SAGE: Geo-Distributed Streaming Data Analysis in Clouds
SAGE: Geo-Distributed Streaming Data Analysis in Clouds
复制标题
SAGE：云中的地理分布式流数据分析
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
L. Bougé
中科院分区：
文献类型：
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作者：
R. Tudoran;Gabriel Antoniu;L. Bougé
传感器网络、证券交易所、气候监测或科学应用的持续增长以越来越快的速度产生了新的流数据。管理和处理这类有时从多个地理位置产生的数据提出了重大挑战，因为它需要实时处理或数据汇总。传统的解决方案如DBMS、MapReduce或采用单一位置环境的专用解决方案无法满足处理地理分布的流数据所需的需求。像Azure这样的公共云拥有遍布全球的数据中心，它们提供了能够处理此类处理的基础设施。我们的方法提出了一种面向服务的云体系结构，通过组合分布在多个云数据中心的服务来执行流分析。因此，计算向利用地理数据局部性的多个数据源移动。初步结果表明，该方法具有良好的可扩展性，在Azure云中可达到1000个核心，与单一位置处理相比，性能提高了3.3倍。
The continuous growth of sensor networks, stock exchanges, climate monitoring or scientific applications produces new streaming data at increasing rates. Managing and processing such data, sometimes generated from multiple geographical locations, raises important challenges as it requires real-time processing or data aggregation. Conventional solutions like DBMS, MapReduce or dedicated solutions adopting single-located environments fail to meet the demands required for processing the Geo-distributed streaming data. Public clouds like Azure, with data centers spread around the globe, offer the infrastructure which can handle such a processing. Our approach, proposes a service-oriented cloud architecture for performing the stream analysis, by composing services which are distributed among multiple cloud data centers. Hence, the computation is moved towards the multiple data sources exploiting the geographical data locality. The initial results showed good scalability of the approach, reaching 1000 cores in the Azure cloud, and performance improvements compared to single location processing of a factor of 3.3.