Optimizing Hadoop Scheduling in Single-Board-Computer-Based Heterogeneous Clusters
B Qureshi - Computation, 2024 - mdpi.com
Single-board computers (SBCs) are emerging as an efficient and economical solution for fog
and edge computing, providing localized big data processing with lower energy …
and edge computing, providing localized big data processing with lower energy …
Exploring Deep Insights from Vast Data: An Overview of Deep Learning Techniques for Big Data
Big data has emerged very fast, and this has brought both opportunities and problems that
are related to the application of deep learning. This paper explores how deep learning can …
are related to the application of deep learning. This paper explores how deep learning can …
A Big Data, Bigger Impact: A Comprehensive Review of Machine Learning Advancements
The exponential growth of data driven by the internet has necessitated effective extraction of
insights, with big data and machine learning standing as pivotal tools. This paper aims to …
insights, with big data and machine learning standing as pivotal tools. This paper aims to …
Enhancement in Cloud Performance using the Clustering Method of Scientific Workflow Tasks
In the fast-paced competitive environment, cloud computing is develo** satisfactory
interest in the community of scientific applications. For executing complex data sets of …
interest in the community of scientific applications. For executing complex data sets of …
Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters
B Qureshi - 2024 - preprints.org
In the dynamic landscape of sustainable computing, use of edge devices is paramount for
reducing the need for large-scale centralized data centers. By processing data locally, edge …
reducing the need for large-scale centralized data centers. By processing data locally, edge …
[CITATION][C] Comparison of Hadoop Mapreduce and Apache Spark in Big Data Processing with Hgrid247-DE
FD Utami, FD Astuti - Journal of Applied Informatics and Computing, 2024