期刊
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
卷 36, 期 8, 页码 1786-1801出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2018.2844681
关键词
Content prefetching; vehicular ad-hoc networks (VANETs); mobility prediction; content popularity estimation; over-the-top services; road side units (RSUs); edge computing
资金
- SNSF SwissSenseSynergy Project [154458]
- COST Action [CA 15127]
- FCT/MEC [CMUP-ERI/TIC/0010/2014, UID/EEA/50008/2013, POCI-010247-FEDER-017738]
Content prefetching brings contents close to end users before their explicit requests to reduce the content retrieval time, which is crucial for mobile scenarios, such as vehicular adhoc networks (VANETs). In order to make intelligent prefetching decisions, three questions have to be answered: which content should be prefetched, when and where it should be prefetched. This paper answers these questions by proposing a vehicle mobility prediction-based over-the-top (OTT) content prefetching solution. We proposed a vehicle mobility prediction module to estimate the future connected roadside units (RSUs) using data traces collected from a real-world VANET testbed deployed in the city of Porto, Portugal. We designed a multi-tier caching mechanism with an OTT content popularity estimation scheme to forecast the content request distribution. We implemented a learning-based algorithm to proactively prefetch the user content to VANET edge caching at RSUs. We implemented a prototype using Raspberry Pi emulating RSU nodes to prove the system functionality. We also performed large-scale OpenStack experiments to validate the system scalability. Extensive experiment results prove that the system can bring benefits for both end-users and OTT service providers, which help them to optimize network resource utilization and reduce bandwidth consumption.
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