Economical and efficient big data sharing with i-Cloud
Economical and efficient big data sharing with i-Cloud
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DOI：
10.1109/bigcomp.2014.6741417
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发表时间：
2014-02
期刊：
影响因子：
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通讯作者：
T. Banditwattanawong;Masawee Masdisornchote;P. Uthayopas
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文献类型：
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作者：
T. Banditwattanawong;Masawee Masdisornchote;P. Uthayopas
大数据可以托管在云上，并通过云服务以前所未有的数量、种类和速度进行分布式共享。这不仅会导致云网络拥塞和云服务延迟，还会导致公有云数据输出费用增加。客户端云缓存缓解了这些问题。此外，当大数据存储在由不同公共云提供商构建的混合云中时，云缓存必须意识到不一致的数据输出成本。根据我们的代表性场景，部署 i-Cloud 方法作为云缓存的核心机制可以节省高达 14.78% 的数据输出成本，即每年节省 4,425 美元，并提供 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。主要发现是，学习统一成本模式的 i-Cloud 可以在非统一成本环境中表现良好。
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.