Mobile Agent Improvement Testing on A Distributed Network Cluster Using Unsorted Metadata with A Distribution and Delocalization Model (DAME)

Benard Osero, Elisha Abade and Stephen Mburu
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Benard Osero: Department of Computing and Informatics, University of Nairobi, Nairobi, Kenya
Elisha Abade: Department of Computing and Informatics, University of Nairobi, Nairobi, Kenya
Stephen Mburu: Department of Computing and Informatics, University of Nairobi, Nairobi, Kenya

International Journal of Research and Innovation in Applied Science, 2023, vol. 8, issue 8, 84-100

Abstract: There has been growing demand for high performance systems for processing of both structured and unstructured data thus prompting managers and Organizations to find better methods for addressing the data processing needs now and in the future. The trend is projected to increase exponentially, as virtualized and distributed IOT systems are likely to exacerbate the problem as individual nodes will handle large chunks of data; consequently, these organizations are immersing their energies and resources in the research and use of Intelligent tools for data management and analysis which require real time processing, storage and transmission. Our research inspired by the Amidal’s law and Gustafson Barsis law of distribution uses Mobile agent distribution model complimented with map reduce in a virtualized environment to discover the extent to which the distribution of server nodes may improve performance as compared to the centralized server nodes in order to handle large amounts of data that will be produced and transmitted by the individual nodes. The distribution model in our research borrows from the concept of divide and conquer algorithms whose run-time is O (n log n). To test performance improvement, we employed a custom made Simulator called DAME, which has the capability of catching and distributing metadata through its agent based domain controller. Our research indicates that distribution of nodes on a network has a significant performance improvement with throughput increasing by 88 %, Latency decreasing by 23% and Scalability improvement by up to 43 %.

Date: 2023
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