Enhancing Cloud Service Selection and Orchestration with DALMOCS: A Dynamic Adaptive Learning and Multi-Criteria Decision Analysis Approach

Main Article Content

Claus Pahl
Mohamed Mohsen Gammoudi
Fulvio Risso
Giuseppe Tricomi

Abstract

This paper introduces the Dynamic Adaptive Learning Model for Optimized Cloud Service Selection (DALMOCS), an innovative framework designed to enhance cloud service selection and orchestration by leveraging adaptive learning techniques and a specialized Multi-Criteria Decision Analysis (MCDA) approach. DALMOCS dynamically adjusts selection parameters and criteria weights in real-time, significantly improving the decision-making process. Our quantitative analysis demonstrates the model's efficacy, with DALMOCS achieving a 92% accuracy in service selection and a 95% user satisfaction rate under baseline conditions. It maintained high adaptability with indices of 0.75 and 0.80 under dynamic market conditions and varying user requirements, respectively, alongside consistent execution times, showcasing its efficiency and resilience. These results highlight DALMOCS's potential to offer a robust, adaptable, and efficient solution for navigating the complexities of the cloud services market, marking a significant advancement in cloud computing research and application.

Article Details

How to Cite
[1]
Claus Pahl, Mohamed Mohsen Gammoudi, Fulvio Risso, and Giuseppe Tricomi, “Enhancing Cloud Service Selection and Orchestration with DALMOCS: A Dynamic Adaptive Learning and Multi-Criteria Decision Analysis Approach”, Int. J. Comput. Eng. Res. Trends, vol. 11, no. 2, pp. 18–26, Feb. 2024.
Section
Research Articles

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