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Dissertation/Thesis Abstract

The Effect of the Implementation of a Swarm Intelligence Algorithm on the Efficiency of the Cosmos Open Source Managed Operating System
by Usman, Modibo, D.B.A., Northcentral University, 2018, 132; 10810882
Abstract (Summary)

As the complexity of mankind’s day-to-day challenges increase, so does a need for the optimization of know solutions to accommodate for this increase in complexity. Today’s computer systems use the Input, Processing, and Output (IPO) model as a way to deliver efficiency and optimization in human activities. Since the relative quality of an output utility derived from an IPO based computer system is closely coupled to the quality of its input media, the measure of the Optimal Quotient (OQ) is the ratio of the input to output which is 1:1. This relationship ensures that all IPO based computers are not just linearly predictable, but also characterized by the Garbage In Garbage Out (GIGO) design concept. While current IPO based computer systems have been relatively successful at delivering some measure of optimization, there is a need to examine (Li & Malik, 2016) alternative methods of achieving optimization. The purpose of this quantitative research study, through an experimental research design, is to determine the effects of the application of a Swarm Intelligence algorithm on the efficiency of the Cosmos Open Source Managed Operating System.

By incorporating swarm intelligence into an improved IPO design, this research addresses the need for optimization in computer systems through the creation of an improved operating system Scheduler. The design of a Swarm Intelligence Operating System (SIOS) is an attempt to solve some inherent vulnerabilities and problems of complexity and optimization otherwise unresolved in the design of conventional operating systems. This research will use the Cosmos open source operating system as a test harness to ensure improved internal validity while the subsequent measurement between the conventional and improved IPO designs will demonstrate external validity to real world applications.

Indexing (document details)
Advisor: Bradley, Jama, O’Donnell, Tom
School: Northcentral University
Department: Business and Technology Management
School Location: United States -- California
Source: DAI-B 79/09(E), Dissertation Abstracts International
Subjects: Artificial intelligence
Keywords: Swarm intelligence
Publication Number: 10810882
ISBN: 978-0-355-93909-5
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