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Go to Editorial ManagerThis paper proposes a new load balancing algorithm for data center networks by means of exploiting the characteristics of Software Defined Networks. Mininet was utilized as an emulation tool for the purpose of emulating and evaluating the proposed design, Miniedit was utilized as a GUI tool for the same purpose. In order to obtain a realistic environment to the data center network, Fat-Tree topology was utilized with the following parameters; 4 pods, 16 edge switches, 16 aggregation switches, 4 core switches, and 16 hosts. Different scenarios and traffic distributions were applied in order to cover as much possible cases of the real traffic. POX controller was chosen as an SDN controller.The suggested design showed outperformance when compared to the traditional scheme in term of throughput and loss rate for all the evaluated scenarios. The first scenario assumes joining of new hosts while in the second scenario; there was an increase in the demand of the already established connections. The proposed algorithm showed a loss free performance in the first scenarios, whereas, the traditional scheme presented 15% to 31% loss rate for the same scenario. In the second scenario, the proposed algorithm recorded up to 81% improvement in the loss rate when compared to the traditional scheme. Moreover, the proposed algorithm showed a superiority over the traditional scheme in term of throughput, where it maintained the throughput intact without any reduction in the first scenario in contrast to the traditional scheme that underwent from a considerable degradation in the throughput value. The traditional scheme underwent from an average throughput reduction of 5Mbps in the case of joining of new hosts (first scenario). In the second scenario, both schemes underwent from a throughput reduction, however, the proposed scheme always showed superiority over the traditional scheme, whereas, it recorded up to 16.6% improvement in the throughput average value.
As internet network developed rapidly in the past ten years, and its operating environment is constantly changing along with the development of computer and communication technology, the congestion problem has become more and more serious. Since TCP is the primary protocol for transport layers on the internet, the data transmitted via the transport protocol utilizes Vegas Transmission Control Protocol (TCP) as the congestion control algorithm, where it uses increasing in delay round trip time (RTT) as a signal of network congestion. However, this congestion control algorithm will attempt to fill network buffer, which causes an increase in (RTT) determined by Vegas, thereby reducing the congestion window, and making the transmission slower, Therefore Vegas has not been widely adopted on the Internet. In this paper, an improved algorithm called TCP Vegas-A is proposed consist of two parts: the first part is sending the congestion window used by the algorithm for congestion avoidance along with the TTL (Time To Live) mechanism that limits the lifetime of a packet in the network. While the second part of the algorithm is the priority-based packet sending strategy, and jitter is used as a congestion signal indication. The combination of the two is expected to improve the efficiency of congestion detection. A mathematical model is established, and the analysis of the model shows that the algorithm has better effects on controlling congestion and improving the network throughput, decreasing packet loss rate and increasing network utilization, the simulation is done using NS-2 network simulation platform environment and the results support the theoretical analysis.
In the last two decades, underwater acoustic sensor networks have begun to be used for commercial and non-commercial purposes. In this paper, the focus will be on improving the monitoring performance system of oil pipelines. Linear wireless sensor networks are a model of underwater applications for which many solutions have been developed through several research studies in previous years for data collection research. In underwater environments, there are certain inherent limitations, like large propagation delays, high error rate, limited bandwidth capacity, and communication with short-range. Many deployment algorithms and routing algorithms have been used in this field. In this work a new hierarchical network model proposed with improvement to Smart Redirect or Jump algorithm (SRJ). This improved algorithm is used in an underwater linear wireless sensor network for data transfer to reduce the complexity in routing algorithm for relay nodes which boost delay in communication. This work is implemented using OMNeT++ and MATLAB based on their integration. The results obtained based on throughput, energy consumption, and end to the end delay.
This study evaluates the performance and efficiency of four deep learning models—VGG-16, ResNet-50, Inception-V3, and DenseNet-121—in detecting pneumonia from chest X-rays, addressing the critical need for balanced accuracy and computational efficiency in clinical diagnostics. Methods: A dataset of 5,234 chest X-rays (3,875 pneumonia, 1,341 normal) was augmented via rotation, flipping, and zooming to mitigate class imbalance. Models were trained on an RTX 2060 GPU for 40 epochs, with performance assessed using accuracy, F1 score, sensitivity, specificity, precision, and computational metrics (training time, memory usage). Statistical significance was validated via paired t-tests (p < 0.05). Results: DenseNet-121 achieved the highest accuracy (95.2% ± 0.8), F1 score (95.1% ± 0.7), and throughput (400 images/sec) with minimal memory usage (33MB). ResNet-50 and Inception-V3 showed moderate performance, while VGG-16 exhibited overfitting tendencies. In conclusion, DenseNet-121 showed strong performance compared to other models, both in terms of accuracy and processing speed, which is essential for use in real-time clinical settings. However, the small size of the validation set and limited population diversity are important limitations that should be addressed in future studies. Moreover, more testing on larger datasets is needed to confirm the stability of the model and see how the model will work in different settings. Future work should address ethical considerations in AI-driven diagnostics and validate findings across multi-institutional datasets.