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Representation selection problem: Optimizing video delivery through caching

机译：表示选择问题：通过缓存优化视频传递

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摘要

To cope with Internet video explosion, recent work proposes to deploy caches to absorb part of the traffic related to popular videos. Nonetheless, caching literature has mainly focused on network-centric metrics, while the quality of users' video streaming experience should be the key performance index to optimize. Additionally, the general assumption is that each user request can be satisfied by a single object, which does not hold when multiple representations at different quality levels are available for the same video. Our contribution in this paper is to extend the classic object placement problem (which object to cache and where) by further considering the representation selection problem (i.e., which quality representation to cache), employing two methodologies to tackle this challenge. First, we employ a Mixed Integer Linear Programming (MILP) formulation to obtain the centralized optimal solution, as well as bounds to natural policies that are readily obtained as additional constraints of the MILP. Second, from the structure of the optimal solution, we learn guidelines that assist the design of distributed caching strategies: namely, we devise a simple yet effective distributed strategy that incrementally improves the quality of cached objects. Via simulation over large scale scenarios comprising up to hundred nodes and hundred million objects, we show our proposal to be effective in balancing user perceived utility vs bandwidth usage.
机译：为了应对Internet视频爆炸，最近的工作建议部署缓存以吸收与流行视频相关的部分流量。尽管如此，缓存文献主要集中在以网络为中心的指标上，而用户视频流体验的质量应该是进行优化的关键性能指标。另外，一般的假设是，每个用户请求都可以由单个对象满足，而当不同质量级别的多个表示可用于同一视频时，该对象将不成立。我们在本文中的贡献是通过进一步考虑表示选择问题（即要缓存哪种质量表示）来扩展经典的对象放置问题（要缓存哪个对象以及在哪里缓存），采用两种方法来应对这一挑战。首先，我们采用混合整数线性规划（MILP）公式来获得集中的最优解，以及作为MILP附加约束而容易获得的自然政策的界限。其次，从最佳解决方案的结构中，我们学习有助于设计分布式缓存策略的准则：即，我们设计一种简单而有效的分布式策略，以逐步提高缓存对象的质量。通过在多达一百个节点和一亿个对象的大规模场景中进行仿真，我们证明了我们的建议可以有效平衡用户感知的效用与带宽使用。

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