Taming Replication Latency of Big Data Events with Capacity Planning.

Ensuring low replication latency of database events is business-critical but challenging with big data. A proposed capacity-planning model helps achieve this goal by forecasting future traffic rates, predicting replication latency, and determining required replication capacity. The Web extra at http...

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Detalles Bibliográficos
Publicado en:Computer (00189162) Vol. 48; no. 3; pp. 36 - 42
Autores principales: Zhuang, Zhenyun, Ramachandra, Haricharan, Xiong, Chaoyue
Formato: Artículo
Publicado: IEEE Mar2015
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Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario:Ensuring low replication latency of database events is business-critical but challenging with big data. A proposed capacity-planning model helps achieve this goal by forecasting future traffic rates, predicting replication latency, and determining required replication capacity. The Web extra at http://youtu.be/ZupPlrS8dGA is a video of in which author Zhenyun Zhuang demonstrates Naarad, an open-source performance analysis tool (https://github.com/linkedin/naarad) written in python that analyzes various metrics (gc, sar, Jmeter etc), evaluates SLAs and generates a user friendly report to aid in performance analysis and investigations.