Flame-MR: An event-driven architecture for MapReduce applications

Nowadays, many organizations analyze their data with the MapReduce paradigm, most of them using the popular Apache Hadoop framework. As the data size managed by MapReduce applications is steadily increasing, the need for improving the Hadoop performance also grows. Existing modifications of Hadoop (e.g., Mellanox Unstructured Data Accelerator) attempt to improve performance by changing some of its underlying subsystems. However, they are not always capable to cope with all its performance bottlenecks or they hinder its portability. Furthermore, new frameworks like Apache Spark or DataMPI can achieve good performance improvements, but they do not keep compatibility with existing MapReduce applications. This paper proposes Flame-MR, a new event-driven MapReduce architecture that increases Hadoop performance by avoiding memory copies and pipelining data movements, without modifying the source code of the applications. The performance evaluation on two representative systems (an HPC cluster and a public cloud platform) has shown experimental evidence of significant performance increases, reducing the execution time by up to 54% on the Amazon EC2 cloud.     

CloudFlow: A data-aware programming model for cloud workflow applications on modern HPC systems

Traditional High-Performance Computing (HPC) based big-data applications are usually constrained by having to move large amount of data to compute facilities for real-time processing purpose. Modern HPC systems, represented by High-Throughput Computing (HTC) and Many-Task Computing (MTC) platforms, on the other hand, intend to achieve the long-held dream of moving compute to data instead. This kind of data-aware scheduling, typically represented by Hadoop MapReduce, has been successfully implemented in its Map Phase, whereby each Map Task is sent out to the compute node where the corresponding input data chunk is located. However, Hadoop MapReduce limits itself to a one-map-to-one-reduce framework, leading to difficulties for handling complex logics, such as pipelines or workflows. Meanwhile, it lacks built-in support and optimization when the input datasets are shared among multiple applications and/or jobs. The performance can be improved significantly when the knowledge of the shared and frequently accessed data is taken into scheduling decisions.To enhance the capability of managing workflow in modern HPC system, this paper presents CloudFlow, a Hadoop MapReduce based programming model for cloud workflow applications. CloudFlow is built on top of MapReduce, which is proposed not only being data aware, but also shared-data aware. It identifies the most frequently shared data, from both task-level and job-level, replicates them to each compute node for data locality purposes. It also supports user-defined multiple Map- and Reduce functions, allowing users to orchestrate the required data-flow logic. Mathematically, we prove the correctness of the whole scheduling framework by performing theoretical analysis. Further more, experimental evaluation also shows that the execution runtime speedup exceeds 4X compared to traditional MapReduce implementation with a manageable time overhead.     

Mapreduce performance model for Hadoop 2.x

MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that may provide reasonably accurate job response time estimation at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance model for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, they could not be applied to Hadoop 2.x due to fundamental architectural changes and dynamic resource allocation in Hadoop 2.x. Thus, the proposed solution is based on an existing performance model for Hadoop 1.x, but taking into consideration architectural changes and capturing the execution flow of a MapReduce job by using queuing network model. This way, the cost model reflects the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup.     

用于Hadoop2.x的MapReduce性能评估模型

基于MapReduce的程序被越来越多地应用于大型数据分析的应用中.Apache Hadoop是最常用的开源MapReduce模型之一.程序运行时间的缩短对于MapReduce程序以及所有数据处理应用而言至关重要,而能够准确估算MapReduce程序的执行时间是优化程序的重要环节.本文定义了一个在Hadoop2.x版本...     

A security framework in G-Hadoop for big data computing across distributed Cloud data centres

MapReduce is regarded as an adequate programming model for large-scale data-intensive applications. The Hadoop framework is a well-known MapReduce implementation that runs the MapReduce tasks on a cluster system. G-Hadoop is an extension of the Hadoop MapReduce framework with the functionality of allowing the MapReduce tasks to run on multiple clusters. However, G-Hadoop simply reuses the user authentication and job submission mechanism of Hadoop, which is designed for a single cluster. This work proposes a new security model for G-Hadoop. The security model is based on several security solutions such as public key cryptography and the SSL protocol, and is dedicatedly designed for distributed environments. This security framework simplifies the users authentication and job submission process of the current G-Hadoop implementation with a single-sign-on approach. In addition, the designed security framework provides a number of different security mechanisms to protect the G-Hadoop system from traditional attacks.     

大数据分析的分布式MOLAP技术

大数据的规模效应给数据存储、管理以及数据分析带来了极大的挑战,学界和业界广泛采用分布式文件系统和MapReduce编程模型来应对这一挑战.提出了大数据环境中一种基于Hadoop分布式文件系统（HDFS）和MapReduce编程模型的分布式MOLAP技术,称为DOLAP（distributed OLAP）.DOLAP采用一种特殊的多维模型完成维和度量的映射;采用维编码和遍历算法实现维层次上的上卷下钻操作;采用数据分块和线性化算法将维和度量保存在分布式文件系统中;采用数据块选择算法优化OLAP的性能;采用MapReduce编程模型实现OLAP操作.描述了DOLAP在科学数据分析的应用案例,并与主流的非关系数据库系统进行性能对比.实验结果表明,尽管数据装载性能略显不足,但DOLAP的性能要优于基于HBase,Hive,HadoopDB,OLAP4Cloud等主流非关系数据库系统实现的OLAP性能.     

基于Hadoop MapReduce模型的应用研究

MapReduce是一种简化并行计算的分布式编程模型,是Google的一项重要技术,通常被用于数据密集型的分布式并行计算.探讨了来自Apache开源的分布式计算平台Hadoop的核心设计MapReduce编程模型,并通过算法实验分析和研究了MapReduce模型的工作方式和应用方法.     

MapReduce Parallel Programming Model: A State-of-the-Art Survey

With the development of information technologies, we have entered the era of Big Data. Google’s MapReduce programming model and its open-source implementation in Apache Hadoop have become the dominant model for data-intensive processing because of its simplicity, scalability, and fault tolerance. However, several inherent limitations, such as lack of efficient scheduling and iteration computing mechanisms, seriously affect the efficiency and flexibility of MapReduce. To date, various approaches have been proposed to extend MapReduce model and improve runtime efficiency for different scenarios. In this review, we assess MapReduce to help researchers better understand these novel optimizations that have been taken to address its limitations. We first present the basic idea underlying MapReduce paradigm and describe several widely used open-source runtime systems. And then we discuss the main shortcomings of original MapReduce. We also review these MapReduce optimization approaches that have recently been put forward, and categorize them according to the characteristics and capabilities. Finally, we conclude the paper and suggest several research works that should be carried out in the future.     

基于Hadoop云计算模型探究

云计算是并行计算、分布式计算和网格计算的发展。文中详细地阐述了MapReduce的编程思想、工作原理、步骤和方法。探讨了来自Apache开源的分布式计算平台Hadoop的核心设计MapReduce编程模型,并通过算法实验分析和研究了MapReduce模型的工作方式和应用方法。     

Benchmarking MapReduce implementations under different application scenarios

The MapReduce paradigm provides a scalable model for large scale data intensive computing and associated fault-tolerance. Data volumes generated and processed by scientific applications are growing rapidly. Several MapReduce implementations, with various degrees of conformance to the key tenets of the model, are available today. Each of these implementations is optimized for specific features. To make the right decisions, HPC application and middleware developers must thus understand the complex dependences between MapReduce features and their application. We present a set of benchmarks for quantifying, comparing, and contrasting the performance of MapReduce implementations under a wide range of representative use cases. To demonstrate the utility of the benchmarks and to provide a snapshot of the current implementation landscape, we report the performance of three different MapReduce implementations, and draw conclusions about their current performance characteristics. The three implementations we chose for evaluation are the widely used Hadoop implementation, Twister, which has been widely discussed in the literature in the context of scientific applications, and LEMO-MR which is our own implementation. We present the performance of these three implementations and draw conclusions about their performance characteristics.     

MapReduce集群环境下的数据放置策略

MapReduce是一种适用于大规模数据密集型应用的有效编程模型,具有编程简单、易于扩展、容错性好等特点,已在并行和分布式计算领域得到了广泛且成功的应用.由于MapReduce将计算扩展到大规模的机器集群上,处理数据的合理放置成为影响MapReduce集群系统性能(包括能耗、资源利用率、通信和I/O代价、响应时间、系统的可靠性和吞吐率等)的关键因素之一.首先,对MapReduce编程模型的典型实现——Hadoop缺省的数据放置策略进行分析,并进一步讨论了MapReduce框架下,设计数据放置策略时需考虑的关键问题和衡量数据放置策略的标准;其次,对目前MapReduce集群环境下的数据放置策略优化方法的研究与进展进行了综述和分析;最后,分析和归纳了MapReduce集群环境下数据放置策略的下一步研究工作.     

基于Hadoop的广域网分布式主题爬虫系统框架

广域网分布式爬虫与局域网爬虫相比有诸多的优势,而现有基于Hadoop分布式爬虫的设计主要是面向局域网环境的。为解决Hadoop分布式计算平台不适合部署于广域网的问题,设计了一个基于Hadoop的广域网分布式爬虫系统框架。爬虫系统利用消息中间件实现分布式可靠通信,数据存储采用可伸缩的Hadoop分布式文件系统HDFS,网页解析利用MapReduce并行处理,并基于模板匹配实现框架可定制。系统的性能仿真显示该框架具有支撑大规模爬虫并发工作的能力。     

基于Hadoop的贝叶斯过滤MapReduce模型

传统分布式大型邮件系统对海量邮件的过滤存在编程难、效率低、前期训练耗用资源大等缺点,为此,对传统贝叶斯过滤算法进行并行化改进,利用云计算MapReduce模型在海量数据处理方面的优势,设计一种基于Hadoop开源云架构的贝叶斯邮件过滤MapReduce模型,优化邮件的训练和过滤过程。实验结果表明,与传统分布式计算模型相比,该模型在召回率、查准率和精确率方面性能较好,同时可降低邮件过滤成本,提高系统执行效率。     

MRA++: Scheduling and data placement on MapReduce for heterogeneous environments

MapReduce has emerged as a popular programming model in the field of data-intensive computing. This is due to its simplistic design, which provides ease of use for programmers, and its framework implementations such as Hadoop, which have been adopted by large business and technology companies. In this paper we make some improvements to the Hadoop MapReduce framework by introducing algorithms that are suitable for heterogeneous environments. The goal is to efficiently perform data-intensive computing in heterogeneous environments. The need for these adaptations derives from the fact that, following the framework design proposed by Google, Hadoop is optimized to run in large homogeneous clusters. Hence we propose MRA++, a new MapReduce framework design that considers the heterogeneity of nodes during data distribution, task scheduling and job control. MRA++establishes a training task to gather information prior to the data distribution. However, we show that the delay introduced in the setup phase is offset by the effectiveness of the mechanisms and algorithms, that achieve performance gains of more than 70% in 10 Mbps networks.     

A hadoop based platform for natural language processing of web pages and documents

The rapid and extensive pervasion of information through the web has enhanced the diffusion of a huge amount of unstructured natural language textual resources. A great interest has arisen in the last decade for discovering, accessing and sharing such a vast source of knowledge. For this reason, processing very large data volumes in a reasonable time frame is becoming a major challenge and a crucial requirement for many commercial and research fields. Distributed systems, computer clusters and parallel computing paradigms have been increasingly applied in the recent years, since they introduced significant improvements for computing performance in data-intensive contexts, such as Big Data mining and analysis. Natural Language Processing, and particularly the tasks of text annotation and key feature extraction, is an application area with high computational requirements; therefore, these tasks can significantly benefit of parallel architectures. This paper presents a distributed framework for crawling web documents and running Natural Language Processing tasks in a parallel fashion. The system is based on the Apache Hadoop ecosystem and its parallel programming paradigm, called MapReduce. In the specific, we implemented a MapReduce adaptation of a GATE application and framework (a widely used open source tool for text engineering and NLP). A validation is also offered in using the solution for extracting keywords and keyphrase from web documents in a multi-node Hadoop cluster. Evaluation of performance scalability has been conducted against a real corpus of web pages and documents.     

iMapReduce: A Distributed Computing Framework for Iterative Computation

Iterative computation is pervasive in many applications such as data mining, web ranking, graph analysis, online social network analysis, and so on. These iterative applications typically involve massive data sets containing millions or billions of data records. This poses demand of distributed computing frameworks for processing massive data sets on a cluster of machines. MapReduce is an example of such a framework. However, MapReduce lacks built-in support for iterative process that requires to parse data sets iteratively. Besides specifying MapReduce jobs, users have to write a driver program that submits a series of jobs and performs convergence testing at the client. This paper presents iMapReduce, a distributed framework that supports iterative processing. iMapReduce allows users to specify the iterative computation with the separated map and reduce functions, and provides the support of automatic iterative processing within a single job. More importantly, iMapReduce significantly improves the performance of iterative implementations by (1) reducing the overhead of creating new MapReduce jobs repeatedly, (2) eliminating the shuffling of static data, and (3) allowing asynchronous execution of map tasks. We implement an iMapReduce prototype based on Apache Hadoop, and show that iMapReduce can achieve up to 5 times speedup over Hadoop for implementing iterative algorithms.     

Governing energy consumption in Hadoop through CPU frequency scaling: An analysis

With increasingly inexpensive storage and growing processing power, the cloud has rapidly become the environment of choice to store and analyze data for a variety of applications. Most large-scale data computations in the cloud heavily rely on the MapReduce paradigm and on its Hadoop implementation. Nevertheless, this exponential growth in popularity has significantly impacted power consumption in cloud infrastructures. In this paper, we focus on MapReduce processing and we investigate the impact of dynamically scaling the frequency of compute nodes on the performance and energy consumption of a Hadoop cluster. To this end, a series of experiments are conducted to explore the implications of Dynamic Voltage and Frequency Scaling (DVFS) settings on power consumption in Hadoop clusters. By enabling various existing DVFS governors (i.e., performance, powersave, ondemand, conservative and userspace) in a Hadoop cluster, we observe significant variation in performance and power consumption across different applications: the different DVFS settings are only sub-optimal for several representative MapReduce applications. Furthermore, our results reveal that the current CPU governors do not exactly reflect their design goal and may even become ineffective to manage the power consumption in Hadoop clusters. This study aims at providing a clearer understanding of the interplay between performance and power management in Hadoop clusters and therefore offers useful insight into designing power-aware techniques for Hadoop systems.     

SLA-aware energy-efficient scheduling scheme for Hadoop YARN

Apache Hadoop becomes ubiquitous for cloud computing which provides resources as services for multi-tenant applications. YARN (a.k.a. MapReduce 2.0) is one of the key features in the second-generation Hadoop, which provides resource management and scheduling for large-scale MapReduce environments. Two enormous challenges in the YARN scheduler are the abilities to automatically tailor and control resource allocations to different jobs for achieving their Service Level Agreements (SLAs), and minimize energy consumption of the overall cloud computing system. In this work, we propose an SLA-aware energy-efficient scheduling scheme which allocates appropriate amount of resources to MapReduce applications with YARN architecture. In our task scheduling policy, We consider the data locality information to save the MapReduce network traffic. Furthermore, the slack time between the actual execution time of completed tasks and expected completion time of the application is utilized to improve the energy-efficiency of the system. An online userspace governor-based dynamic voltage and frequency scaling (DVFS) scheme is designed in the YARN per-application ApplicationMaster to dynamically change the CPU frequency for upcoming tasks given the slack time from previous completed tasks. Experimental evaluation shows that our proposed scheme outperforms the existing MapReduce scheduling policies in terms of both resource ultization and energy-efficiency.     

Hadoop云平台MapReduce模型优化研究

针对Hadoop平台MapReduce分布式计算模型运行机制中的顺序制约而产生的计算资源浪费问题,从提高平台中每个执行节点的细粒度并行数据处理角度出发,结合Java共享内存多线程编程技术,对该模型进行了优化,提出一种MapReduce+OpenMP粗细粒度相结合的分布式并行计算模型。并在由四个节点组成的Hadoop集群环境下对不同规模大小的出租车GPS轨迹数据分析处理,验证该模型的性能和效率,实验结果证明MapReduce+OpenMP分布式并行计算模型确实能够提高针对大数据集的计算效率,是对Hadoop平台大数据分析处理模型有效的完善和优化。     

通过HiBench基准测试比较Hadoop与Spark的性能

Hadoop MapReduce和Apache Spark都适用在大型集群上以并行和分布式模式有效地处理大量数据.为了比较这两个框架的性能,作者使用HiBench基准套件基于执行时间和内存消耗两个标准进行测试.测试结果表明,这些框架的性能根据用例实现的不同而有很大差异.从而可以得出结论:在一般情况下,Spark能够比H...