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@article{190926,
        author = {Jeewesh Jha and Prof Dr Gurjeet Singh and Prof Dr Sudhir Pathak and Abhidha Verma},
        title = {Secure Cloud Resource Orchestration: Blockchain + AoI-Aware Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {4011-4023},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=190926},
        abstract = {This paper proposes a novel cloud resource scheduling framework that explicitly integrates the Age of Information (AOI) metric into the scheduling decision process, enabling direct quantification and optimization of information freshness. The framework employs an enhanced deep reinforcement learning (DRL) approach to learn adaptive scheduling policies in dynamic cloud environments. A multidimensional reward function is designed to jointly optimize AOI, resource utilization, and task completion performance, allowing system-level freshness optimization without compromising efficiency. To improve learning stability and convergence, prioritized experience replay and n-step learning are incorporated into the training process. Extensive simulation results demonstrate that the proposed framework consistently achieves lower average AOI under diverse workload conditions while satisfying resource capacity and energy consumption constraints. These findings provide both theoretical insights and practical guidance for improving real-time cloud service quality and supporting timely decision-making in cloud and edge computing environments.},
        keywords = {Age of Information (AOI) Cloud resource scheduling Deep reinforcement learning Real-time optimization Edge computing},
        month = {January},
        }