Designing Efficient Algorithms for MapReduce: A Simplified Approach
ER. VISHESH NARENDRA PAMADI
MapReduce, big data, algorithm design, parallel processing, data-intensive applications, scalability, efficiency, distributed computing
MapReduce is a programming model that has revolutionized the way large-scale data processing tasks are approached. With the advent of big data, the demand for efficient data processing and analysis has never been greater. MapReduce offers a simplified yet powerful framework for processing massive datasets by distributing the workload across a cluster of machines. This paper presents a systematic approach to designing efficient algorithms for the MapReduce framework, emphasizing simplicity and scalability. We explore the core principles of the MapReduce model and illustrate how complex computational problems can be decomposed into smaller, manageable tasks using the map and reduce functions. By examining case studies and real-world applications, we demonstrate how these principles can be applied to optimize performance and resource utilization. Our approach not only enhances the efficiency of existing algorithms but also provides a foundational guide for developing new algorithms that are inherently suitable for parallel processing. The simplicity of our methodology lies in its ability to break down intricate processes into clear, concise steps that leverage the parallel nature of MapReduce, ultimately achieving significant improvements in processing time and resource allocation. This paper aims to equip developers and data scientists with the tools and insights needed to harness the full potential of MapReduce in their data-intensive applications
"Designing Efficient Algorithms for MapReduce: A Simplified Approach", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.8, Issue 7, page no.23-37, July-2021, Available :https://tijer.org/TIJER/papers/TIJER2107003.pdf
Volume 8
Issue 7,
July-2021
Pages : 23-37
Paper Reg. ID: TIJER_154525
Published Paper Id: TIJER2107003
Downloads: 00081
Research Area: Science and Technology
Country: -, -, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
