دانلود رایگان مقاله پرداختن به تقاضای niche بر اساس پیش بینی تحرک مفصل و ذخیره محبوبیت محتوا

Abstract

We present an efficient mobility-based proactive caching model for addressing niche mobile demand, along with popularity-based and legacy caching model extensions. Opposite to other proactive solutions which focus on popular content, we propose a distributed solution that targets less popular, personalised or dynamic content requests by prefetching data in small cells based on aggregated user mobility prediction information. According to notable studies, niche demand, particularly for video content, represents a significant 20–40% of Internet demand and follows a growing trend. Due to its novel design, our model can directly address such demand, while also make a joint use of content popularity information with the novelty of dynamically tuning the contribution of mobility prediction and content popularity on local cache actions. Based on thorough performance evaluation simulations after exploring different demand levels, video catalogues and mobility scenarios including human walking and automobile mobility, we show that gains from mobility prediction can be high and able to adapt well to temporal locality due to the short timescale of measurements, exceeding cache gains from popularity-only caching up to 41% for low caching demand scenarios. Our model’s performance can be further improved at the cost of an added computational overhead by adapting cache replacements by, e.g. in the aforementioned scenarios, 41%. Also, we find that it is easier to benefit from requests popularity with low mobile caching demand and that mobility-based gains grow with popularity skewness, approaching close to the high and robust gains yielded with the model extensions.

7. Conclusions and future work

We present a novel Efficient Mobility-based Caching (EMC) distributed model along with content popularity and legacy caching model extensions. Our solution has significant design advantages over other proactive approaches, the most important of which lie in its ability (i) to address niche mobile demand, (ii) to dynamically tune the contribution of mobile requests’ popularity and users’ mobility prediction on cache actions, and (iii) to take on-the-fly cache decisions based on contemporary, short timescale local mobility information. By design, our approach targets less popular or personalised content that is unaddressed by other proactive approaches in literature and CDNs, and can be applied to heterogeneous wireless network environments in order to yield monetary and delay cost gains for users with positive implications on QoE. According to credible studies and network forecast reports, such niche content requests represent 20–40% of Internet demand, with mobile video in particular following the growing popularity trend of personalised videos published in social networks. As we discuss, even if 60–80% of video demand continuous to account for popular content in the future, still a significant 20–40% will refer to niche videos. To our knowledge, our approach is the most appropriate for complementing CDNs because it is the only one that intercepts requests for niche content which is otherwise missed due the providence of CDNs exclusively for popular objects.