Talib Kalefa Hasan

Background: The cloud-native architectures have reinvented the original strategies of the companies’ IT infrastructure approach and became popular due to the concepts of modularity, scalability, and resilience. These architectures respond to the shortcomings of the monolithic architectures to meet the new business challenges and workloads, including embracing innovation technologies like Artificial Intelligence and big data processing solutions. Objective: This study was designed with the objective of assessing the performance and business viability of cloud-native systems, based on critical indicators such as availability, resilience to failure, resource use, and compatibility with innovative technologies. The objective was to define the barriers and possibilities for improving cloud native architectures in various enterprises. Methods: A cross-sectional research, consideration, experiment test and case study and performance analysis. Response time, CPU and memory consumption and recovery time were compared across the range of throughput from 1000 to 12000 requests per second. To enhance the interpretational framework, key usage scenarios in the three sectors of healthcare, retail and finance were collected and compared with the results. Results: Cloud-native systems proved to provide high availability rates (> 99.9%), resource scalability, and component resource efficiency. With the use of AI in combination with big data analytics, improvement in performance was realized. But some of the problems that were seen include vendor lock, integration issues, and fluctuating peak load issues. Conclusion: All identified improvements signify the potential of cloud-native architectures for improving enterprise IT functioning. It is thus possible to continue perfecting the identified challenges to enhance their effectiveness, optimal for the current dynamic digital environment.

  Background: The marriage of 5G and Artificial Intelligence (AI) has been brought forward as a key enabler of Industry 4.0 and smart city applications. These technologies solve the problem of latency, scalability, and energy use, providing technology support for real-time decision-making and efficient organization of work. Nevertheless, studies regarding their individual and collective effects in a plethora of industrial and urban contexts are still limited. Objective: The objective of this research is to assess the performance, energy saving, and expansibility of 5G and AI synergies in manufacturing, logistics, healthcare, and smart city applications and highlight their challenges and potential for further exploration. Methods: An experimental data collection, mathematical modeling and comparative analysis approach was employed. Performance indicators including latency, possible and actual throughput, power usage, and predicting achievement were measured in real pilot tests implemented in dense networks and IoT contexts. Available data were compared with other similar studies to gain an understanding of the results. Results: The conjoin with 5G and AI suggested potential optimization of process; the latency has been decreased to more than 90%, its predictive maintenance was sharpened, and its power consumption was decrease to 75%. The feasibility of extending scalability and system reliability of the protocol was confirmed in dense IoT environments, with further potential for emission reduction. Conclusion: The study identifies the use of 5G in Industry 4.0 with AI in addressing dynamic issues but potential drawback includes scalability and security. More studies should be conducted on the novel hybrid architectures and 6G integration concerning more extensive areas.