The goal with this thesis work has been to identify what a telecommunications company should monitor and to find a network monitoring system that can monitor these identified objects on two different platforms: Windows and Linux. The network monitoring system has been implemented in a telecommunications company’s environment and this thesis presents how the system monitors their environment. The subject for this thesis work is within network monitoring. The problem formulation has been answered by conducting a literature study and by testing network monitoring systems’ features in a lab environment. The sources used in the literature study consists of scientific articles and other articles found on the web. The lab environment consisted of virtual machines that runs Linux or Windows as an operating system.   The purpose of the work was to enlighten Cellip in what objects they should monitor and to help the company to monitor them by implementing a network monitoring system. Cellip is a telecommunications company that provides IP-telephony services through Session Initiation Protocol. The limits of this thesis work are based on what their environment supports in terms of monitoring. Cellip’s environment consists of Linux and Windows servers, Cisco switches and firewalls, and Sonus Session Border Controllers.   In summary, the result of this thesis gives the reader information about what a telecommunications company with a similar environment to Cellip should monitor, what three systems that can monitor these objects, which of the three systems that has most automatized features and finally how the chosen system Datadog monitors and presents the objects. Some of the objects that are important to monitor is: memory, disk storage, latency, packet loss. In conclusion, this thesis presents a monitoring baseline for telecommunication companies with a similar environment to Cellip.

Rapporten beskriver arbetet och resultaten av en jämförelse mellan Sensu och op5 Monitor, vilka är verktyg som används för att övervaka enheter i nätverk, så kallade network monitoring systems. Arbetet har utförts för att utbudet av nätverksövervakningsverktyg ständigt växer och det ansågs värdefullt att jämföra en ny aktör med ett äldre verktyg som är byggd på ett annat tankesätt. Det som ansågs intressant att testa var hur dessa verktyg hanterade de rapporter som skapades och samlades in, om det slutgiltiga resultatet från detta skulle skilja sig åt eller inte. För att testa detta sattes en virtuell testmiljö upp, där Sensu och op5 Monitor rullade parallellt med varandra och övervakade samma system och använde sig utav samma plugin för övervakningen. Experimenten utfördes på två stycken tjänster, BIND9 samt Apache2, i och med att de två pluginen som användes var uppbyggda på olika sätt konstruerades även olika experiment. Under dessa experiment samlades information in om hur de två övervakningsverktygen hanterade de rapporter de fick in, vilket sedan sammanställdes och analyserades. Slutsatsen av det hela var att Sensu och op5 Monitor hanterar sina insamlade rapporter på ett likvärdigt sätt, de rapporterade resultaten blev i samtliga fall detsamma, således fungerade de två övervakningsverktygen på ett jämgott vis. / The report describes the work and results of a comparison between Sensu and op5 Monitor, which are both tools used to monitor devices in a network, more commonly known as network monitoring systems. The subject was chosen due to the fact that there is such a wide range of network monitoring systems, and it is constantly expanding. It was considered valuable to compare a newcomer with an older tool that is built with a different mindset. It was considered interesting to test how these tools handled the reports created and collected by them, to see if the final results from this would differ or not. To test this a virtual testing environment was built, where Sensu and op5 Monitor were run in parallel to each other and monitored the same systems and used the same set of plugins. The experiments were conducted on two services, BIND9 and Apache2, since the plugins were constructed in different ways, so were the various experiments. In these experiments information was gathered about how the two monitoring tools handled the reports they received, which was then compiled and analysed. The conclusion of it all was that Sensu and op5 Monitor handles the collected information in a similar manner. The reported results were in all cases the same, thus the two monitoring tools behave in the same fashion.

With this paper we intended to simplify deployment and management of monitoring agents for a Network Monitoring System. We found interest on the subject since the time consumed to deploy and manage agents was found to be very inefficient. During a lecture with the Swedish based company Op5 AB at the Linnaeus University in Kalmar, Sweden, we presented the complex of problem. The lecturer showed great interest in a solution on the subject and we found it to be a great thesis subject for the Bachelor degree in Computer Science. By the year of 2016 it is expected that the number of network connected devices will grow threefold, there will be four times as much IP traffic and the data storage demand will increase tenfold. [8] This growing demand will also affect the requirement on the Network Monitoring System and in turn the monitoring agents. In this paper we created a baseline, which consisted of a timing regarding the time consumption for manual deployment, configuration and management of the monitoring agents. We also developed an automated way for deployment, configuration and management of monitoring agents by integrating a Content Management Software called Puppet, combined with several scripts. To simplify the management and deployment furthermore a widget was developed for Op5’s Web based User Interface called Ninja. The developed solution was measured against the baseline and a result regarding time consumption was presented. The result fell into a discussion on the subject of automatization and the time savings that it may result in due to less frequent human errors and a less repetitive work processes.