A Novel NSGA-III-GKM++ Framework for Multi-Objective Cloud Resource Brokerage Optimization

Ahmed Yosreddin Samti (), Ines Ben Jaafar, Issam Nouaouri and Patrick Hirsch ()
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Ahmed Yosreddin Samti: Strategies for Modeling and ARtificial inTelligence (SMART Lab), University of Tunis, Bardo, Tunis 2000, Tunisia
Ines Ben Jaafar: Strategies for Modeling and ARtificial inTelligence (SMART Lab), University of Tunis, Bardo, Tunis 2000, Tunisia
Issam Nouaouri: Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), Université d’Artois, 62400 Béthune, France
Patrick Hirsch: Institute of Production and Logistics, BOKU University, Feistmantelstr. 4, 1180 Vienna, Austria

Mathematics, 2025, vol. 13, issue 13, 1-29

Abstract: Cloud resource brokerage is a fundamental challenge in cloud computing, requiring the efficient selection and allocation of services from multiple providers to optimize performance, sustainability, and cost-effectiveness. Traditional approaches often struggle with balancing conflicting objectives, such as minimizing the response time, reducing energy consumption, and maximizing broker profits. This paper presents NSGA-III-GKM++, an advanced multi-objective optimization model that integrates the NSGA-III evolutionary algorithm with an enhanced K-means++ clustering technique to improve the convergence speed, solution diversity, and computational efficiency. The proposed framework is extensively evaluated using Deb–Thiele–Laumanns–Zitzler (DTLZ) and Unconstrained Function (UF) benchmark problems and real-world cloud brokerage scenarios. Comparative analysis against NSGA-II, MOPSO, and NSGA-III-GKM demonstrates the superiority of NSGA-III-GKM++ in achieving high-quality tradeoffs between performance and cost. The results indicate a 20% reduction in the response time, 15% lower energy consumption, and a 25% increase in the broker’s profit, validating its effectiveness in real-world deployments. Statistical significance tests further confirm the robustness of the proposed model, particularly in terms of hypervolume and Inverted Generational Distance (IGD) metrics. By leveraging intelligent clustering and evolutionary computation, NSGA-III-GKM++ serves as a powerful decision support tool for cloud brokerage, facilitating optimal service selection while ensuring sustainability and economic feasibility.

Keywords: cloud computing; multi-objective optimization; NSGA-III; K-means++; cloud brokerage (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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