Ericsson offers the option for 5G Radio Access Networks to be deployed on virtualized platforms in cloud environment.
The Ericsson virtualized solution for user plane processing is called vPP and its primary purpose is to deliver end-user data at rates fit for the bandwidth need in the 5G solutions. The end-user data is supplied via a network of Radio Nodes, where the vPP is providing just-in-time delivery based on flow-control algorithms. Evaluating the hardware resources required for vPP depending on end-user behavior is a challenge that requires an enhanced understanding of the impact from the end-user behavior and the flow-control towards Radio Nodes on the hardware resources.
The following steps are envisioned as part of the thesis work:
· Investigate how end-user traffic patterns should be parameterized to be a suitable input to predicting vPP resource utilization.
· Identify and implement a model (preferably in Python) for the HW related resources consumed by a vPP for processing end-user data and executing the flow control to Radio Nodes based on the parameterized traffic model. The HW related resources may possibly be CPU time, memory use, or perhaps something completely different that the master thesis identifies.
· Perform model validation/benchmark against known product performance by verifying the model with an Ericsson 5G NSA (EN-DC) vPP deployment.
· The finding from the study will influence how similar tasks are solved for a wide spectra of Ericsson Radio Access Network solutions.
This project is targeting students in computer science, electrical engineering or similar fields.
Extent: 1-2 students, 30hp each
Location: Ericsson AB Mjärdevi, Linköping
Preferred Starting Date: Spring 2020
Keywords: Cloud, x86, C++, Python, Mobile Telecommunication, Optimization
Last day of application 31st of October 2019
For any queries please contact recruiter Sarah Lashari, email@example.com
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Primary country and city: Sweden (SE) || || Linköping || R&D
Req ID: 300278