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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost
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CoRE: Cooperative End-to-End Traffic Redundancy Elimination for Reducing Cloud Bandwidth Cost

机译：CoRE：消除端到端流量冗余以降低云带宽成本

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摘要

The pay-as-you-go service model impels cloud customers to reduce the usage cost of bandwidth. Traffic Redundancy Elimination (TRE) has been shown to be an effective solution for reducing bandwidth costs, and thus has recently captured significant attention in the cloud environment. By studying the TRE techniques in a trace driven approach, we found that both short-term (time span of seconds) and long-term (time span of hours or days) data redundancy can concurrently appear in the traffic, and solely using either sender-based TRE or receiver-based TRE cannot simultaneously capture both types of traffic redundancy. Also, the efficiency of existing receiver-based TRE solution is susceptible to the data changes compared to the historical data in the cache. In this paper, we propose a Cooperative end-to-end TRE solution (CoRE) that can detect and remove both short-term and long-term redundancy through a two-layer TRE design with cooperative operations between layers. An adaptive prediction algorithm is further proposed to improve TRE efficiency through dynamically adjusting the prediction window size based on the hit ratio of historical predictions. Besides, we enhance CoRE to adapt to different traffic redundancy characteristics of cloud applications to improve its operation cost. Extensive evaluation with several real traces show that CoRE is capable of effectively identifying both short-term and long-term redundancy with low additional cost while ensuring TRE efficiency from data changes.
机译：随用随付的服务模型促使云客户减少带宽的使用成本。业已证明，消除流量冗余（TRE）是降低带宽成本的有效解决方案，因此最近在云环境中引起了广泛关注。通过以跟踪驱动的方式研究TRE技术，我们发现短期（几秒钟的时间跨度）和长期（几小时或几天的时间跨度）数据冗余可以同时出现在流量中，并且仅使用任一发送者基于TRE或基于接收器的TRE无法同时捕获两种类型的流量冗余。而且，与基于缓存的历史数据相比，现有的基于接收器的TRE解决方案的效率易受数据更改的影响。在本文中，我们提出了一种协作式端到端TRE解决方案（CoRE），该解决方案可以通过两层TRE设计以及各层之间的协作操作来检测并去除短期和长期冗余。进一步提出了一种自适应预测算法，通过基于历史预测的命中率动态调整预测窗口大小来提高TRE效率。此外，我们增强了CoRE以适应云应用程序的不同流量冗余特性，从而降低了其运营成本。通过对多个真实轨迹的广泛评估表明，CoRE能够以较低的额外成本有效地识别短期和长期冗余，同时确保数据更改带来的TRE效率。

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