Workload Characterization and Performance Implications of Large-Scale Blog Servers

Author:

Jeon Myeongjae1,Kim Youngjae2,Hwang Jeaho3,Lee Joonwon4,Seo Euiseong4

Affiliation:

1. Rice University

2. Oak Ridge National Laboratory

3. Korea Advanced Institute of Science and Technology

4. Sungkyunkwan University

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.

Funder

Ministry of Education, Science and Technology

Ministry of Knowledge Economy

U.S. Department of Energy

Korea Evaluation Institute of Industrial Technology

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

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