MapReduce is a technique used to vastly improve distributed processing of data and can massively speed up computation. Hadoop and its MapReduce relies on JVM and Java which is expensive on memory. High Performance Computing based MapReduce framework could be used that can perform more memory-efficiently and faster than the standard MapReduce. This paper explores an entirely C++ based approach to the MapReduce and its feasibility on multiple factors like developer friendliness, deployment interface, efficiency and scalability. This paper also introduces Delayed Reduction and deployment techniques that can speed up MapReduce in a compiled environment.
MapReduce has been widely applied in various fields of data and compute intensive applications and also it is important programming model for cloud computing. Hadoop is an open-source implementation of MapReduce which operates on terabytes of data using commodity hardware. We have applied this Hadoop MapReduce programming model for analyzing web log files so that we could get hit count of specific web application. This system uses Hadoop file system to store log file and results are evaluated using Map and Reduce function. Experimental results show hit count for each field in log file. Also du…
Cloud Computing is emerging as a new computational paradigm shift. Hadoop-MapReduce has become a powerful Computation Model for processing large data on distributed commodity hardware clusters such as Clouds. In all Hadoop implementations, the default FIFO scheduler is available where jobs are scheduled in FIFO order with support for other priority based schedulers also. In this paper we study various scheduler improvements possible with Hadoop and also provided some guidelines on how to improve the scheduling in Hadoop in Cloud Environments.
This tutorial presents a recipe for the construction of a compute cluster for processing large volumes of data, using cheap, easily available personal computer hardware (Intel/AMD based PCs) and freely available open source software (Ubuntu Linux, Apache Hadoop).
Motivation: Storage of genomic data is a major cost for the Life Sciences, effectively addressed mostly via specialized data compression methods. For the same reasons of abundance in data production, the use of Big Data technologies is seen as the future for genomic data storage and processing, with MapReduce-Hadoop as leaders. Somewhat surprisingly, none of the specialized FASTA/Q compressors is available within Hadoop. Indeed, their deployment there is not exactly immediate. Such a State of the Art is problematic. Results: We provide major advances in two different directions. Methodological…
Hadoop MapReduce is now a popular choice for performing large-scale data analytics. This technical report describes a detailed set of mathematical performance models for describing the execution of a MapReduce job on Hadoop. The models describe dataflow and cost information at the fine granularity of phases within the map and reduce tasks of a job execution. The models can be used to estimate the performance of MapReduce jobs as well as to find the optimal configuration settings to use when running the jobs.
While advanced analysis of large dataset is in high demand, data sizes have surpassed capabilities of conventional software and hardware. Hadoop framework distributes large datasets over multiple commodity servers and performs parallel computations. We discuss the I/O bottlenecks of Hadoop framework and propose methods for enhancing I/O performance. A proven approach is to cache data to maximize memory-locality of all map tasks. We introduce an approach to optimize I/O, the in-node combining design which extends the traditional combiner to a node level. The in-node combiner reduces the total n…
Hadoop is a popular MapReduce framework for developing parallel applications in distributed environments. Several advantages of MapReduce such as programming ease and ability to use commodity hardware make the applicability of soft computing methods for parallel and distributed systems easier than before. In this paper, we present the results of an experimental study on running soft computing algorithms using Hadoop. This study shows how a simple genetic algorithm running on Hadoop can be used to produce solutions for high dimensional optimization problems. In addition, a simple but effective …
Distributed data processing platforms for cloud computing are important tools for large-scale data analytics. Apache Hadoop MapReduce has become the de facto standard in this space, though its programming interface is relatively low-level, requiring many implementation steps even for simple analysis tasks. This has led to the development of advanced dataflow oriented platforms, most prominently Apache Spark and Apache Flink. Those platforms not only aim to improve performance through improved in-memory processing, but in particular provide built-in high-level data processing functionality, suc…
Clustering of image is one of the important steps of mining satellite images. In our experiment we have simultaneously run multiple K-means algorithms with different initial centroids and values of k in the same iteration of MapReduce jobs. For initialization of initial centroids we have implemented Scalable K-Means++ MapReduce (MR) job [1]. We have also run a validation algorithm of Simplified Silhouette Index [2] for multiple clustering outputs, again in the same iteration of MR jobs. This paper explored the behavior of above mentioned clustering algorithms when run on big data platforms lik…
When dealing with massive data sorting, we usually use Hadoop which is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. A common approach in implement of big data sorting is to use shuffle and sort phase in MapReduce based on Hadoop. However, if we use it directly, the efficiency could be very low and the load imbalance can be a big problem. In this paper we carry out an experimental study of an optimization and analysis of large scale data sorting algorithm based on hadoop. In order to reach optimization, w…
Big data analysis has become much popular in the present day scenario and the manipulation of big data has gained the keen attention of researchers in the field of data analytics. Analysis of big data is currently considered as an integral part of many computational and statistical departments. As a result, novel approaches in data analysis are evolving on a daily basis. Thousands of transaction requests are handled and processed everyday by different websites associated with e-commerce, e-banking, e-shopping carts etc. The network traffic and weblog analysis comes to play a crucial role in su…
Many Hadoop configuration parameters have significant influence in the performance of running MapReduce jobs on Hadoop. It is time-consuming and tedious for general users to manually tune the parameters for optimal MapReduce performance. Besides, most of existing self-tuning system have opaque implementation, making it difficult to use in practice. This study presents an open-source project that hosts the developing self-tuning system called Catla to address the issues. Catla integrates multiple direct search and derivative-free optimization-based techniques to facilitate tuning efficiency for…
High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications. Consequently, they have been architected as producers and not consumers of data. The Apache Hadoop ecosystem has evolved to meet the requirements of data processing applications and has addressed many of the limitations of HPC platforms. There exist a class of scientific applications however, that need the collective capabilities of traditional high-performance computing environments and the Apache Hadoop ecosystem. For example, the scientific dom…
Monte Carlo simulations employed for the analysis of portfolios of catastrophic risk process large volumes of data. Often times these simulations are not performed in real-time scenarios as they are slow and consume large data. Such simulations can benefit from a framework that exploits parallelism for addressing the computational challenge and facilitates a distributed file system for addressing the data challenge. To this end, the Apache Hadoop framework is chosen for the simulation reported in this paper so that the computational challenge can be tackled using the MapReduce model and the da…
Traveling Salesman Problem (TSP) is one of the most common studied problems in combinatorial optimization. Given the list of cities and distances between them, the problem is to find the shortest tour possible which visits all the cities in list exactly once and ends in the city where it starts. Despite the Traveling Salesman Problem is NP-Hard, a lot of methods and solutions are proposed to the problem. One of them is Genetic Algorithm (GA). GA is a simple but an efficient heuristic method that can be used to solve Traveling Salesman Problem. In this paper, we will show a parallel genetic alg…
Hadoop MapReduce is a framework for distributed storage and processing of large datasets that is quite popular in big data analytics. It has various configuration parameters (knobs) which play an important role in deciding the performance i.e., the execution time of a given big data processing job. Default values of these parameters do not always result in good performance and hence it is important to tune them. However, there is inherent difficulty in tuning the parameters due to two important reasons - firstly, the parameter search space is large and secondly, there are cross-parameter inter…
We present the Hadoop Fair Sojourn Protocol (HFSP) scheduler, which implements a size-based scheduling discipline for Hadoop. The benefits of size-based scheduling disciplines are well recognized in a variety of contexts (computer networks, operating systems, etc...), yet, their practical implementation for a system such as Hadoop raises a number of important challenges. With HFSP, which is available as an open-source project, we address issues related to job size estimation, resource management and study the effects of a variety of preemption strategies. Although the architecture underlying H…
HPC environments have traditionally been designed to meet the compute demand of scientific applications and data has only been a second order concern. With science moving toward data-driven discoveries relying more on correlations in data to form scientific hypotheses, the limitations of HPC approaches become apparent: Architectural paradigms such as the separation of storage and compute are not optimal for I/O intensive workloads (e.g. for data preparation, transformation and SQL). While there are many powerful computational and analytical libraries available on HPC (e.g. for scalable linear …
Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating these techniques to a MapReduce implementation such as Hadoop presents unique challenges that can lead to new design choices. This paper describes how column-oriented storage techniques can be incorporated in Hadoop in a way that preserves its popular programming APIs. We show that simply using binary storage formats in Hadoop can provide a 3x perf…
MapReduce, the popular programming paradigm for large-scale data processing, has traditionally been deployed over tightly-coupled clusters where the data is already locally available. The assumption that the data and compute resources are available in a single central location, however, no longer holds for many emerging applications in commercial, scientific and social networking domains, where the data is generated in a geographically distributed manner. Further, the computational resources needed for carrying out the data analysis may be distributed across multiple data centers or community …
Many techniques have been proposed to implement the Apriori algorithm on MapReduce framework but only a few have focused on performance improvement. FPC (Fixed Passes Combined-counting) and DPC (Dynamic Passes Combined-counting) algorithms combine multiple passes of Apriori in a single MapReduce phase to reduce the execution time. In this paper, we propose improved MapReduce based Apriori algorithms VFPC (Variable Size based Fixed Passes Combined-counting) and ETDPC (Elapsed Time based Dynamic Passes Combined-counting) over FPC and DPC. Further, we optimize the multi-pass phases of these algor…
In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification, and moving object tracking. Since such studies benefit from the highest quality data, methods such as image coaddition (stacking) will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources or transient objects, these data streams …
MapReduce (MR) is the most popular solution to build applications for large-scale data processing. These applications are often deployed on large clusters of commodity machines, where failures happen constantly due to bugs, hardware problems, and outages. Testing MR-based systems is hard, since it is needed a great effort of test harness to execute distributed test cases upon failures. In this paper, we present a novel testing solution to tackle this issue called HadoopTest. This solution is based on a scalable harness approach, where distributed tester components are hung around each map and …
Huge amounts of data being generated continuously by digitally interconnected systems of humans, organizations and machines. Data comes in variety of formats including structured, unstructured and semi-structured, what makes it impossible to apply the same standard approaches, techniques and algorithms to manage and process this data. Fortunately, the enterprise level distributed platform named Hadoop Ecosystem exists. This paper explores Apache Hive component that provides full stack data managements functionality in terms of Data Definition, Data Manipulation and Data Processing. Hive is a d…
Designing fast and scalable algorithm for mining frequent itemsets is always being a most eminent and promising problem of data mining. Apriori is one of the most broadly used and popular algorithm of frequent itemset mining. Designing efficient algorithms on MapReduce framework to process and analyze big datasets is contemporary research nowadays. In this paper, we have focused on the performance of MapReduce based Apriori on homogeneous as well as on heterogeneous Hadoop cluster. We have investigated a number of factors that significantly affects the execution time of MapReduce based Apriori…
Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that mode…
Istanbul Technical University（伊斯坦布尔理工大学）；ASELSAN A.S.（阿瑟尔桑公司）
Most of the popular Big Data analytics tools evolved to adapt their working environment to extract valuable information from a vast amount of unstructured data. The ability of data mining techniques to filter this helpful information from Big Data led to the term Big Data Mining. Shifting the scope of data from small-size, structured, and stable data to huge volume, unstructured, and quickly changing data brings many data management challenges. Different tools cope with these challenges in their own way due to their architectural limitations. There are numerous parameters to take into consider…
In this poster, we quantitatively measure the impacts of data movement on performance in MapReduce-based applications when executed on HPC systems. We leverage the PAPI 'powercap' component to identify ideal conditions for execution of our applications in terms of (1) dataset characteristics (i.e., unique words); (2) HPC system (i.e., KNL and KNM); and (3) implementation of the MapReduce programming model (i.e., with or without combiner optimizations). Results confirm the high energy and runtime costs of data movement, and the benefits of the combiner optimization on these costs.
Several research works have focused on supporting index access in MapReduce systems. These works have allowed users to significantly speed up selective MapReduce jobs by orders of magnitude. However, all these proposals require users to create indexes upfront, which might be a difficult task in certain applications (such as in scientific and social applications) where workloads are evolving or hard to predict. To overcome this problem, we propose LIAH (Lazy Indexing and Adaptivity in Hadoop), a parallel, adaptive approach for indexing at minimal costs for MapReduce systems. The main idea of LI…
The advent of high performance computing (HPC) and graphics processing units (GPU), present an enormous computation resource for Large data transactions (big data) that require parallel processing for robust and prompt data analysis. While a number of HPC frameworks have been proposed, parallel programming models present a number of challenges, for instance, how to fully utilize features in the different programming models to implement and manage parallelism via multi-threading in both CPUs and GPUs. In this paper, we take an overview of three parallel programming models, CUDA, MapReduce, and …
Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing. A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large data sets across cluster of workstations. To handle massive scale data, Hadoop exploits the Hadoop Distributed File System termed as HDFS. The HDFS similar to most distributed file systems share a familiar problem on data sharing and availability among compute nodes, often which leads to decrease in performance. This paper is an experimental evaluation of Hadoop's computing performance whic…
This paper describes a distributed MapReduce implementation of the minimum Redundancy Maximum Relevance algorithm, a popular feature selection method in bioinformatics and network inference problems. The proposed approach handles both tall/narrow and wide/short datasets. We further provide an open source implementation based on Hadoop/Spark, and illustrate its scalability on datasets involving millions of observations or features.
Hadoop is currently the large-scale data analysis "hammer" of choice, but there exist classes of algorithms that aren't "nails", in the sense that they are not particularly amenable to the MapReduce programming model. To address this, researchers have proposed MapReduce extensions or alternative programming models in which these algorithms can be elegantly expressed. This essay espouses a very different position: that MapReduce is "good enough", and that instead of trying to invent screwdrivers, we should simply get rid of everything that's not a nail. To be more specific, much discussion in t…
Every day the number of traffic cameras in cities rapidly increase and huge amount of video data are generated. Parallel processing infrastruture, such as Hadoop, and programming models, such as MapReduce, are being used to promptly process that amount of data. The common approach for video processing by using Hadoop MapReduce is to process an entire video on only one node, however, in order to avoid parallelization problems, such as load imbalance, we propose to process videos by splitting it into equal parts and processing each resulting chunk on a different node. We used some machine learni…
We present a new record on computing specific bits of Pi, the mathematical constant, and discuss performing such computations on Apache Hadoop clusters. The specific bits represented in hexadecimal are 0E6C1294 AED40403 F56D2D76 4026265B CA98511D 0FCFFAA1 0F4D28B1 BB5392B8. These 256 bits end at the 2,000,000,000,000,252nd bit position, which doubles the position and quadruples the precision of the previous known record. The position of the first bit is 1,999,999,999,999,997 and the value of the two quadrillionth bit is 0. The computation is carried out by a MapReduce program called DistBbp. T…
Document clustering is a traditional, efficient and yet quite effective, text mining technique when we need to get a better insight of the documents of a collection that could be grouped together. The K-Means algorithm and the Hierarchical Agglomerative Clustering (HAC) algorithm are two of the most known and commonly used clustering algorithms; the former due to its low time cost and the latter due to its accuracy. However, even the use of K-Means in text clustering over large-scale collections can lead to unacceptable time costs. In this paper we first address some of the most valuable appro…
Frequent Pattern Mining is a one field of the most significant topics in data mining. In recent years, many algorithms have been proposed for mining frequent itemsets. A new algorithm has been presented for mining frequent itemsets based on N-list data structure called Prepost algorithm. The Prepost algorithm is enhanced by implementing compact PPC-tree with the general tree. Prepost algorithm can only find a frequent itemsets with required (pre-order and post-order) for each node. In this chapter, we improved prepost algorithm based on Hadoop platform (HPrepost), proposed using the Mapreduce …
Upcoming HPC clusters will feature hybrid memories and storage devices per compute node. In this work, we propose to use the MPI one-sided communication model and MPI windows as unique interface for programming memory and storage. We describe the design and implementation of MPI storage windows, and present its benefits for out-of-core execution, parallel I/O and fault-tolerance. In addition, we explore the integration of heterogeneous window allocations, where memory and storage share a unified virtual address space. When performing large, irregular memory operations, we verify that MPI windo…
Hadoop and Spark are widely used distributed processing frameworks for large-scale data processing in an efficient and fault-tolerant manner on private or public clouds. These big-data processing systems are extensively used by many industries, e.g., Google, Facebook, and Amazon, for solving a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and social network analysis. However, all these popular systems have a major drawback in terms of locally distributed computations, which prevent them in implement…
In recent years, the issue of energy consumption in high performance computing (HPC) systems has attracted a great deal of attention. In response to this, many energy-aware algorithms have been developed in different layers of HPC systems, including the hardware layer, service layer and system layer. These algorithms are of two types: first, algorithms which directly try to improve the energy by tweaking frequency operation or scheduling algorithms; and second, algorithms which focus on improving the performance of the system, with the assumption that efficient running of a system may indirect…
