Leveraging Big Data Analytics for Cache-Enabled Wireless Networks
| Autor: | Alper Karatepe, Manhal Abdel Kader, Merouane Debbah, Mehdi Bennis, Engin Zeydan, Ejder Bastug, Ahmet Salih Er |
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| Přispěvatelé: | Large Networks and Systems Group (LANEAS), CentraleSupélec, Centre for Wireless Communications [University of Oulu] (CWC), University of Oulu, AveaLabs, Istanbul, Mathematical and Algorithmic Sciences Lab [Paris], Huawei Technologies France [Boulogne-Billancour] |
| Jazyk: | angličtina |
| Rok vydání: | 2015 |
| Předmět: |
proactive caching
Wireless network Computer science business.industry ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS Big data Cloud computing Backhaul (telecommunications) [STAT]Statistics [stat] Base station [SPI]Engineering Sciences [physics] machine learning big data 5G cellular networks Wireless Cache [MATH]Mathematics [math] business content popularity estima-tion 5G Computer network |
| Zdroj: | Proceedings of the IEEE Global Communications Conference IEEE Global Communications Conference (GLOBECOM) IEEE Global Communications Conference (GLOBECOM), Dec 2015, San Diego, United States. ⟨10.1109/glocomw.2015.7414014⟩ GLOBECOM Workshops |
| DOI: | 10.1109/glocomw.2015.7414014⟩ |
| Popis: | International audience; While 5G wireless networks are expected to handle the ever growing data avalanche, classical deployment/optimiza-tion approaches such as hyper-dense deployment of base stations or having more bandwidth are cost-inefficient, and are therefore seen as stopgaps. In this regard, context-aware approaches which exploits human predictability, recent advances in storage, edge/cloud computing and big data analytics are needed. In this article, we approach this problem from a proactive caching perspective where gains of cache-enabled base stations in 5G wireless are studied. In particular, huge amount of real data from a telecom operator in Turkey is collected/processed on a big data platform, and an analysis is carried out for content popularity estimation for caching, aiming to improve users' experience in terms of request satisfactions and offloading the backhaul. Subsequently, with this mobile traffic data collected from many base stations within several hours of time interval and the estimation of content popularity via machine learning tools, we investigate the gains of proactive caching via numerical simulations. The results show that proactive caching fulfils 100% of user request satisfaction and offloads 98% of the backhaul, in a setting of 16 base stations with 15.4 Gbyte of storage size (87% of the total catalog size) and 10% of content ratings. |
| Databáze: | OpenAIRE |
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