administrators or DevOps engineers. These issues can be addressed by deploying a kubernetes cluster in a multi-region environment. A multi-region kubernetes de-ployment reduces the hassle of handling multiple kubernetes masters by having onlyone master with worker nodes spread across multiple regions. In this thesis, we investigated a multi-region kubernetes cluster’s network performance by deploying a multi-region kubernetes cluster with worker nodes across multiple openstack regions and tunneled using wireguard(a VPN protocol). A literature review on the common factors that influence the network performance in a multi-region deployment is conducted for the network performance metrics. Then, we compared the request-response time of this multi-region kubernetes cluster with the regular kubernetes cluster to evaluate the performance of the deployed multi-region kubernetescluster. The results obtained show that a kubernetes cluster with worker nodes ina single shared network has an average request-response time of 2ms. In contrast, the kubernetes cluster with worker nodes in different openstack projects and regions has an average request-response time of 14.804 ms. This thesis aims to provide a performance comparison of the kubernetes cluster with and without wireguard, fac-tors affecting the performance, and an in-depth understanding of concepts related to kubernetes and wireguard.