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Research Article
Cloud-Based Resource Management for Scalable Application Deployment
Ebole Alpha Friday*,
Ayinde Yusuf Olatunji
,
Alli Kazeem Oluwatosin
Issue:
Volume 12, Issue 1, June 2025
Pages:
1-15
Received:
10 December 2024
Accepted:
22 December 2024
Published:
10 February 2025
Abstract: The growing adoption of cloud computing has underscored the critical need for efficient resource management to ensure scalability and reliability in modern applications. This paper explores key strategies for addressing challenges and solutions in cloud resource management, identifying best practices and essential performance indicators for optimizing resource allocation through a detailed analysis of various approaches. It emphasizes the importance of integrated frameworks that enhance performance, reduce costs, and support diverse workloads. Dynamic provisioning enables real-time resource allocation based on demand, preventing both overprovisioning and underutilization. Auto-scaling adjusts resources automatically to accommodate workload fluctuations, maintaining application performance during peak usage and minimizing costs during low demand periods. Machine learning-driven load balancing predicts traffic patterns, strategically distributing workloads to reduce latency and improve reliability. By examining multiple strategies, the study identifies optimal practices and critical metrics for resource management, such as response time, throughput, and cost-effectiveness, which are essential for evaluating the success of these approaches. The findings underscore the value of frameworks that seamlessly integrate automated decision-making, predictive analytics, and adaptable algorithms to meet the diverse demands of modern applications. It also provides a comprehensive review of current methods and offers actionable recommendations to enhance the scalability and dependability of cloud-based systems. These advancements are crucial for aligning cloud systems with the dynamic needs of contemporary applications, fostering innovation, and ensuring the long-term sustainability of cloud computing solutions.
Abstract: The growing adoption of cloud computing has underscored the critical need for efficient resource management to ensure scalability and reliability in modern applications. This paper explores key strategies for addressing challenges and solutions in cloud resource management, identifying best practices and essential performance indicators for optimiz...
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Research Article
Detection of Brain Tumors Using Optimized Features in Clustering Techniques for Enhanced Model Development with Accuracy
Yavanaboina Tezaaw*,
Kumba Vijaya Lakshmi
Issue:
Volume 12, Issue 1, June 2025
Pages:
16-22
Received:
18 February 2025
Accepted:
3 March 2025
Published:
21 March 2025
Abstract: The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they produce depending on their location, size, and type. Tumor classification has traditionally relied on histopathological examination, but molecular insights are becoming crucial in improving accuracy and treatment strategies. Clustering techniques particularly DBSCAN (Density-Based Spatial Clustering of Applications with Noise) identify molecular subtypes in brain tumor datasets, specifically genetic expression data from the GSE50161 dataset. The goal is to improve the detection of Brain Tumor patterns ultimately contributing to better diagnostic, prognostic, and treatment strategies for the patients. Identifying distinct molecular subtypes through genetic expression data can assist in creating personalized treatment plans for patients. By categorizing tumors more accurately, clinicians can choose therapies that target specific molecular mechanisms leading to better outcome Ultimately leading to enhanced accuracy in brain tumor detection.
Abstract: The growth of cells in the brain or nearby tissues, known as brain tumors, they may be termed benign(non-cancerous) or malignant(cancerous) and can cause various symptoms depending on their location and size. Brain tumor, both benign and malignant cause significant clinical challenges due to their complexity and the diverse range of symptoms they p...
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Research Article
Enhancing the Performance of AODV Routing Protocol for Selfish Node Detection in MANET
Abebaw Mebrat,
Ermias Melku Tadesse*,
Tarekegn Walle Yirdaw,
Abubuker Girma
Issue:
Volume 12, Issue 1, June 2025
Pages:
23-34
Received:
10 March 2025
Accepted:
27 March 2025
Published:
19 April 2025
DOI:
10.11648/j.wcmc.20251201.13
Downloads:
Views:
Abstract: Recently, a better approach to access computing services is necessary because of the growing popularity of portable computers and consumer needs. Self-configuring wireless networks without a defined infrastructure are known as mobile ad hoc networks, or MANETs. MANETs are susceptible to a range of assaults because of their dynamic network architecture, lack of central monitoring, and inadequate security measures. Detecting a node's misbehavior in a MANET and successfully validating the selfish node using an algorithm for detecting selfish nodes are the main goals of this study. The discovery results in decreased retransmission and improved performance across all network parameters. In this study, the routing algorithm used was AODV. The suggested approach is implemented using the NS2 simulation tool. Our suggested technique enhances the packet delivery ratio, throughput, and reduces packet drop and delay—all of which are network metrics that are compared and analyzed—both with and without selfish nodes. The suggested AODV protocol improved the simulation study based on the routing performance in terms of throughput, packet lost, packet delivery ratio, and end-to-end delay. However, the simulation result analysis revealed that the end-to-end delay reduced from 1.902 to 1.08, the throughput improved from 674.52 to 724.521, the packet delivery ratio improved from 85.60 to 87.6638, and the packet lost improved from 34.40 to 32.38. We came to the conclusion that the suggested Selfish node detection algorithm showed improvement in all performance parameters examined.
Abstract: Recently, a better approach to access computing services is necessary because of the growing popularity of portable computers and consumer needs. Self-configuring wireless networks without a defined infrastructure are known as mobile ad hoc networks, or MANETs. MANETs are susceptible to a range of assaults because of their dynamic network architect...
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