Performing MapReduce on Data Centers with Hierarchical Structures

Data centers are created as distributed information systems for massive data storage and processing. The structure of a data center determines the way that its inner servers, links and switches are interconnected. Several hierarchical structures have been proposed to improve the topological performance of data centers. By using recursively defined topologies, these novel structures can well support general applications and services with high scalability and reliability. However, these structures ignore the details of some specific applications running on data centers, such as MapReduce, a well-known distributed data processing application. The communication and control mechanisms for performing MapReduce on the traditional structure cannot be employed on the hierarchical structures. In this paper, we propose a methodology for performing MapReduce on data centers with hierarchical structures. Our methodology is based on the distributed hash table (DHT), an efficient data retrieval approach on distributed systems. We utilize the advantages of DHT, including decentralization, fault tolerance and scalability, to address the main problems that face hierarchical data centers in supporting MapReduce. Comprehensive evaluation demonstrates the feasibility and excellent performance of our methodology

Parallel Hierarchical Affinity Propagation with MapReduce

The accelerated evolution and explosion of the Internet and social media is generating voluminous quantities of data (on zettabyte scales). Paramount amongst the desires to manipulate and extract actionable intelligence from vast big data volumes is the need for scalable, performance-conscious analytics algorithms. To directly address this need, we propose a novel MapReduce implementation of the exemplar-based clustering algorithm known as Affinity Propagation. Our parallelization strategy extends to the multilevel Hierarchical Affinity Propagation algorithm and enables tiered aggregation of unstructured data with minimal free parameters, in principle requiring only a similarity measure between data points. We detail the linear run-time complexity of our approach, overcoming the limiting quadratic complexity of the original algorithm. Experimental validation of our clustering methodology on a variety of synthetic and real data sets (e.g. images and point data) demonstrates our competitiveness against other state-of-the-art MapReduce clustering techniques

Large Scale Hierarchical K-Means Based Image Retrieval With MapReduce

Image retrieval remains one of the most heavily researched areas in Computer Vision. Image retrieval methods have been used in autonomous vehicle localization research, object recognition applications, and commercially in projects such as Google Glass. Current methods for image retrieval become problematic when implemented on image datasets that can easily reach billions of images. In order to process these growing datasets, we distribute the necessary computation for image retrieval among a cluster of machines using Apache Hadoop. While there are many techniques for image retrieval, we focus on systems that use Hierarchical K-Means Trees. Successful image retrieval systems based on Hierarchical K-Means Trees have been built using the tree as a Visual Vocabulary to build an Inverted File Index and implementing a Bag of Words retrieval approach, or by building the tree as a Full Representation of every image in the database and implementing a K-Nearest Neighbor voting scheme for retrieval. Both approaches involve different levels of approximation, and each has strengths and weaknesses that must be weighed in accordance with the needs of the application. Both approaches are implemented with MapReduce, for the first time, and compared in terms of image retrieval precision, index creation run-time, and image retrieval throughput. Experiments that include up to 2 million images running on 20 virtual machines are shown

Embed and Conquer: Scalable Embeddings for Kernel k-Means on MapReduce

The kernel $k$-means is an effective method for data clustering which extends the commonly-used $k$-means algorithm to work on a similarity matrix over complex data structures. The kernel $k$-means algorithm is however computationally very complex as it requires the complete data matrix to be calculated and stored. Further, the kernelized nature of the kernel $k$-means algorithm hinders the parallelization of its computations on modern infrastructures for distributed computing. In this paper, we are defining a family of kernel-based low-dimensional embeddings that allows for scaling kernel $k$-means on MapReduce via an efficient and unified parallelization strategy. Afterwards, we propose two methods for low-dimensional embedding that adhere to our definition of the embedding family. Exploiting the proposed parallelization strategy, we present two scalable MapReduce algorithms for kernel $k$-means. We demonstrate the effectiveness and efficiency of the proposed algorithms through an empirical evaluation on benchmark data sets.Comment: Appears in Proceedings of the SIAM International Conference on Data Mining (SDM), 201

Implications of storage subsystem interactions on processing efficiency in data intensive computing

Includes bibliographical references.2015 Fall.Processing frameworks such as MapReduce allow development of programs that operate on voluminous on-disk data. These frameworks typically include support for multiple file/storage subsystems. This decoupling of processing frameworks from the underlying storage subsystem provides a great deal of flexibility in application development. However, as we demonstrate, this flexibility often exacts a price: performance. Given the data volumes, storage subsystems (such as HDFS, MongoDB, and HBase) disperse datasets over a collection of machines. Storage subsystems manage complexity relating to preservation of consistency, redundancy, failure recovery, throughput, and load balancing. Preserving these properties involve message exchanges between distributed subsystem components, updates to in-memory data structures, data movements, and coordination as datasets are staged and system conditions change. Storage subsystems prioritize these properties differently, leading to vastly different network, disk, memory, and CPU footprints for staging and accessing the same dataset. This thesis proposes a methodology for comparing and identifying the storage subsystem suited for the processing that is being performed on a dataset. We profile the network I/O, disk I/O, memory, and CPU costs introduced by a storage subsystem during data staging, data processing, and generation of results. We perform this analysis with different storage subsystems and applications with different disk-I/O to CPU processing ratios