The Big Enterprises all over the world are setting up their services in cloud as this is cheaper and offers a lot of other benefits. These services have to be updated from time to time and for this, the enterprises have to upgrade their systems in the cloud. During these upgrades, the enterprises face a lot of problems known as compatibility issues. The companies are investing big time to avoid these compatibility issues. The investment could be in money, time, labor etc. also known as effort. Therefore, it is in our interest to attempt to reduce this effort. In this study, our main objectives are to calculate the effort required to maintain the backward compatibility during the upgrade process in the cloud and to find ways to reduce this effort. Reducing the effort will help the companies cut down their investment. A hypothesis was introduced saying that the network usage was dependent on the upgrade method chosen. We have chosen experimentation to be the suitable research method. To run our experiments, we have created a virtual environment similar to that of Ericsson. The experimental values were recorded. We recorded the values such as code complexity, total time for the upgrade process and the network usage during the experiment. Using these values, we tried to estimate the effort and scale it to real time scenarios. Using ANOVA we proved that our null hypothesis was correct. The results have then been discussed in detail and RQ1 is answered. Later RQ2 is answered based on the answer for RQ1. Through our analysis we were able to get a rough estimate of the effort (labor, time, cost) required to maintain backward compatibility. We propose that the existence of the tool mentioned will reduce the effort considerably. The features of the tool are explained in detail. Some future work as the actual development of the tool has been suggested.

The goal of this diploma thesis is to design and develop a system for collecting, processing and storing data from large IoT networks. The developed system introduces a complex solution able to process data from various IoT networks using Apache Hadoop ecosystem. The data are real-time processed and stored in a NoSQL database, but the data are also stored  in the file system for a potential later processing. The system is optimized and tested using data from IQRF network. The data stored in the NoSQL database are visualized and the system periodically generates derived predictions. Users are connected to this system via an information system, which is able to automatically generate notifications when monitored values are out of range.