MapReduce编程模型在网络I/O密集型程序中的应用研究*

利用开源Hadoop平台,重点研究了MapReduce在轻量数据集、网络I/O密集型程序的适用性。采用MapReduce编程模型改造了一个典型的轻量数据集、网络I/O密集型应用——FTP站点扫描程序;搭建了一个小规模Hadoop集群环境,调整了Hadoop平台的默认配置,并用真实数据对改造前后的程序进行了性能测试。实验证明,MapReduce编程模型具有良好的分布式特性,可适用于轻量数据集、网络I/O密集型程序。     

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.     

基于Hadoop平台的数据挖掘技术研究

Hadoop集MapReduce、HDFS、HBase、Avro、Pig等子项目于一身,并行编程模型(MapReduce)、分布式文件系统(HDFS)是Hadoop的核心技术。用户可以通过结合编程模型MapReduce与Hadoop的方式对分布式程序进行进行二次开发,从海量数据中挖掘隐含的、新颖的、对决策实施工作有指导价值的关系、模型,在Hadoop平台上构建数据挖掘系统。     

Hadoop MapReduce短作业执行性能优化

Hadoop MapReduce并行计算框架被广泛应用于大规模数据并行处理.近年来,由于其能较好地处理大规模数据,Hadoop MapReduce也被越来越多地使用在查询应用中.为了能够处理大规模数据集,Hadoop的基本设计更多地强调了数据的高吞吐率.然而在处理对短作业响应性能有较高要求的查询应用时,Hadoop MapReduce并行计算框架存在明显不足.为了提升Hadoop对于短作业的执行效率,对原有的Hadoop MapReduce作出以下3点优化:1)通过优化原有的setup和cleanup任务的执行方式,成功地缩短了作业初始化环境准备和作业结束环境清理的时间;2)将首次任务分配从"拉"模式转变为"推"模式;3)将作业执行过程中JobTracker和TaskTrackers之间的控制消息通信从现有的周期性心跳机制中分离出来,采用即时传递机制.最后,采用一种典型的基于MapReduce并行化的查询应用BLAST,对优化工作进行了评估.各种不同类型BLAST作业的测试实验表明,与现有的标准Hadoop相比,优化后的Hadoop平均执行性能提升约23%.     

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。     

基于MapReduce的数据挖掘平台设计与实现

MapReduce编程模型的简单性和高性价比使得其适用于海量数据的并行处理.然而,MapReduce欠缺对多数据源、组件复用以及数据可视化支持,这些缺点使用户在运用MapReduce框架进行数据挖掘时暴露出开发效率低下,重复开发等问题.提出了一种基于MapReduce的数据挖掘平台的设计与实现,该设计思想为Hadoop作为大规模数据计算平台在数据挖掘、数据可视化以及商业智能应用方面的不足提供了参考与弥补.同时,基于该方法实现了一个大规模数据挖掘工具.     

基于MapReduce模型的并行量子进化算法

利用MapReduce模型可自动编写串行程序及编程接口简单的优点,实现量子进化算法在MapReduce模型下的并行化,提出基于MapReduce模型的并行量子进化算法MRQEA,并将其部署到Hadoop云计算平台上运行。对0-1背包问题的测试结果证明,MRQEA算法在处理大型数据集时具有良好的加速比和并行效率。     

基于MapReduce的并行石漠化CA模型

针对石漠化演化模拟预测CA模型在单机上训练和运行时间较长的问题。给出了MapReduce编程模型实现的并行化石漠化CA模型,并在用普通PC搭建的Hadoop集群上进行研究实验。实验结果表明,在Hadoop集群上实现的MapReduce并行化石漠化CA模型具有较好的加速比。     

MapReduce分布编程模型

MapReduce是分布编程模型,是Hadoop平台的核心算法。文章介绍MapReduce特点、函数式编程范式、映射函数与化简函数、MapReduce模型理论基础和应用方法等内容。     

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.     

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.     

SHadoop: Improving MapReduce performance by optimizing job execution mechanism in Hadoop clusters

As a widely-used parallel computing framework for big data processing today, the Hadoop MapReduce framework puts more emphasis on high-throughput of data than on low-latency of job execution. However, today more and more big data applications developed with MapReduce require quick response time. As a result, improving the performance of MapReduce jobs, especially for short jobs, is of great significance in practice and has attracted more and more attentions from both academia and industry. A lot of efforts have been made to improve the performance of Hadoop from job scheduling or job parameter optimization level. In this paper, we explore an approach to improve the performance of the Hadoop MapReduce framework by optimizing the job and task execution mechanism. First of all, by analyzing the job and task execution mechanism in MapReduce framework we reveal two critical limitations to job execution performance. Then we propose two major optimizations to the MapReduce job and task execution mechanisms: first, we optimize the setup and cleanup tasks of a MapReduce job to reduce the time cost during the initialization and termination stages of the job; second, instead of adopting the loose heartbeat-based communication mechanism to transmit all messages between the JobTracker and TaskTrackers, we introduce an instant messaging communication mechanism for accelerating performance-sensitive task scheduling and execution. Finally, we implement SHadoop, an optimized and fully compatible version of Hadoop that aims at shortening the execution time cost of MapReduce jobs, especially for short jobs. Experimental results show that compared to the standard Hadoop, SHadoop can achieve stable performance improvement by around 25% on average for comprehensive benchmarks without losing scalability and speedup. Our optimization work has passed a production-level test in Intel and has been integrated into the Intel Distributed Hadoop (IDH). To the best of our knowledge, this work is the first effort that explores on optimizing the execution mechanism inside map/reduce tasks of a job. The advantage is that it can complement job scheduling optimizations to further improve the job execution performance.     

Optimizing and Tuning MapReduce Jobs to Improve the Large‐Scale Data Analysis Process

Data‐intensive applications process large volumes of data using a parallel processing method. MapReduce is a programming model designed for data‐intensive applications for massive data sets and an execution framework for large‐scale data processing on clusters of commodity servers. While fault tolerance, easy programming structure, and high scalability are considered strong points of MapReduce; however its configuration parameters must be fine‐tuned to the specific deployment, which makes it more complex in configuration and performance. This paper explains tuning of the Hadoop configuration parameters, which directly affect MapReduce's job workflow performance under various conditions to achieve maximum performance. On the basis of the empirical data we collected, it became apparent that three main methodologies can affect the execution time of MapReduce running on cluster systems. Therefore, in this paper, we present a model that consists of three main modules: (1) Extending a data redistribution technique in order to find the high‐performance nodes, (2) Utilizing the number of map/reduce slots in order to make it more efficient in terms of execution time, and (3) Developing a new hybrid routing schedule shuffle phase in order to define the scheduler task while memory management level is reduced.     

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.     

Designing a MapReduce performance model in distributed heterogeneous platforms based on benchmarking approach

MapReduce framework is an effective method for big data parallel processing. Enhancing the performance of MapReduce clusters, along with reducing their job execution time, is a fundamental challenge to this approach. In fact, one is faced with two challenges here: how to maximize the execution overlap between jobs and how to create an optimum job scheduling. Accordingly, one of the most critical challenges to achieving these goals is developing a precise model to estimate the job execution time due to the large number and high volume of the submitted jobs, limited consumable resources, and the need for proper Hadoop configuration. This paper presents a model based on MapReduce phases for predicting the execution time of jobs in a heterogeneous cluster. Moreover, a novel heuristic method is designed, which significantly reduces the makespan of the jobs. In this method, first by providing the job profiling tool, we obtain the execution details of the MapReduce phases through log analysis. Then, using machine learning methods and statistical analysis, we propose a relevant model to predict runtime. Finally, another tool called job submission and monitoring tool is used for calculating makespan. Different experiments were conducted on the benchmarks under identical conditions for all jobs. The results show that the average makespan speedup for the proposed method was higher than an unoptimized case.

     

HAT: history-based auto-tuning MapReduce in heterogeneous environments

In MapReduce model, a job is divided into a series of map tasks and reduce tasks. The execution time of the job is prolonged by some slow tasks seriously, especially in heterogeneous environments. To finish the slow tasks as soon as possible, current MapReduce schedulers launch a backup task on other nodes for each of the slow tasks. However, traditional MapReduce schedulers cannot detect slow tasks correctly since they cannot estimate the progress of tasks accurately (Hadoop home page http://hadoop.apache.org/, 2011; Zaharia et al. in 8th USENIX symposium on operating systems design and implementation, ACM, New York, pp. 29–42, 2008). To solve this problem, this paper proposes a History-based Auto-Tuning (HAT) MapReduce scheduler, which calculates the progress of tasks accurately and adapts to the continuously varying environment automatically. HAT tunes the weight of each phase of a map task and a reduce task according to the value of them in history tasks and uses the accurate weights of the phases to calculate the progress of current tasks. Based on the accurate-calculated progress of tasks, HAT estimates the remaining time of tasks accurately and further launches backup tasks for the tasks that have the longest remaining time. Experimental results show that HAT can significantly improve the performance of MapReduce applications up to 37% compared with Hadoop and up to 16% compared with LATE scheduler.     

Analytical Performance Models for MapReduce Workloads

MapReduce is a currently popular programming model to support parallel computations on large datasets. Among the several existing MapReduce implementations, Hadoop has attracted a lot of attention from both industry and research. In a Hadoop job, map and reduce tasks coordinate to produce a solution to the input problem, exhibiting precedence constraints and synchronization delays that are characteristic of a pipeline communication between maps (producers) and reduces (consumers). We here address the challenge of designing analytical models to estimate the performance of MapReduce workloads, notably Hadoop workloads, focusing particularly on the intra-job pipeline parallelism between map and reduce tasks belonging to the same job. We propose a hierarchical model that combines a precedence graph model and a queuing network model to capture the intra-job synchronization constraints. We first show how to build a precedence graph that represents the dependencies among multiple tasks of the same job. We then apply it jointly with an approximate Mean Value Analysis (aMVA) solution to predict mean job response time, throughput and resource utilization. We validate our solution against a queuing network simulator and a real setup in various scenarios, finding very close agreement in both cases. In particular, our model produces estimates of average job response time that deviate from measurements of a real setup by less than 15 %.     

MapReduce Workload Modeling with Statistical Approach

Large-scale data-intensive cloud computing with the MapReduce framework is becoming pervasive for the core business of many academic, government, and industrial organizations. Hadoop, a state-of-the-art open source project, is by far the most successful realization of MapReduce framework. While MapReduce is easy- to-use, efficient and reliable for data-intensive computations, the excessive configuration parameters in Hadoop impose unexpected challenges on running various workloads with a Hadoop cluster effectively. Consequently, developers who have less experience with the Hadoop configuration system may devote a significant effort to write an application with poor performance, either because they have no idea how these configurations would influence the performance, or because they are not even aware that these configurations exist. There is a pressing need for comprehensive analysis and performance modeling to ease MapReduce application development and guide performance optimization under different Hadoop configurations. In this paper, we propose a statistical analysis approach to identify the relationships among workload characteristics, Hadoop configurations and workload performance. We apply principal component analysis and cluster analysis to 45 different metrics, which derive relationships between workload characteristics and corresponding performance under different Hadoop configurations. Regression models are also constructed that attempt to predict the performance of various workloads under different Hadoop configurations. Several non-intuitive relationships between workload characteristics and performance are revealed through our analysis and the experimental results demonstrate that our regression models accurately predict the performance of MapReduce workloads under different Hadoop configurations.     

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.     

OPTIMA: On-Line Partitioning Skew Mitigation for MapReduce with Resource Adjustment

Partitioning skew has been shown to be a major issue that can significantly prolong the execution time of MapReduce jobs. Most of the existing off-line heuristics for partitioning skew mitigation are inefficient; they have to wait for the completion of all the map tasks. Some solutions can tackle this problem on-line, but will impose an additional overhead by repartitioning the workload of overloaded tasks. In this paper, we present OPTIMA, an on-line partitioning skew mitigation technique for MapReduce. OPTIMA predicts the workload distribution of reduce tasks at run-time, leverages the deviation detection technique to identify the overloaded tasks and pro-actively adjusts resource allocation for these tasks to reduce their execution time. We provide the upper bound of OPTIMA in time complexity, while allowing OPTIMA to perform totally on-line. Through experiments using both real and synthetic workloads running on an 11-node Hadoop cluster, we have observed OPTIMA can effectively mitigate the partitioning skew and improved the job completion time by up to 36.73 % in our experiments.