Migrating Monoliths to Microservices-based Customizable Multi-tenant Cloud-native Apps

It was common that software vendors sell licenses to their clients to use software products, such as Enterprise Resource Planning, which are deployed as a monolithic entity on clients’ premises. Moreover, many clients, especially big organizations, often require software products to be customized for their specific needs before deployment on premises. While software vendors are trying to migrate their monolithic software products to Cloud-native Software-as-a-Service (SaaS), they face two big challenges that this paper aims at addressing: 1) How to migrate their exclusive monoliths to multi-tenant Cloud-native SaaS; and 2) How to enable tenant-specific customization for multi-tenant Cloud-native SaaS. This paper suggests an approach for migrating monoliths to microservice-based Cloud-native SaaS, providing customers with a flexible customization opportunity, while taking advantage of the economies of scale that the Cloud and multi-tenancy provide. Our approach shows not only the migration to microservices but also how to introduce the necessary infrastructure to support the new services and enable tenant-specific customization. We illustrate the application of our approach on migrating a reference application of Microsoft called SportStore.acceptedVersio

Cloud computing : developing a cost estimation model for customers

Cloud computing is an essential part of the digital transformation journey. It offers many benefits to organisations, including the advantages of scalability and agility. Cloud customers see cloud computing as a moving train that every organisation needs to catch. This means that adoption decisions are made quickly in order to keep up with the new trend. Such quick decisions have led to many disappointments for cloud customers and have questioned the cost of the cloud. This is also because there is a lack of criteria or guidelines to help cloud customers get a complete picture of what is required of them before they go to the cloud. From another perspective, as new technologies force changes to the organizational structure and business processes, it is important to understand how cloud computing changes the IT and non-IT departments and how can this be translated into costs. Accordingly, this research uses the total cost of ownership approach and transaction cost theory to develop a customer-centric model to estimate the cost of cloud computing. The Research methodology used the Design Science Research approach. Expert interviews were used to develop the model. The model was then validated using four case studies. The model, named Sunny, identifies many costs that need to be estimated, which will help to make the cloud-based digital transformation journey less cloudy. The costs include Meta Services, Continuous Contract management, Monitoring and ITSM Adjustment. From an academic perspective, this research highlights the management efforts required for cloud computing and how misleading the rapid provision potential of the cloud resources can be. From a business perspective, proper estimation of these costs would help customers make informed decisions and vendors make realistic promises.Cloud Computing ist ein wesentlicher Bestandteil der Digitalisierung. Es bietet Unternehmen viele Vorteile, wie Skalierbarkeit und Agilität. Cloud-Kunden sehen Cloud Computing als einen Zug, auf den jedes Unternehmen aufspringen muss. Das bedeutet, dass Einführungsentscheidungen schnell getroffen werden, um mit dem neuen Trend Schritt zu halten. Solche Schnellschüsse haben zu vielen Enttäuschungen bei Cloud-Kunden geführt und die Kosten der Cloud in Frage gestellt. Dies ist auch darauf zurückzuführen, dass es keine Kriterien oder Leitlinien gibt, die den Cloud-Kunden helfen, sich ein vollständiges Bild davon zu machen, was von ihnen erwartet wird, bevor sie in die Cloud gehen. Aus einem anderen Blickwinkel ist es wichtig zu verstehen, wie Cloud Computing IT- und Nicht-IT-Abteilungen verändert und wie sich dies auf die Kosten auswirkt, da neue Technologien Veränderungen in der Organisationsstruktur und den Geschäftsprozessen erzwingen. Dementsprechend werden in dieser Forschungsarbeit der Total Cost of Ownership-Ansatz und die Transaktionskostentheorie verwendet, um ein kundenorientiertes Modell zur Schätzung der Kosten von Cloud Computing zu entwickeln. Die Forschungsmethodik basiert auf dem Design Science Research Ansatz. Zur Entwicklung des Modells wurden Experteninterviews durchgeführt. Anschließend wurde das Modell anhand von vier Fallstudien validiert. Das Modell mit dem Namen Sunny identifiziert viele Kosten, die geschätzt werden müssen, um die Reise zur digitalen Transformation in der Cloud weniger wolkig zu gestalten. Zu diesen Kosten gehören Meta-Services, kontinuierliches Vertragsmanagement, Überwachung und ITSM-Anpassung. Aus akademischer Sicht verdeutlicht diese Forschung, welcher Verwaltungsaufwand für Cloud Computing erforderlich ist und wie irreführend das schnelle Bereitstellungspotenzial von Cloud-Ressourcen sein kann. Aus Unternehmenssicht würde eine korrekte Einschätzung dieser Kosten den Kunden helfen, fundierte Entscheidungen zu treffen, und den Anbietern, realistische Versprechungen zu machen

Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud

Many SaaS providers nowadays want to leverage the cloud's capabilities also for their existing applications, for example, to enable sound scalability and cost-effectiveness. This thesis provides the approach CloudMIG that supports SaaS providers to migrate those applications to IaaS and PaaS-based cloud environments. CloudMIG consists of a step-by-step process and focuses on two core components. (1) Restrictions imposed by specific cloud environments (so-called cloud environment constraints (CECs)), such as a limited file system access or forbidden method calls, can be validated by an automatic conformance checking approach. (2) A cloud deployment option (CDO) determines which cloud environment, cloud resource types, deployment architecture, and runtime reconfiguration rules for exploiting a cloud's elasticity should be used. The implied performance and costs can differ in orders of magnitude. CDOs can be automatically optimized with the help of our simulation-based genetic algorithm CDOXplorer. Extensive lab experiments and an experiment in an industrial context show CloudMIG's applicability and the excellent performance of its two core components