Workload Characterization and Performance Implications of Large-Scale Blog Servers

  • Authors:
  • Myeongjae Jeon;Youngjae Kim;Jeaho Hwang;Joonwon Lee;Euiseong Seo

  • Affiliations:
  • Rice University;Oak Ridge National Laboratory;Korea Advanced Institute of Science and Technology;Sungkyunkwan University;Sungkyunkwan University

  • Venue:
  • ACM Transactions on the Web (TWEB)
  • Year:
  • 2012

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Abstract

With the ever-increasing popularity of Social Network Services (SNSs), an understanding of the characteristics of these services and their effects on the behavior of their host servers is critical. However, there has been a lack of research on the workload characterization of servers running SNS applications such as blog services. To fill this void, we empirically characterized real-world Web server logs collected from one of the largest South Korean blog hosting sites for 12 consecutive days. The logs consist of more than 96 million HTTP requests and 4.7TB of network traffic. Our analysis reveals the following: (i) The transfer size of nonmultimedia files and blog articles can be modeled using a truncated Pareto distribution and a log-normal distribution, respectively; (ii) user access for blog articles does not show temporal locality, but is strongly biased towards those posted with image or audio files. We additionally discuss the potential performance improvement through clustering of small files on a blog page into contiguous disk blocks, which benefits from the observed file access patterns. Trace-driven simulations show that, on average, the suggested approach achieves 60.6% better system throughput and reduces the processing time for file access by 30.8% compared to the best performance of the Ext4 filesystem.