TOPICAL TENDENCIES IN OPTIMIZING LOGGING AND MONITORING PROCESSES IN CLOUD SYSTEMS

Abstract

The increasing popularity of cloud systems has led to a heightened role for logging and monitoring processes for ensuring their reliability, availability, and security. Currently these processes really have an essential importance. Effective use of logging and monitoring tools enables developers and support teams to understand the real-time behavior of cloud-deployed information systems and promptly respond to emerging issues. This paper analyzes current trends in this field. The purpose of the work. It investigates existing solutions for optimizing logging and monitoring processes in cloud systems. The methodology. Their detailed analysis was conducted. The advantages and disadvantages of these solutions were highlighted. Existing problems in ensuring proper optimization of logging and monitoring processes in cloud systems were identified. And practical recommendations for their step-by-step resolution were offered. The presented study covers such key aspects as a detailed overview of the infrastructure of logging and monitoring processes (software stacks, cloud platforms, SaaS solutions), tools and platforms for implementing these systems (ELK Stack, CloudWatch, Azure Monitor), and their automation (AIOps, machine learning). The scientific novelty. It is determined that today optimization of logging and monitoring processes is critically important for cloud systems, as it provides the reliability, high availability, and security of cloudbased information systems. A comprehensive approach to implementing logging and monitoring systems is offered, which should include log filtering, aggregation, and compression; anomaly detection in logs and metrics; the formation of key performance indicators (KPIs) for monitoring in accordance with the requirements of the information system; setting threshold values for receiving notifications about possible problems; analysis and visualization of monitoring data; and failure prediction using machine learning. Conclusions. Nowadays optimization of logging and monitoring processes in cloud systems is one of the key factors for the successful operation of modern organizations that strive to improve the stability of information systems, increase the level of data security, and ensure high availability of such systems. Therefore, it can be concluded that modern research in the field of cloud systems, which is aimed to develop new models and IT for improving the implementation of logging and monitoring processes in cloud systems, has significant prospects for development.