Abstract :

In Modern Cloud Computing Environments, Efficient Resource Scheduling Plays A Crucial Role In Maximizing Provider Profit While Ensuring User Satisfaction. Traditional Scheduling Algorithms Primarily Focus On Technical Parameters Such As Processing Time, Cost, And Resource Utilization, Often Neglecting User Behaviour And Request Value. This Paper Proposes A Personality And Value-Aware Scheduling (PVAS) Approach That Integrates User Personality Traits And Request Value Analysis Into The Scheduling Process To Optimize Both User Experience And Cloud Profit. The Proposed System Classifies User Requests Based On Behavioural Patterns—such As Urgency, Patience Level, And Willingness To Pay—derived From User Interaction History And Service-level Preferences. A Multi-objective Optimization Model Is Then Applied To Schedule Requests Dynamically, Balancing Resource Allocation Efficiency And Profit Maximization. Machine Learning Techniques Are Employed To Predict User Value And Satisfaction, Enabling Adaptive Decision-making In Real Time.

Published:

28-10-2025

Issue:

Vol. 25 No. 10 (2025)

Page Nos:

192-200

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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