1,250 research outputs found
PerfXplain: Debugging MapReduce Job Performance
While users today have access to many tools that assist in performing large
scale data analysis tasks, understanding the performance characteristics of
their parallel computations, such as MapReduce jobs, remains difficult. We
present PerfXplain, a system that enables users to ask questions about the
relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain
provides a new query language for articulating performance queries and an
algorithm for generating explanations from a log of past MapReduce job
executions. We formally define the notion of an explanation together with three
metrics, relevance, precision, and generality, that measure explanation
quality. We present the explanation-generation algorithm based on techniques
related to decision-tree building. We evaluate the approach on a log of past
executions on Amazon EC2, and show that our approach can generate quality
explanations, outperforming two naive explanation-generation methods.Comment: VLDB201
Machine Learning Based Auto-tuning for Enhanced OpenCL Performance Portability
Heterogeneous computing, which combines devices with different architectures,
is rising in popularity, and promises increased performance combined with
reduced energy consumption. OpenCL has been proposed as a standard for
programing such systems, and offers functional portability. It does, however,
suffer from poor performance portability, code tuned for one device must be
re-tuned to achieve good performance on another device. In this paper, we use
machine learning-based auto-tuning to address this problem. Benchmarks are run
on a random subset of the entire tuning parameter configuration space, and the
results are used to build an artificial neural network based model. The model
can then be used to find interesting parts of the parameter space for further
search. We evaluate our method with different benchmarks, on several devices,
including an Intel i7 3770 CPU, an Nvidia K40 GPU and an AMD Radeon HD 7970
GPU. Our model achieves a mean relative error as low as 6.1%, and is able to
find configurations as little as 1.3% worse than the global minimum.Comment: This is a pre-print version an article to be published in the
Proceedings of the 2015 IEEE International Parallel and Distributed
Processing Symposium Workshops (IPDPSW). For personal use onl
A Survey on Automatic Parameter Tuning for Big Data Processing Systems
Big data processing systems (e.g., Hadoop, Spark, Storm) contain a vast number of configuration parameters controlling parallelism, I/O behavior, memory settings, and compression. Improper parameter settings can cause significant performance degradation and stability issues. However, regular users and even expert administrators grapple with understanding and tuning them to achieve good performance. We investigate existing approaches on parameter tuning for both batch and stream data processing systems and classify them into six categories: rule-based, cost modeling, simulation-based, experiment-driven, machine learning, and adaptive tuning. We summarize the pros and cons of each approach and raise some open research problems for automatic parameter tuning.Peer reviewe
A Survey of Machine Learning Techniques for Self-tuning Hadoop Performance
The Apache Hadoop framework is an open source implementation of MapReduce for processing and storing big data. However, to get the best performance from this is a big challenge because of its large number configuration parameters. In this paper, the concept of critical issues of Hadoop system, big data and machine learning have been highlighted and an analysis of some machine learning techniques applied so far, for improving the Hadoop performance is presented. Then, a promising machine learning technique using deep learning algorithm is proposed for Hadoop system performance improvement
Learning-based Automatic Parameter Tuning for Big Data Analytics Frameworks
Big data analytics frameworks (BDAFs) have been widely used for data
processing applications. These frameworks provide a large number of
configuration parameters to users, which leads to a tuning issue that
overwhelms users. To address this issue, many automatic tuning approaches have
been proposed. However, it remains a critical challenge to generate enough
samples in a high-dimensional parameter space within a time constraint. In this
paper, we present AutoTune--an automatic parameter tuning system that aims to
optimize application execution time on BDAFs. AutoTune first constructs a
smaller-scale testbed from the production system so that it can generate more
samples, and thus train a better prediction model, under a given time
constraint. Furthermore, the AutoTune algorithm produces a set of samples that
can provide a wide coverage over the high-dimensional parameter space, and
searches for more promising configurations using the trained prediction model.
AutoTune is implemented and evaluated using the Spark framework and HiBench
benchmark deployed on a public cloud. Extensive experimental results illustrate
that AutoTune improves on default configurations by 63.70% on average, and on
the five state-of-the-art tuning algorithms by 6%-23%.Comment: 12 pages, submitted to IEEE BigData 201
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