Esharov Elzarbek Asanovich

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: IoT Smart networks are the latest creation of smart technology where Internet of Things, Artificial Intelligence, and Blockchain technologies have merged. Such technologies have the possibility of increasing performance, security and the degree of expansion in different fields like smart city, health and manufacturing. As it is, there are several issues that organisations continued to encounter when implementing both these systems in order to address diversified network requirements. Objective: The study aims to define how IoT, AI, and Blockchain technologies can be integrated to develop smart networks and how their integration will address the issues of performance, data integrity, and resource utilization in smart networks. Methods: The solution consisted of three components: IoT for instant data gathering, AI for modeling and efficient traffic control, Blockchain for secure data storage. Analyses of various objectives such as data throughput, latency, energy consumption, and security were conducted for smart city applications through simulations. Results: The linked matrix obtained a 45% increase in data transfer rate, a 40% cut in response time and a 50% enhancement of power utilization compared to other systems. Purchases made using blockchain were correct to the last digit, achieved with a success rate of 99.9%, and there were no cases of hacking. AI algorithms minimized congestion levels of the network by 55%, and IoT devices remained available 98% of the time. Conclusion: The incorporation of the IoT, AI and Blockchain enhances the effectiveness and assures the stability of smart networks greatly. From these findings, there is a significant potential for broad utility thus the need for research on the scale, integration, and testing of these in practice.