A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

Large-scale data mining analytics based on MapReduce

In this work, we search for possible approaches to large-scale data mining analytics. We perform an exploration about the existing MapReduce and other MapReduce-like frameworks for distributed data processing and the distributed file systems for distributed data storage. We study in detail about Hadoop Distributed File System (HDFS) and Hadoop MapReduce software framework. We analyse the benefits of newer version of Hadoop software framework which provides better scalability solution by segregating the cluster resource management task from MapReduce framework. This version is called YARN and is very flexible in supporting various kinds of distributed data processing other than batchmode processing of MapReduce. We also looked into various implementations of data mining algorithms based on MapReduce to derive a comprehensive concept about developing such algorithms. We also looked for various tools that provided MapRedcue based scalable data mining algorithms. We could only find Mahout as a tool specially based on Hadoop MapReduce. But the tool developer team decided to stop using Hadoop MapReduce and to use instead Apache Spark as the underlying execution engine. WEKA also has a very small subset of data mining algorithms implemented using MapReduce which is not properly maintained and supported by the developer team. Subsequently, we found out that Apache Spark, apart from providing an optimised and a faster execution engine for distributed processing also provided an accompanying library for machine learning algorithms. This library is called Machine Learning library (MLlib). Apache Spark claimed that it is much faster than Hadoop MapReduce as it exploits the advantages of in-memory computations which is particularly more beneficial for iterative workloads in case of data mining. Spark is designed to work on variety of clusters: YARN being one of them. It is designed to process the Hadoop data. We selected to perform a particular data mining task: decision tree learning based classification and regression data mining. We stored properly labelled training data for predictive mining tasks in HDFS. We set up a YARN cluster and run Spark's MLlib applications on this cluster. These applications use the cluster managing capabilities of YARN and the distributed execution framework of Spark core services. We performed several experiments to measure the performance gains, speed-up and scaleup of implementations of decision tree learning algorithms in Spark's MLlib. We found out much better than expected results for our experiments. We achieved a much higher than ideal speed-up when we increased the number of nodes. The scale-up is also very excellent. There is a significant decrease in run-time for training decision tree models by increasing the number of nodes. This demonstrates that Spark's MLlib decision tree learning algorithms for classification and regression analysis are highly scalable

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

i2MapReduce: Incremental MapReduce for Mining Evolving Big Data

As new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. Incremental processing is a promising approach to refreshing mining results. It utilizes previously saved states to avoid the expense of re-computation from scratch. In this paper, we propose i2MapReduce, a novel incremental processing extension to MapReduce, the most widely used framework for mining big data. Compared with the state-of-the-art work on Incoop, i2MapReduce (i) performs key-value pair level incremental processing rather than task level re-computation, (ii) supports not only one-step computation but also more sophisticated iterative computation, which is widely used in data mining applications, and (iii) incorporates a set of novel techniques to reduce I/O overhead for accessing preserved fine-grain computation states. We evaluate i2MapReduce using a one-step algorithm and three iterative algorithms with diverse computation characteristics. Experimental results on Amazon EC2 show significant performance improvements of i2MapReduce compared to both plain and iterative MapReduce performing re-computation