Network monitoring using Big Data technologies

OData support
Supervisor:
Kocsis Imre
Department of Measurement and Information Systems

In this day and age there is an increasing need for a faster mobile network connection and wider accessibility. Combine this with an ever-growing number of mobile subscribers you get a rapidly scaling mobile industry. Before the Smartphone Era most of the users wanted solid call quality and good network coverage. In contrast, nowadays more and more people want High-Speed Internet access on-the-go, and along with this, high definition multimedia streaming is quickly gaining popularity on mobile devices. This new demand sets requirements for low latencies and high data throughputs, which were answered by LTE, a new wireless communication standard. LTE was designed for high-speed data connections, but its new approach introduced a new task too: the voice call network had to be reengineered to support voice over LTE, or VoLTE. It is now getting deployed in multiple areas, but – as with every other new technology –, it needs testing. Considering the sheer data size generated in a live network the conventional methods are slow for an online analyzation.

The topic of this thesis is to design a new, more effective replacement component for a network analyzer application. Because of its approach the old solution doesn’t scale well, and it is unable to analyze an online network, which is a new requirement. Considering the data size, the need for stream data processing (online network analyzation) and wish for better scalability the new component is based on Big Data analyzation methods.

In my thesis, I collect the needed network knowledge for designing the replacement component, I compare the available Big Data frameworks and choose the most suitable for the application on hand. To walk the talk I implement a small prototype with which I measure the speed and scalability of the new component.

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