AI-Assisted Resource Scheduling in Multi-Cloud Computing Environments

  • Authors

    • Mr. Michael Anderson Director of Information Technology, ABC Technologies Inc, USA. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2024PI3J6N

    Published 02-05-2024

  • Artificial Intelligence, Multi-Cloud Computing, Resource Scheduling, Machine Learning, Reinforcement Learning, Cloud Resource Management, Sla Optimization, Intelligent Scheduling, Predictive Analytics, Cloud Computing

    Issue

    Vol. 7 No. 1 (2024)

    Section

    Articles

    How to Cite

    [1]
    M. Anderson, “AI-Assisted Resource Scheduling in Multi-Cloud Computing Environments”, IJADSMC, vol. 7, no. 1, pp. 01–17, Feb. 2024, doi: 10.67228/30713498/IJADSMC-2024PI3J6N.

  • Abstract

    Cloud computing has evolved significantly, with multi-cloud environments becoming popular for improving scalability, reliability, fault tolerance, and cost efficiency. However, resource scheduling in multi-cloud systems remains challenging due to heterogeneous infrastructures, dynamic workloads, varying pricing models, network latency, and SLA requirements. Traditional scheduling methods such as Round Robin and FCFS often fail to adapt effectively to these complexities. Artificial Intelligence (AI) offers an advanced solution through intelligent resource scheduling. By leveraging machine learning, reinforcement learning, and predictive analytics, AI-based schedulers can forecast workloads, optimize resource allocation, and make autonomous scheduling decisions. This research proposes an AI-driven resource scheduling framework that integrates workload prediction, resource classification, intelligent scheduling, and continuous feedback mechanisms. The framework aims to optimize multiple objectives, including cost reduction, execution efficiency, energy consumption, and SLA compliance. Performance evaluation using metrics such as resource utilization, makespan, response time, throughput, and energy consumption demonstrates that AI-assisted scheduling outperforms traditional approaches. The results indicate improved resource utilization, better workload balancing, reduced operational costs, and enhanced service quality, highlighting the potential of AI-driven scheduling for next-generation multi-cloud resource management systems.

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