“IMPORTANCE OF HADOOP IN THE MODERN ERA”- PERFORMANCE AND ITS PORTABILITY0 تومان
Hadoop is a flexible and open source implementation for analyzing large datasets using Map Reduce. The file
system is developed in java that encourages portability around multiple heterogeneous software and hardware
A Comprehensive View of Hadoop MapReduce Scheduling Algorithms0 تومان
Hadoop is a Java-based programming framework that supports the storing and processing of large data sets in a distributed computing environment and it is very much appropriate for high volume of data. it’s using HDFS for data storing and using MapReduce to processing that data. MapReduce is a popular programming model to support data-intensive applications using shared-nothing clusters. the main objective of MapReduce programming model is to parallelize the job execution across multiple nodes for execution
A comprehensive view of Hadoop research—A systematic literature review0 تومان
Context: In recent years, the valuable knowledge that can be retrieved from petabyte scale datasets – known as Big Data – led to the development of solutions to process information based on parallel and distributed computing. Lately, Apache Hadoop has attracted strong attention due to its applicability to Big Data processing
A security framework in G-Hadoop for big data computing across distributed Cloud data centres0 تومان
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.
A survey of open source tools for machine learning with big data in the Hadoop ecosystem0 تومان
A survey of open source tools for machine learning with big data in the Hadoop ecosystem
A Unified MapReduce Domain-Specific Language for Distributed and Shared Memory Architectures0 تومان
MapReduce is a suitable and ecient parallel programming pattern for processing big data analysis. In recent
years, many frameworks/languages have implemented this pattern to achieve high performance in data mining applications, particularly for distributed memory architectures (e.g., clusters).Nevertheless, the industry of processors is now able to oer powerful processing on single machines (e.g., multi-core). Thus, these applications may address the parallelism in another architectural level.
An Approach to Setup Hadoop in Windows Environment0 تومان
Hadoop is a open source framework for automatic parallelization of computing tasks in distributed environment. Unfortunately programming for Hadoop comprise of certain challenges. It is very difficult to debug and understand Hadoop programs. We can make it a little simple by using a simplified version of the Hadoop cluster that runs locally on the developer’s machine
An Evaluation of Cassandra for Hadoop0 تومان
In the last decade, the increased use and growth of social media, unconventional web technologies, and mobile
applications, have all encouraged development of a new breed of database models. NoSQL data stores target the unstructured data, which by nature is dynamic and a key focus area for “Big Data” research. New generation data can prove costly and unpractical to administer with SQL databases due to lack of structure, high scalability, and elasticity needs. NoSQL data stores such as MongoDB and Cassandra provide
Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?0 تومان
Analyzing Massive Astrophysical Datasets: Can Pig/Hadoop or a Relational DBMS Help?
Bandwidth-Aware Scheduling with SDN in Hadoop: A New Trend for Big Data
Software Defined Networking (SDN) is a revolutionary network architecture that separates out network control functions from the underlying equipment and is an increasingly trend to help enterprises build more manageable data centers where big data processing emerges as an important part of applications.
Characterizing Machines and Workloads on a Google Cluster0 تومان
Cloud computing offers high scalability, flexibility and cost-effectiveness to meet emerging computing requirements. Understanding the characteristics of real workloads on a large production cloud cluster benefits not only cloud service providers but also researchers and daily users. This paper studies a largescale Google cluster usage trace dataset and characterizes how the machines in the cluster are managed and the workloads submitted during a 29-day period behave. We focus on the frequency and pattern of machine maintenance events, joband task-level workload behavior, and how the overall cluster resources are utilized
CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH0 تومان
CLASSIFICATION ALGORITHMS FOR BIG DATA ANALYSIS, A MAP REDUCE APPROACH
Diagnosing Heterogeneous Hadoop Clusters0 تومان
We present a data-driven approach for diagnosing performance issues in heterogeneous Hadoop clusters. Hadoop is a popular and extremely successful framework for horizontally scalable distributed computing over large data sets based on the MapReduce framework. In its current implementation, Hadoop assumes a homogeneous cluster of compute nodes
Hadoop Acceleration Through Network Levitated Merge0 تومان
Hadoop is a popular open-source implementation of the MapReduce
programming model for cloud computing. However, it faces a
number of issues to achieve the best performance from the underlying
system. These include a serialization barrier that delays the
reduce phase, repetitive merges and disk access, and lack of capability
to leverage latest high speed interconnects. We describe
Hadoop-A, an acceleration framework that optimizes Hadoop with
plugin components implemented in C++ for fast data movement,
overcoming its existing limitations
Hadoop and its evolving ecosystem0 تومان
Socio-technical ecosystems are living organisms that grow and shrink, that change velocity, and that split from, or merge with, others. The ecosystems that surround producers of software-intensive products exhibit all of these behaviors. We report on the start of a longitudinal study of the evolution of the Hadoop ecosystem, take a look back
over the history of the ecosystem, and describe how we will be observing this ecosystem over the next few months. Our initial observations of the early days of Hadoop’s ecosystem showed rapid change. We present these observations and a method for taking and analyzing observations in the future
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