No abstract

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases.

No abstract

In this research, we assess the impact of collisions produced by simultaneous transmission using the same Spreading Factor (SF) and over the same channel in LoRa networks, demonstrating that such collisions significantly impair LoRa network performance. We quantify the network performance advantages by combining the primary characteristics of the Capture Effect (CE) and Signature Code (SC) approaches. The system is analyzed using a Markov chain model, which allows us to construct the mathematical formulation for the performance measures. Our numerical findings reveal that the proposed approach surpasses the standard LoRa in terms of network throughput and transmitted packet latency.