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@article{201455,
        author = {Yuvaraj Gandhi S and Revathi. T},
        title = {Workload Scheduling Optimization using Dispersive Fly Optimisation in Cloud Paradigm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {3779-3793},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201455},
        abstract = {Virtual Machine (VM) integration methods use two vital technical aspects, virtual machine communication and virtual machine I/O interception, to optimise load balancing in cloud data. Virtual Machine communication facilitates the seamless input and output data stream flow between software packages executed within individual virtual machines. Virtual Machine I/O interception mechanisms play a crucial role in redirecting the input and output of application packages. This virtual machine communication and virtual machine i/o interception uses resource utilisation and resource allocation on the cloud network to make efficient task scheduling by designing, optimising, and securing workload scheduling. This will be achieved by balancing cost-effectiveness, maximising resource utilisation, ensuring top-notch service quality, avoiding service level agreement violations, and optimising workload performance, which is crucial for achieving operational excellence. Many research articles do not concentrate on these challenges. The limited resource-level provisioning is causing difficulties in obtaining accurate and detailed workload fluctuations, described by multiple layers of noise. The Long-term Potentiation (LTP) model is vital in signal transmission between two neurons. This research presents a hybrid model using neuroscience, including Dispersive Fly Optimisation and transcranial random noise stimulation (tRNS) (“NDFO- tRNS”) for dynamic workload provisioning in a cloud network. The proposed NDFO- tRNS framework forecasts cloud resource utilisation. Finally, the LTP module simulates and predicts the VM workload. The proposed model efficiently integrates resource utilisation and workload scheduling in a cloud network.},
        keywords = {Cloud Network; Dispersive Fly Optimisation; Resource Allocation; Virtual Machine Integration; Workload Scheduling;},
        month = {May},
        }