Morpho: A decoupled MapReduce framework for elastic cloud computing

Lu, Lu; Shi, Xuanhua; Jin, Hai; Wang, Qiuyue; Yuan, Da-Xing; Wu, Song

doi:10.1016/j.future.2013.12.026

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…Therefore, its popularity has grown rapidly for various brands of companies in many fields. It provides a highly efficient platform for parallel execution of applications, allocation of data in distributed database systems, and fault tolerant network communications [27]. The main goal of MapReduce is to facilitate data parallelization, distribution, and load balancing in a simple [26] library.…”

Section: Mapreduce Model Definitionmentioning

confidence: 99%

Towards Large Scale Image Retrieval System Using Parallel Frameworks

Mezzoudj¹

2021

Multimedia Information Retrieval

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Recently, the increasing use of mobile devices, such as cameras and smartphones, has resulted in a dramatic increase in the amount of images collected every day. Therefore, retrieving and managing these large volumes of images has become a major challenge in the field of computer vision. One of the solutions for efficiently managing image databases is an Image Content Search (CBIR) system. For this, we introduce in this chapter some fundamental theories of content-based image retrieval for large scale databases using Parallel frameworks. Section 2 and Section 3 presents the basic methods of content-based image retrieval. Then, as the emphasis of this chapter, we introduce in Section 1.2 A content-based image retrieval system for large-scale images databases. After that, we briefly address Big Data, Big Data processing platforms for large scale image retrieval. In Sections 5, 6, 7, and 8. Finally, we draw a conclusion in Section 9.

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Section: Mapreduce Model Definitionmentioning

confidence: 99%

Towards Large Scale Image Retrieval System Using Parallel Frameworks

Mezzoudj¹

2021

Multimedia Information Retrieval

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“…Lu Lu et al [2014] proposed A decoupled Map Reduce computing-storage system for cloud computing environment and present the load aware data placement strategy also implementation design to data placement by the virtual machine. In this paper author proposed the comparison between physical storage and virtual storage in reference of the load balancing and high availability of the data.…”

Section: Storage and Replicationmentioning

confidence: 99%

“…In this paper author proposed the comparison between physical storage and virtual storage in reference of the load balancing and high availability of the data. The -Morpho‖ is proposed as a modified version of the Hadoop [5].The morpho consists process of task execution through virtual machine same as Hadoop and storage keeping through the physical storage .When Map Reduce computation will performing the map tasks can get meta data file directly from physical machines storage without any extra data transfers .Morpho achieves high performance due to virtual placement. The virtual placement increases the resistant factor of the resource utilization.…”

Section: Storage and Replicationmentioning

confidence: 99%

A Catholic Research on Big Data and Hadoop Environment

Lakshkar¹,

Kalani²,

Todwal³

2015

IJCA

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In recent years, there are various sources that generates data in petabyte and terabyte, known as big data and its generated by human, machine, sensor etc. So the solution leds with big data is Apache Hadoop has attracted strong attention because of its applicability on processing for the large data sets. This paper present the review about the big data and it's characteristics and also the types of the open source tools environment like HADOOP. The objective of paper to identify the power of the Hadoop on the big data and motivation behind the new research and outlines to address to Apache Hadoop also includes the programming paradigm that is Map-Reduce.

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“…Subsequently, its prominence has developed quickly for different brands of endeavors in numerous fields. It gives a profoundly powerful and effective structure for the parallel execution of the applications, data allotment in distributed database systems, and adaptation to internal failure arrange interchanges [5] [6].…”

Section: Introductionmentioning

confidence: 99%

Importance of MapReduce for Big Data Applications: A Survey

Durairaj

Poornappriya

2018

AJCST

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Significant regard for MapReduce framework has been trapped by a wide range of areas. It is presently a practical model for data-focused applications because of its basic interface of programming, high elasticity, and capacity to withstand the subjection to defects. Additionally, it is fit for preparing a high extent of data in Distributed Computing environments (DCE). MapReduce, on various events, has turned out to be material to a wide scope of areas. MapReduce is a parallel programming model and a related usage presented by Google. In the programming model, a client determines the calculation by two capacities, Map and Reduce. The basic MapReduce library consequently parallelizes the calculation and handles muddled issues like data dispersion, load adjusting, and adaptation to non-critical failure. Huge data spread crosswise over numerous machines, need to parallelize. Moves the data, and gives booking, adaptation to non-critical failure. A writing survey on the MapReduce programming in different areas has completed in this paper. An examination course has been distinguished by utilizing a writing audit.

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Morpho: A decoupled MapReduce framework for elastic cloud computing

Cited by 13 publications

References 6 publications

Towards Large Scale Image Retrieval System Using Parallel Frameworks

Towards Large Scale Image Retrieval System Using Parallel Frameworks

A Catholic Research on Big Data and Hadoop Environment

Importance of MapReduce for Big Data Applications: A Survey

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