Big Data Processing and Application Research

Gao, Penglin; Han, Zhaoming; Wan, Fucheng

doi:10.1109/aiam50918.2020.00031

Cited by 10 publications

(2 citation statements)

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“…Recently, Cloud computing is a novel paradigm that maximizes efficiency while reducing management interactions [1]. This paradigm allows for distributed and parallel computing and is the foundation for processing large amounts of data, surpassing the capabilities of individual machines limited by RAM capacities [2] [3]. Nevertheless, the exponential growth in cloud computing users and the increasing demands of contemporary technology have made resource allocation optimization in these environments even more crucial [4].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Mittal,

Kumar,

Sharma

2024

IJCDS

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The need for cloud computing has increased in the age of contemporary networked systems, driving the pursuit of optimal resource allocation and data processing. It is imperative in essential fields where security, such as transportation systems, depends on computing performance. Even after much research has been done on managing resources in cloud computing, finding algorithms that maximize job completion, minimize costs, and maximize resource consumption has remained a top priority. However, existing techniques have shown limitations, which calls for new ways. Our work shows the novel hybrid approach that has the potential to change the game completely. The Neural Network Task Classification (N2TC) is the result of merging neural networks with genetic algorithms. This ground-breaking method skillfully applies the Genetic Algorithm Task Assignment (GATA) for resource allocation while utilizing neural networks for task categorization. Notably, our algorithm carefully considers execution time, response time, costs, and system efficiency to promote fairness, a defense against resource scarcity. Our method achieves a remarkable 13.3% cost reduction, a stunning 12.1% increase in response time, and a 3.2% increase in execution time. These strong indicators act as a wake-up call, announcing our hybrid algorithm's power and revolutionary potential in transforming the paradigms around cloud-based task scheduling. This work represents a turning point in cloud computing, demonstrating an innovative combination of algorithms that not only overcomes current constraints but also ushers in a new era of efficacy and efficiency with far-reaching implications outside the domain of transportation systems.

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Section: Introductionmentioning

confidence: 99%

Revolutionizing Cloud-Based Task Scheduling: A Novel Hybrid Algorithm for Optimal Resource Allocation and Efficiency in Contemporary Networked Systems

Mittal,

Kumar,

Sharma

2024

IJCDS

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“…As explained by Gupta et al [4], the Map-Reduce-based big data analytics and NO SQL management techniques help a lot to achieve business goals. There are a decent number of tools [5,6] like Mahout, Apache Spark, Hadoop, FlinkML etc.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Evolutionary Schema on Precision Medicine for Diabetes Using Big Data Analytics

Sobhanbabu,

Bharati

2023

RIA

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Precision medicine is the future of the Healthcare system, which is going to change the generalized medicine prescription. To establish precision medicine in the place of generalized medicine, new frameworks have to be designed that will utilize the existing enormous volumes of data. Big data is the term that represents massive volumes of data being generated through various means, including in Healthcare. In this work, a new framework for precision medicine is proposed beginning from big data, applying Classification techniques to avail reduced data and there on to achieve precision medicine with an intelligent medicine advisor. In the first phase, we evaluated the noisy big data with innovative hierarchical decision attention networks, Multi class classification using Map Reduce mechanism. This phase is experimented with a diabetes dataset. This output can be utilized by the next phase component called Intelligent Medicine advisor to get system recommended precision medicine, which can help the medical practitioner to join the Artificial Intelligence in their treatment.

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