Dynamic cache rental and content caching in elastic wireless CDNs

Jeongho Kwak, G. Paschos, G. Iosifidis
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引用次数: 20

Abstract

With elastic CDNs, content providers can rent cache space on demand at different cloud locations in order to enhance their offered quality of service (QoS). This paper addresses a key challenge in this context, namely how to invest an available budget in cache space in order to match spatio-temporal fluctuations of file demand and storage price. Specifically, we consider jointly dynamic cache rental, file placement, and request-cache association in a wireless scenario in order to provide a just-in-time CDN service. The objective is to maximize the benefit in average download delay obtained by the rented caches, while ensuring that the time-average rental cost is less than a fixed budget. We leverage a Lyapunov drift-minus-benefit technique to transform our infinite horizon problem into day-by-day subproblems which can be solved without knowledge of distant future file popularity and transmission rates. For the case of non-overlapping small cells (also wired case) we provide an efficient subproblem solution, referred to as JCC. However, in the general overlapping case, the subproblem becomes a mixed integer non-linear program (MINLP). In this case, we employ a dual decomposition method to derive a scalable solution, namely the JCCA algorithm. Finally, via extensive simulations, we reveal that the proposed JCCA algorithm attains 82.66 % higher delay benefit than existing static cache storage-based algorithms when available average cache budget is 20% of entire file library; moreover, the benefit becomes higher as the average cache budget gets tighter.
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弹性无线cdn中的动态缓存租用和内容缓存
有了弹性cdn,内容提供商可以在不同的云位置按需租用缓存空间,以增强其提供的服务质量(QoS)。本文解决了在此背景下的一个关键挑战,即如何在缓存空间上投入可用预算,以匹配文件需求和存储价格的时空波动。具体地说,我们在无线场景中联合考虑动态缓存租赁、文件放置和请求-缓存关联,以提供及时的CDN服务。目标是最大化租用缓存获得的平均下载延迟的好处,同时确保时间平均租用成本小于固定预算。我们利用Lyapunov漂移-负收益技术将我们的无限视界问题转化为每天的子问题,这些子问题可以在不知道遥远未来的文件流行度和传输速率的情况下解决。对于不重叠的小单元(也是连接的情况),我们提供了一种有效的子问题解决方案,称为JCC。然而,在一般的重叠情况下,子问题变成了一个混合整数非线性规划(MINLP)。在这种情况下,我们采用对偶分解方法来推导可扩展的解决方案,即JCCA算法。最后,通过大量的仿真,我们发现当可用的平均缓存预算为整个文件库的20%时,所提出的JCCA算法比现有的基于静态缓存存储的算法获得了82.66%的延迟效益;此外,随着平均缓存预算越来越紧,收益也会越来越高。
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