H2020Индивидуална стипендия2020–2022

CORRELATION · Characterization and prediction of service-level traffic for future sliced mobile network

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период: 2020-02-01 → 2022-01-31
Финансиране от ЕС: 224 934 €
Участници: 1
Схема: MSCA-IF-EF-SE

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Накратко на български

Трафикът в мобилните мрежи се анализира, за да се предвиди как се променя натоварването от отделни услуги, като например стрийминг на видео. Това помага за по-доброто проектиране на бъдещите мрежи и динамичното управление на техните ресурси.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Characterization and prediction of service-level traffic for future sliced mobile network

This project focuses on characterization and prediction of the service-level mobile network traffic. The technical objectives of this project are: 1) to depict the spatial-temporal characteristics of individual services, especially for the dominating services in 5G mobile networks, at multi-scales; 2) to design appropriate predicting methods for individual services based on their spatial-temporal characteristics; 3) to reveal the hidden correlations of traffic patterns among diverse services; and 4) to improve the prediction accuracy for service-level traffic based on inter-service correlations and investigate whether we can forecast diverse services’ traffic via the historical records of only a few key services. The achievement of the above objectives will influence the architecture design of future mobile networks and revolutionize how operators will tailor the network slices dynamically. The project is timely. 1) Network slicing is an important trend of future mobile networks and the orchestration of network slices is highly depended on the fluctuations of individual services; 2) Characterization and prediction for service-level mobile traffic with BDA techniques is still a nascent research field; 3) With current prosperity of artificial intelligence, the emerging machine learning tools are offering brand-new opportunities for service-level mobile traffic analysis and forecast.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

Network slicing is a key enabling technology for the 5th generation (5G) and beyond mobile networks. Network slicing allows the creation of multiple logical network instances on the same underlying physical network. Slices can then be formed or combined on-demand, with parameters optimized according to various service requirements so as to meet the users’ instant requests for specific mobile services. Hence, the performance of network slicing heavily depends on characterizing and predicting the spatial- temporal traffic patterns for individual services in near real-time. Research on characterization and prediction for service-level mobile traffic is still in nascence. Firstly, the traffic characteristics and predicting methods of individual services, especially the 5G services, have not been studied adequately. Secondly, traffic correlation among different services and the reasons behind it have not been well studied. Thirdly, inter-service correlations have not been well exploited in service-level mobile traffic prediction. In this project, we will address these gaps. Firstly, we will study the spatial-temporal characteristics of service-level traffic patterns at multi-scales, based on which, we will investigate the traffic predicting frameworks for individual services. Secondly, for the first time, we will investigate the traffic correlation among different services and try to discover the underlying reasons by analyzing the service usage profiles of different user groups. Finally, based on inter-service correlations, we will investigate whether we can improve service-level traffic prediction accuracy and whether we could execute prediction for diverse services according to the historical records of only a few key services.The success of the CORRELATION project will make proactive network slicing possible, which will then drive proactive network optimisation for 5G and beyond mobile networks.

Оригинален текст от CORDIS (на английски).

Участници

RANPLAN WIRELESS NETWORK DESIGN LTD · CAMBRIDGEКоординаторОбединеното кралство