Browsar Examensarbeten för masterexamen // Master Theses efter Ämnesord "5G"
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- Post5G User Plane Load Simulator(2021) DÜSTERDIECK, OLOF; HUDA, TASDIKUL; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Papatriantafilou, Marina; Duvignau, RomaricAs the commercial introduction of 5G networks is getting closer, the deployment of testing technology able to perform at the requirements specified for 5G, is of utmost importance. As a company on the leading edge of 5G deployment, Ericsson manages mobile traffic by developing a gateway known as the Evolved Packet Gateway (EPG). EPG is the main component responsible for bridging the gap for data packets between 5G base stations and servers on the Internet. Following the 5G design, EPG is split into a Control Plane (CP) and a User Plane (UP). The purpose of this thesis is to implement a 5G network simulator that is able to load test a physical 5G User Plane which is used to route traffic in a mobile network. The simulator is able to simulate traffic in both the uplink and downlink direction as well as sending traffic on several different user sessions. To simulate traffic, three different traffic models, steady-rate, step-wise rate and Poisson processes, are used. In each simulation, the traffic is stateless and follows a client-to-server architecture. All parts included in this architecture is simulated, except for the UP. To analyze the results of a test, we define several different performance metrics such as throughput and latency. These are evaluated using evaluation techniques such as Control Charts. Several sets of experiments are performed in which we verify the implemented load types and measure the maximum rate of both the simulator and the UP. Using these results, it is possible to analyze the scalability of the UP and our simulator. We conclude that our simulator performs above the required rate to load test the UP for most scenarios with differing number of users and flows per each user. Where this is not the case, ideas and suggestions on how the simulator can be enhanced additionally are given. We also conclude that the UP scales very well with an increased number of users and that it performs above the proposed 5G requirements.
- PostDetecting Network Partitioning in Cloud Native 5G Mobile Network Applications(2022) Bergström, Herman; Fredriksson, Oskar; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Ali-Eldin Hassan, Ahmed; Duvignau, RomaricWith the transition of the 5G core network to a cloud native service-based architecture—composed of network functions operating through microservices communicating over the network—there is an increased risk of network failures causing service downtime unrelated to the applications themselves. In particular, cases of partial and simplex network partitionings have been observed in production systems to produce silent failures causing severe symptoms. Thus, diagnosing these failures have proven difficult. As such, the need of monitoring the network between microservices is of particular interest. In this thesis, we devise a distributed monitoring scheme to identify and classify network partitionings in a Kubernetes cluster. We implement and evaluate two approaches of this scheme based on both active and passive monitoring. While both approaches are feasible for our purpose, we conclude that our approach to passive monitoring struggles with classifying simplex partitions due to TCP being a two-way protocol. Similarly, operating the passive mode requires privileges not necessarily suitable for a shared cloud environment. While the active monitoring scheme is able to infer all types of partitions, it will—unlike the passive alternative—increase the overall load on the network. We further present how to make our proof-of-concept implementation scalable when deployed in larger clusters.
- PostEvaluating RPC for Cloud-Native 5G Mobile Network Applications(2020) Kraft, Hanna; Johansson, Rasmus; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Massimiliano Gulisano, Vincenzo; Duvignau, RomaricThis thesis investigates the communication between services in 5G network functions. The development of the 5G Core (5GC) is by design increasing the amount of communication needed in the control plane. The reason for this is the migration to the cloud and the adoption of a microservices architecture. The telecommunications domain sets strict requirements on performance, which implies the need for the implementation of inter-service communication to be carefully constructed. This thesis evaluates the use of Remote Procedure Call (RPC) as inter-service communication in a 5GC network function. The purpose is to evaluate whether RPC frameworks will fulfill the requirements of inter-service communication and the strict requirements on telecom applications. The frameworks evaluated are gRPC and Apache Thrift. We also compare the frameworks to a TCP solution since this is the approach currently considered for this use case and a solution with minimal overhead to the communication. The evaluation is both quantitative, with benchmarks on latency, throughput and CPU usage, and qualitative where qualities such as availability and ease of development are evaluated. From the evaluation, we can conclude that using RPC frameworks would suit most needs. Even if the evaluated RPC frameworks perform slightly worse than a reference TCP solution in the quantitative evaluation, they can provide many other benefits such as bidirectional streaming RPC and highavailability features. Among the evaluated RPC frameworks, Apache Thrift stands out slightly in terms of performance, while gRPC stands out in the qualitative evaluation.
- PostIntegrating Programmable Smart-NICs into Industrial Packet-Processing Systems(2021) Blomkvist, Lina; Svensson, Tove; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Papatriantafilou, Marina; Duvignau, RomaricIn order to cope with the requirements of 5G, Smart Network Interface Controllers are being used to offload general use CPUs. For them to be able to become more widely used, research of how to integrate them into already existing industrial systems and the cost of such a transition is needed. This thesis presents the method of integrating a P4 programmed Netronome Agilio Smart Network Interface Controller (SNIC) into a high speed industrial packet processing pipeline. A partition of the industrial system handling packet classification is translated and implemented and run on the SNIC and tested and compared to the original program performing the same task. The effect on performance is analysed and a qualitative evaluation of the process conducted. The challenges faced in this project consisted of understanding the industrial system environment and how to seamlessly translate the code without losing the original functionality.
- PostPerceived Software Engineering Challenges Facing the Truck Manufacturing Industry in the 5G Era(2021) AY, KONSTANTIN; ZDJELAR, SOFIJA; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Horkoff, Jennifer; Knauss, Eric[Context] A fourth wave of technological advancement known as Industry 4.0 is dawning upon us. Blurring the boundaries between the physical and the virtual worlds, Industry 4.0 will create smart factories that have many beneficial outcomes in terms of productivity, efficiency, flexibility, and profitability. Be that as it may, Industry 4.0 needs 5G to enable all its promises. The role of Software Engineering is indisputable in Industry 4.0. Given that the manufacturing industry is undergoing a major technological shift with the introduction of 5G, it is necessary to investigate whether manufacturers need to change their current Software Engineering practices in order to successfully adopt Industry 4.0 in the 5G era. [Objective] This study aimed to identify challenges that a truck manufacturer may face during the implementation of a 5G-enabled Industry 4.0 use case given their current practices. It also aimed to examine whether the identified challenges puts the case company in such a position that it needs to introduce new practices in order to take full advantage of the opportunities presented by said use case. [Method] A qualitative exploratory case study was conducted in which interviews constituted data collection. Literature also played a central role: Industry 4.0 challenges that were identified in related work helped determine if the challenges identified in this study were unique enough to require further research and new solutions. [Results/Conclusion] This study found that Industry 4.0 projects in the 5G era will be multi-vendor projects that have strict requirements on system robustness, interoperability, and security. This will reportedly be challenging for the truck manufacturer in question to achieve in view of their current practices and technologies. These challenges do not differ significantly from Industry 4.0 projects unrelated to 5G, meaning that not many of the challenges identified in this study were unique enough to require further research and new solutions. Still, this study compiled Software Engineering guidelines for adopting elements of Industry 4.0 in the 5G era as painlessly as possible.
- PostTraffic Classification of 5G Packet Traces(2023) Tesen Marañon, Jose Armando; Chalmers tekniska högskola / Institutionen för data och informationsteknik; Chalmers University of Technology / Department of Computer Science and Engineering; Pathan, Risat; Duvignau, RomaricWith the usage of mobile phones, privacy concerns have been a long-standing issue, and the recent advancement of 5G technology has only amplified these concerns. While encryption is a crucial method for protecting user privacy, studies indicate that machine learning techniques can identify web and mobile applications even though the traffic is encrypted. To explore this problem, this project aims to investigate the potential for identifying mobile applications during encrypted communication on a 5G network. The project utilizes three machine learning models, namely k-Nearest Neighbors (k-NN), Random Forest, and Long Short-Term Memory (LSTM). To achieve this goal, various factors are analyzed, including the type of traffic, packet size, and timing information, to identify specific mobile applications. This project’s results show that it is possible to identify an app over a 5G network with an accuracy of 85% approximately, raising privacy concerns on communications over a 5G network. Under this context, this job updates the current State of Art regarding the private communications over an encrypted network, showing how privacy is vulnerable in 5G networks.