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All Journal Journal of Computer Science and Engineering (JCSE) Journal of Computing Theories and Applications International Journal of Research in STEM Education
Moses, Timothy
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Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Mathematics Physics

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Journal : Journal of Computer Science and Engineering (JCSE)

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A fault-tolerance model for Hadoop rack-aware resource management system Moses, Timothy; Abiodun, Oladunjoye John
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.651

Abstract

The central resource manager of Hadoop Yet Another Resource Manager (YARN) has posed a major concern to big data analysis and exploration. The central arbiter is overwhelmed whenever there are resource requests by application masters and heartbeat communication from several name nodes in the Hadoop cluster; thereby, degrading the performance of the framework. An attempt to decentralize the resource manager's responsibilities by introducing a new layer in the cluster named the Rack Unit Resource Manager (RU_RM) layer increased cluster performance but introduced a fault-tolerance concern. This work, therefore, developed a fault-tolerant model to allow for efficient and effective data analysis in the Hadoop cluster. A pseudo-distributed computation was set up with the help of the YARN Scheduler Load Simulator (SLS) and WordCount operation performed with varying input sizes. Two fault scenarios were presented and the results obtained showed that with an increase in input size (workload), the running time of the developed fault-tolerant model though slightly higher than that of the existing model, is significantly negligible when compared to the computation bottleneck incurred anytime RU_RM fails. The developed model, therefore, has good performance in the presence of failure of a unit (RU_RM) in the cluster.
Big Data Indexing: Taxonomy, Performance Evaluation, Challenges and Research Opportunities Othman, Abubakar Usman; Moses, Timothy; Aisha, Umar Yahaya; Gital, Abdulsalam Ya’u; Souley, Boukari; Adeleke, Badmos Tajudeen
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

In order to efficiently retrieve information from highly huge and complicated datasets with dispersed storage in cloud computing, indexing methods are continually used on big data. Big data has grown quickly due to the accessibility of internet connection, mobile devices like smartphones and tablets, body-sensor devices, and cloud applications. Big data indexing has a variety of problems as a result of the expansion of big data, which is seen in the healthcare industry, manufacturing, sciences, commerce, social networks, and agriculture. Due to their high storage and processing requirements, current indexing approaches fall short of meeting the needs of large data in cloud computing. To fulfil the indexing requirements for large data, an effective index strategy is necessary. This paper presents the state-of-the-art indexing techniques for big data currently being proposed, identifies the problems these techniques and big data are currently facing, and outlines some future directions for research on big data indexing in cloud computing. It also compares the performance taxonomy of these techniques based on mean average precision and precision-recall rate.
Co-Authors Abiodun, Oladunjoye John Adeleke, Badmos Tajudeen Agushaka, Ovre Jeffery Aisha, Umar Yahaya Eke, Christopher Ifeanyi Gital, Abdulsalam Ya’u Godswill, Theophilus Iorzua, Joseph Tersoo Kwaghtyo, Dekera Kenneth Othman, Abubakar Usman Souley, Boukari Yakubu, Sani