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Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I

Research Article

DRL Based Multi-objective Resource Optimization Technique in a Multi-cloud Environment

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-48888-7_9,
        author={Ramanpreet Kaur and Divya Anand and Upinder Kaur},
        title={DRL Based Multi-objective Resource Optimization Technique in a Multi-cloud Environment},
        proceedings={Cognitive Computing and Cyber Physical Systems. 4th EAI International Conference, IC4S 2023, Bhimavaram, Andhra Pradesh, India, August 4-6, 2023, Proceedings, Part I},
        proceedings_a={IC4S},
        year={2024},
        month={1},
        keywords={Multi-cloud Deep reinforcement learning Resources allocation Cyber shake seismogram workflow Task scheduling Enhanced Flower Pollination},
        doi={10.1007/978-3-031-48888-7_9}
    }
    
  • Ramanpreet Kaur
    Divya Anand
    Upinder Kaur
    Year: 2024
    DRL Based Multi-objective Resource Optimization Technique in a Multi-cloud Environment
    IC4S
    Springer
    DOI: 10.1007/978-3-031-48888-7_9
Ramanpreet Kaur1,*, Divya Anand2, Upinder Kaur3
  • 1: Department of Computer Application, Lovely Professional University
  • 2: Department of Computer Science and Engineering, Lovely Professional University
  • 3: Department of Computer Science and Engineering, Akal University
*Contact email: ramaninsa1990@gmail.com

Abstract

The concept of multi-cloud becomes interesting progressively to cloud users because of its high response time, flexibility, high throughput, and reliability. But at the ground level, the concept of multi-cloud creates many challenges for researchers. The request of users and the multi-cloud environment is heterogeneous now a day. To work in this kind of environment required an intelligent system. Researchers are doing well in this field to make the whole process very flexible by providing an intelligent environment. The proposed multi-objective resource optimization deep reinforcement learning (MOROT-DRL) model uses the Q-learning technique of Deep Reinforcement Learning (DRL) to allocate resources in a multi-cloud environment. It includes a service analyzer for analyzing the requests and MET(Minimum Execution Time)algorithm used for scheduling the task according to execution time and then enhanced flower pollination allocate the optimized resources for the demanded request. The comparison of the proposed model is done with simulation results of MOROT and neural network model and also implemented on GoCS real dataset of google. The proposed model gives better results when compared based on energy, CO2,and cost.

Keywords
Multi-cloud Deep reinforcement learning Resources allocation Cyber shake seismogram workflow Task scheduling Enhanced Flower Pollination
Published
2024-01-05
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-48888-7_9
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