Data Logging is the main activity to be considered in maintaining a server or database in working condition without any errors or failures. Data collection can be automatic, so, no human presence is necessary. To store the data of logs for many days and visualizing became a huge problem in recent days. Coming to node SGSN-MME, which is the main component of the GPRS network, which handles all packet switched data within the mobile operator's network. A lot of log data is generated and stored in file systems on the redundant File Server Boards in SGSN-MME node. The evolution of the SGSN-MME is taking it from dedicated, purpose-built, hardware into virtual machines in the Cloud, where virtual file server boards fit very badly. The purpose of this thesis is to give a better way to store the log data and add visualization using the ELK stack concept. Fetching useful information from logs is one of the most important part of this stack and is being done in Logstash using its grok filters and a set of input, filter and output plug-ins which helps to scale this functionality for taking various kinds of inputs ( file,TCP, UDP, gemfire, stdin, UNIX, web sockets and even IRC and twitter and many more) , filter them using (groks,grep,date filters etc.)and finally write output to ElasticSearch. The Research Methodology involved in carrying out this thesis work is a Qualitative approach. A study is carried using the ELK concept with respect to Legacy approach in Ericsson company. A suitable approach and the better possible solution is given to the vSGSN-MME node to store the log data. Also to provide the performance and uses multiple users of input providers and provides the analysis of the graphs from the results and analysis. To perform the tests accurately, readings are taken in defined failure scenarios. From the test cases, a plot is provided on the CPU load in vSGSN-MME which easily gives the suitable and best promising way.

The purpose of this thesis is to investigate and implement a framework for log management and analysis tailored to the treatment planning system (TPS) RayStation. A TPS is a highly advanced software package used in radiation oncology clinics, and the complexity of the software makes writing robust code challenging. Although the product is tested rigorously during development, bugs are present in released software. The purpose of the the framework is to allow the RayStation development team insight into errors encountered in clinics by centralizing log file data recorded at clinics around the world. A framework based on the Elastic stack, a suite of open-source products, is proposed, addressing a set of known issues described as the access problem, the processing problem, and the analysis problem. Firstly, log files are stored locally on each machine running RayStation, some of which may not be connected to the Internet. Gaining access to the data is further complicated by legal frameworks such as HIPAA and GDPR that put constraints on how clinic data can be handled. The framework allows for access to the files while respecting these constraints. Secondly, log files are written in several different formats. The framework is flexible enough to process files of multiple different formats and consistently extracts relevant information. Thirdly, the framework offers comprehensive tools for analyzing the collected data. Deployed in-house on a set of 38 machines used by the RayStation development team, the framework was demonstrated to offer solutions to each of the listed problems.