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• Parastar, Paniz; Caso, Giuseppe; Iglesias, Jesus Alberto Omaña; Lutu, Andra & Alay, Ozgu (2024). Rethinking the mobile edge for vehicular services. Computer Networks. ISSN 1389-1286. 253. doi: 10.1016/j.comnet.2024.110687. Full text in Research Archive
• Parastar, Paniz; Lutu, Andra; Erduran, Özgü Alay; Caso, Giuseppe & Perino, Diego (2023). Spotlight on 5G: Performance, Device Evolution and Challenges from a Mobile Operator Perspective. In Su, Lu & Pathak, Parth (Ed.), IEEE INFOCOM 2023 - IEEE Conference on Computer Communications. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9798350334159. doi: 10.1109/INFOCOM53939.2023.10229108. Full text in Research Archive Show summary

  Fifth Generation (5G) has been acknowledged as a significant shift in cellular networks, expected to run significantly different classes of services and do so with outstanding performance in terms of low latency, high capacity, and extreme reliability. Managing the resulting complexity of mobile network architectures will depend on making efficient decisions at all network levels based on end-user requirements. However, to achieve this, it is critical to first understand the current mobile ecosystem and capture the device heterogeneity, which is one of the major challenges for ensuring the successful exploitation of 5G technologies.In this paper, we conduct a large-scale measurement study of a commercial mobile operator in the UK, focusing on bringing forward a real-world view on the available network resources, as well as how more than 30M end-user devices utilize the mobile network. We focus on the current status of the 5G Non-Standalone (NSA) deployment and the network-level performance and show how it caters to the prominent use cases that 5G promises to support. Finally, we demonstrate that a fine-granular set of requirements is, in fact, necessary to orchestrate the service to the diverse groups of 5G devices, some of which operate in permanent roaming.

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• Parastar, Paniz; Alay, Ozgu; Lutu, Andara & Caso, Guiseppe (2026). Efficient Deployment and Orchestration of Heterogeneous Multi-Access Edge Computing in 5G and Beyond Networks. Universitetet i Oslo. Full text in Research Archive Show summary

  The evolution of cellular networks towards 5G and beyond has introduced unprecedented opportunities and challenges for supporting heterogeneous and low-latency services. These services range from latency-critical like autonomous driving to bandwidth-intensive video streaming. Their stringent requirements for reliability, latency, and scalability call for computation and storage capabilities closer to end-users, where Multi-Access Edge Computing (MEC) emerges as a key enabling technology. This dissertation investigates how MEC can be effectively integrated into next-generation networks to support latency-critical services like vehicular services-as one of the most demanding use cases-, addressing the intertwined challenges of MEC deployment and orchestration. This thesis begins by empirically evaluating commercial 5G deployments to provide a realistic foundation for subsequent designs. Using operator-side data, we characterize the performance of 5G relative to 4G infrastructures and uncover the rapid growth of heterogeneous device populations, including connected cars and smart meters. These findings highlight the urgent need for differentiated approaches to network management that go beyond smartphone centric assumptions. Building on these insights, the second part of this work develops a practical, multi-tier MEC deployment framework tailored to vehicular services. Leveraging real-world datasets, we formulate cost- and latency-aware deployment strategies that exploit existing mobile network infrastructure while ensuring service-level objectives for diverse vehicular services. The proposed approach demonstrates the benefits of multi-tier over conventional single-tier deployments. Building on top of multi-tier MEC framework, the third part of this thesis addresses online orchestration by focusing on energy-efficient task computation at the edge. Using real-world vehicular mobility traces, we design strategies that jointly minimize energy consumption at both vehicles and MEC servers while meeting strict latency constraints. We introduce complementary solutions for static and mobility-influenced vehicular scenarios, showing significant improvements in energy efficiency compared to state-of-the-art baselines

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