This thesis is focused on developing software with ITSM integration features. The aim of this work is to design a suitable software architecture for integration applications in a cloud environment. Due to the use of the solution for many customers, emphasis is placed on sustainability and reusability. After wide technology analysis, a web application has been created that is fully configurable to meet a wide range of customer needs. It is decided to use the Node.js environment and the Loopback 4 framework for application development. To ensure lower costs for the operation of cloud resources, compared to competing technologies such as Java. The entire system is deployed in IBM Cloud. The resulting application is put into production by IBM.

Introduction: Nowadays, the data generated by global communication systems is enormously increasing.  There is a need by Telecommunication Industries to monitor and manage this data generation efficiently. Apache Cassandra is a NoSQL database that manages any formatted data and a massive amount of data flow efficiently.  Aim: This project is focused on developing a new random compaction strategy and evaluating this random compaction strategy's performance. In this study, limitations of generic compaction strategies Size Tiered Compaction Strategy and Leveled Compaction Strategy will be investigated. A new random compaction strategy will be developed to address the limitations of the generic Compaction Strategies. Important performance metrics required for the evaluation of the strategy will be studied. Method: In this study, a grey literature review is done to understand the working of Apache Cassandra, different compaction strategies' APIs. A random compaction strategy is developed in two phases of development. A testing environment is created consisting of a 4-node cluster and a simulator. Evaluated the performance by stress-testing the cluster using different workloads. Conclusions: A stable RCS artifact is developed. This artifact also includes the support of generating random threshold from any user-defined distribution. Currently, only Uniform, Geometric, and Poisson distributions are supported. The RCS-Uniform's performance is found to be better than both STCS and  LCS. The RCS-Poisson's performance is found to be not better than both STCS and LCS. The RCS-Geometric's performance is found to be better than STCS.

This master thesis analyses and investigates how to speed up the deployment of a suite of services to a Platform as a Service. The project use IBM’s Cloud Pak for Applications together with Red Hats OpenShift to provide insights on important factors that influences the deployment process. In this thesis, a modification was done on the installer such that the deployment instructions were sent in parallel instead of sequentially. Except for the parallel suggestion, this thesis also investigates different options on how constraints could be applied to the CPU and what the consequences are. At the end of this report, the reader will also see how the deployment times are affected by cluster scaling. An implementation of the parallel deployment showed that the installation time of Cloud Pak for Applications could be decreased. It was also shown that the CPU was not utilized fully and that there exists significant CPU saturation during the deployment. The evaluation of the scaling analysis showed that, in regards of this thesis, it is more beneficial both timewise and cost-wise to scale horizontally rather than vertically.