经济高效的大数据共享：i-Cloud
Economical and efficient big data sharing with i-Cloud
- School of Information Technology, Sripatum University（斯里帕坦大学信息技术学院）
- Computer Engineering Department Kasetsart University（凯斯里特大学计算机工程系）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对云大数据共享中的网络拥塞和高额数据传出费用，提出i-Cloud云缓存机制，节省14.78%成本，并在命中率、延迟等方面优于现有方法。
AI中文摘要：
大数据可以托管在云上，并通过云服务以前所未有的规模、多样性和速度进行分布式共享。这不仅导致云网络拥塞和云服务延迟，还增加了公共云的数据传出费用。客户端云缓存缓解了这些问题。此外，当大数据存储在使用不同公共云提供商构建的混合云中时，云缓存必须意识到非均匀的数据传出成本。将i-Cloud方法作为云缓存的核心机制，根据我们的代表性场景，每年可节省高达14.78%的数据传出成本，即每年节省4,425美元，并实现了17.24%的字节命中率、17.96%的延迟节省和29.33%的缓存命中率，优于LRU、GDSF和LFU-DA方法。一个主要发现是，i-Cloud学习均匀成本模式，能在非均匀成本环境中表现良好。
英文摘要：
Big data can be hosted on cloud and being shared distributedly through cloud services in an unprecedented volume, variety and velocity. This causes not only cloud network congestions and delayed cloud services but also increases in public cloud data-out charges. Client-side cloud cache alleviates these problems. Furthermore, cloud cache must be aware of nonuniform data-out costs when big data is stored in hybrid clouds built with different public cloud providers. Deploying i-Cloud approach as the core mechanism of cloud cache could save data-out cost up to 14.78% or 4,425 USD saved per annum based on our representative scenario, and delivered 17.24% byte-hit, 17.96% delay-saving and 29.33% cache hit outperforming LRU, GDSF and LFU-DA approaches. A main finding is that i-Cloud, learning uniform cost patterns, could perform well against nonuniform cost environment.
