Investigating hybrid SSD FTL schemes for Hadoop workloads
Proceedings of the ACM International Conference on Computing Frontiers
Joint optimization of overlapping phases in MapReduce
Performance Evaluation
Toward fine-grained online task characteristics estimation in scientific workflows
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
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MapReduce is becoming the state-of-the-art computing paradigm for processing large-scale datasets on a large cluster with tens or thousands of nodes. It has been widely used in various fields such as e-commerce, Web search, social networks, and scientific computation. Understanding the characteristics of MapReduce workloads is the key to achieving better configuration decisions and improving the system throughput. However, workload characterization of MapReduce, especially in a large-scale production environment, has not been well studied yet. To gain insight on MapReduce workloads, we collected a two-week workload trace from a 2,000-node Hadoop cluster at Taobao, which is the biggest online e-commerce enterprise in Asia, ranked 14th in the world as reported by Alexa. The workload trace covered 912,157 jobs, logged from Dec. 4 to Dec. 20, 2011. We characterized the workload at the granularity of job and task, respectively and concluded with a set of interesting observations. The results of workload characterization are representative and generally consistent with data platforms for e-commerce websites, which can help other researchers and engineers understand the performance and job characteristics of Hadoop in their production environments. In addition, we use these job analysis statistics to derive several implications for potential performance optimization solutions.