Mohammed K. H. Al-Dulaimi

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: Quantum computing has posed a profound threat to the classical cryptographic systems as it is advancing at an exponential rate with the help of quantum algorithms like Shor’s and Grover’s which can easily decipher the Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) algorithms. Huge requirements for cryptographic frameworks that can withstand quantum hacking have inspired Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), and systems that use both. Objective: The aim of this article is to review the performance, scalability and integration of quantum-secure cryptographic services, with a practical lens on how they can be used in real-time environments like self-driving cars, industrial IoT, and intelligent health systems. It also aims at establishing the drawback of the current model and directions for further enhancement. Methods: The study employs simulative experimentation to understand lest exposures to quantum algorithms and rates cryptographic systems on standards such as latency, Quantum Bit Error Rate (QBER), computational overhead, scalability, and cost. Comparative assessment furniture integrated analysis of QKD, PQC, and hybrid system by identifying the advantages and disadvantage of each system. Results: As a result, adopting hybrid systems provided the best or comparable median results with lowest latency in real-time applications of ~45 ms or lower compared to alternative Multi-Access Edge Computing (MEC) architectures and types of security elements at high scalability. Thus, QKD, while being exceptional in security, has the problem of scalability, while PQC had average results on the given parameters. Conclusion : Quantum threats are adequately dealt with by hybrid cryptographic systems as this study has also pointed out. It is seen that initiation to future work may someday distribute resources effectively, expedite PQC standardization, and embrace artificially intelligent network frameworks for flexibility and expansiveness across different networks.