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- W4378976937 abstract "IoT sensors and devices are being used in more and more applications every day. Cloud computing has been introduced in order to meet the storage and processing demands for time-sensitive applications on low-powered end devices that leverage IoT devices and sensors. Processing and analysis of IoT workloads are carried out by virtual machines (VMs) housed inside of actual computers. It is an NP-complete issue to allocate VMs inside a physical machine (PM). If a VM is unsatisfied with the provided service, the VM may decide to re-join the line as a feedback customer. Whenever the system is empty, the physical machine may take a working vacation rather than remaining in the system idle. Working vacations and vacation service times are both independent random variables with exponential distributions. An M/M/1 feedback queue in steady-state with balking, reneging, and recurrent working vacations (WV) have been applied in the VM allocation to the PMs. The matrix approach is used to determine the model's steady-state system length distributions. A population based meta-heuristic approach teaching learning based optimization (TLBO) technique is provided for cost optimization and performance features of the VM allocation. Numerical findings presented as tables are used to perform sensitivity analysis." @default.
- W4378976937 created "2023-06-02" @default.
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- W4378976937 date "2023-04-26" @default.
- W4378976937 modified "2023-10-16" @default.
- W4378976937 title "Cost Analysis and Optimization of Virtual Machine Allocation in the Cloud Data Center" @default.
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- W4378976937 doi "https://doi.org/10.1109/icict57646.2023.10134312" @default.
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