Content caching based on mobility prediction and joint user Prefetch in Mobile edge networks
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Content caching based on mobility prediction and joint user Prefetch in Mobile edge networks Genghua Yu 1 & Jia Wu 1 Received: 25 July 2019 / Accepted: 25 June 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the development of 5G mobile networks, people’s demand for network response speed and services has increased to meet the needs of a large amount of data traffic, reduce the backhaul load caused by frequently requesting the same data (or content). The file is pre-stored in the base station by the edge device, and the user can directly obtain the requested data in the local cache without remotely. However, changes in popularity are difficult to capture, and data is updated more frequently through the backhaul. In order to reduce the number of backhauls and provide caching services for users with specific needs, we can provide proactive caching with users without affecting user activity. We propose a content caching strategy based on mobility prediction and joint user prefetching (MPJUP). The policy predicts the prefetching device data by predicting the user’s movement position with respect to time by the mobility of the user and then splits the partial cache space for prefetching data based on the user experience gain. Besides, we propose to reduce the backhaul load by reducing the number of content backhauls by cooperating prefetch data between the user and the edge cache device. Experimental analysis shows that our method further reduces the average delay and backhaul load, and the prefetch method is also suitable for more extensive networks. Keywords Mobile edge cache . Popularity-based caching . Backhaul . Mobility predictions . Prefetching data
1 Introduction With the development of mobile internet technology, a variety of mobile applications and multimedia services enrich people’s lives [1], but also generate huge mobile network traffic, these applications and services rely heavily on high-rate and low-latency data transmission. According to the 2017 Cisco VNI Technical Report [3], global mobile data traffic will reach 587 EB by 2021, which is equivalent to 122 times in 2011. From 2016 to 2021, mobile video will grow by 8.7 times, accounting for 78% of total mobile traffic. The rapid growth of mobile network traffic, especially mobile video traffic, has This article is part of the Topical Collection: Special Issue on Emerging Trends on Data Analytics at the Network Edge Guest Editors: Deyu Zhang, Geyong Min, and Mianxiong Dong * Jia Wu [email protected] 1
School of computer science and engineering, Central South University Chang Sha, Hu Nan 410075, China
brought tremendous pressure and challenges to the current mobile network. In the 5G network era, users’ demand for data has grown like never before. The proliferation of mobile network traffic has made bandwidth resources very tight. At the same time, the current end-to-end transmission mechanism causes a large amount of content to be repeatedly transmitted [2], resulting in a waste of network resources. In order
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