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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Publicado en:Computer (00189162) Vol. 48; no. 3; pp. 36 - 42
Autores principales: Zhuang, Zhenyun, Ramachandra, Haricharan, Xiong, Chaoyue
Formato: Artículo
Publicado: IEEE Mar2015
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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        atl: Taming Replication Latency of Big Data Events with Capacity Planning.
      aug:
        au:
          Zhuang, Zhenyun
          Ramachandra, Haricharan
          Xiong, Chaoyue
        affil: LinkedIn
      su:
        Database administration
        Big data
        Replication (Experimental design)
        Software measurement
        Database management
      sug:
        subj:
          Database administration
          Big data
          Replication (Experimental design)
          Software measurement
          Database management
      keyword:
        ARIMA
        autoregressive integrated moving average
        Capacity planning
        Data models
        database replication
        graph databases
        high-performance computing
        Internet/Web technologies
        LinkedIn
        multithreading
        Predictive models
        replication latency
        Soci
        SPARQL
        time-series decomposition RDF databases
        traffic rate forecasting
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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