Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization

Authors

DOI:

https://doi.org/10.4108/eetsis.5716

Keywords:

Multi-cloud, Deep reinforcement learning, Resource allocation, Cyber shake seismogram workflow, Task scheduling Enhanced Flower Pollination

Abstract

INTRODUCTION: The multi-data canter architecture is being investigated as a significant development in meeting the increasing demands of modern applications and services. The study provides a toolset for creating and managing virtual machines (VMs) and physical hosts (PMs) in a virtualized cloud environment, as well as for simulating various scenarios based on real-world cloud usage trends.

OBJECTIVES: To propose an optimized resource management model using the Enhanced Flower Pollination algorithm in a heterogeneous environment.

METHODS: The combination of Q-learning with flower pollination raises the bar in resource allocation and job scheduling. The combination of these advanced methodologies enables our solution to handle complicated and dynamic scheduling settings quickly, making it suited for a wide range of practical applications. The algorithm finds the most promising option by using Q-values to drive the pollination process, enhancing efficiency and efficacy in discovering optimal solutions. An extensive testing using simulation on various datasets simulating real-world scenarios consistently demonstrates the suggested method's higher performance.

RESULTS: In the end, the implementation is done on AWS clouds; the proposed methodology shows the excellent performance by improving energy efficiency, Co2 Reduction and cost having multi-cloud environment  

CONCLUSION: The comprehensive results and evaluations of the proposed work demonstrate its effectiveness in achieving the desired goals. Through extensive experimentation on diverse datasets representing various real-world scenarios, the proposed work consistently outperforms existing state-of-the-art algorithms.

Author Biography

Ramanpreet Kaur, Lovely Professional University

Baba Farid College of Engineering and Technology

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Published

10-04-2024

How to Cite

1.
Kaur R, Anand D, Kaur U, Verma S. Integrative Resource Management in Multi Cloud Computing: A DRL Based Approach for multi-objective Optimization. EAI Endorsed Scal Inf Syst [Internet]. 2024 Apr. 10 [cited 2024 May 18];. Available from: https://publications.eai.eu/index.php/sis/article/view/5716

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Research articles