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  1. Proceedings of the 2013 International Workshop on Data-Intensive Scalable Computing Systems (DISCS-2013)
  2. On the core affinity and file upload performance of Hadoop
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SDAFT: a novel scalable data access framework for parallel BLAST
On the core affinity and file upload performance of Hadoop
CRUCIBLE: towards unified secure on- and off-line analytics at scale
Large data and computation in a hazard map workflow using Hadoop and Neteeza architectures
Enhancing both fairness and performance using rate-aware dynamic storage cache partitioning
Novel parallel method for mining frequent patterns on multi-core shared memory systems
A framework for an in-depth comparison of scale-up and scale-out
Design of an active storage cluster file system for DAG workflows
Toward a data scalable solution for facilitating discovery of scientific data resources
BDMPI: conquering BigData with small clusters using MPI

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On the core affinity and file upload performance of Hadoop

Content Provider ACM Digital Library
Author Lee, Min Cho, Joong-Yeon Jin, Hyun-Wook Schwan, Karsten
Abstract The MapReduce programming model is introduced for big-data processing, where the data nodes perform both data storing and computation. Thus, we need to understand different resource requirements of data storing and computation tasks and schedule these efficiently over multi-core processors. The core affinity defines mapping between a set of cores and a given task. The core affinity can be decided based on resource requirements of a task because this largely affects the efficiency of computation, memory, and I/O resource utilization. In this paper, we analyze the impact of core affinity on the file upload performance of Hadoop Distributed File System (HDFS). Our study can provide the insight into the process scheduling issues on big-data processing systems. We also suggest a framework for dynamic core affinity based on our observations and show that a preliminary implementation can improve the throughput more than 40% compared with default Linux system.
Starting Page 25
Ending Page 30
Page Count 6
File Format PDF
ISBN 9781450325066
DOI 10.1145/2534645.2534651
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2013-11-18
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Process scheduling Multi-core Affinity Hadoop distributed file system Big-data
Content Type Text
Resource Type Article
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