COLLECTING METRICS FOR CONTINUOUS PLATFORM MONITORING

ABSTRACT

This article examines the collection of metrics for continuous platform monitoring in the context of modern requirements for stability and performance. It analyzes the principles of metric classification, their role at various stages of the platform lifecycle, as well as methods for data processing and visualization. Special attention is given to the integration of metrics with development processes such as CI/CD and monitoring tools including Prometheus, Grafana, Fluentd, Logstash, Elastic Stack, and Datadog. Theoretical and practical approaches are proposed to improve monitoring efficiency and enhance platform quality. The article also discusses the prospects of using metrics for predicting potential issues, increasing platform resilience, and optimizing performance under growing workloads.

АННОТАЦИЯ

В статье рассматривается вопрос сбора метрик для непрерывного мониторинга состояния платформ в условиях современных требований к стабильности и производительности. Анализируются принципы классификации метрик, их роль на различных этапах жизненного цикла платформы, а также методы обработки и визуализации данных. Особое внимание уделяется интеграции метрик с процессами разработки, такими как CI/CD, и инструментам мониторинга, включая Prometheus, Grafana, Fluentd, Logstash, Elastic Stack и Datadog. Предложены теоретические и практические подходы для повышения эффективности мониторинга и улучшения качества платформ. В статье также обсуждаются перспективы использования метрик для прогнозирования потенциальных проблем, повышения отказоустойчивости платформ и оптимизации их производительности в условиях растущих нагрузок.

Keywords: platform monitoring metrics, metric classification, Prometheus, Grafana, Fluentd, CI/CD.

Ключевые слова: метрики мониторинга платформы, классификация метрик, Prometheus, Grafana, Fluentd, CI/CD.

Introduction

In the context of rapid technological advancements and increasing demands for the stability and security of software platforms, monitoring their state has become an integral part of the development and operational processes. Effective control over platform performance, based on the collection and analysis of metrics, enables timely identification of issues, optimization of resources, and enhancement of user experience. In recent years, as systems have grown in scale and architectural complexity, the need for monitoring has become particularly relevant, with metrics playing a crucial role in maintaining high performance and reliability.

The purpose of this study is to provide a theoretical foundation for methods of collecting and analyzing metrics for continuous platform state monitoring. The study examines the classification of metrics and their role at different stages of the platform lifecycle. Particular attention is given to the theoretical aspects of integrating monitoring systems into the development process, including the use of modern technologies and approaches such as DevOps, CI/CD, and machine learning, to ensure timely responses to system state changes.

Main part. Classification and structure of metrics for platform monitoring

Monitoring the state of a platform during development and operation is a crucial aspect of ensuring the stability, performance, and security of software systems. In this context, a platform comprises a combination of software, hardware, and network components that enable the execution of various tasks, from data processing to user interaction [1]. Unlike traditional applications, platforms typically feature more complex architectures that include distributed components, interactions with external services and systems, and the need for high availability and fault tolerance. Monitoring these platforms serves to observe their state in real-time, enabling the prediction of potential failures, prevention of overloads, and efficient resource management.

Classification of metrics for monitoring the platform is included in the organizational effective process of control over the system state [2]. Metrics are the basis of diagnostics and decision-making, and selection and classification of indicators should be performed in such a way to allow the best reflection of the current state of the platform (fig.1).

Figure 1. Categories of metrics for platform monitoring

System metrics describe the consumption of platform resources and performance, including CPU load, memory usage, disk space, network bandwidth, and response time. These metrics are important in the general evaluation of the state of the platform and in the particular creation of performance degradation issues. For example, one can consider high response times or narrow bandwidth as an indication to optimize algorithms or scale the system, whereas CPU and memory load problems generally indicate bad code or inefficient resource management.

Operational metrics such as uptime, failure frequency, MTTR, and request success rates measure the reliability, availability, and stability of the platform. These metrics will help evaluate user experience and system performance under load. Monitoring uptime tracks availability, while failure frequency and MTTR highlight problem areas and recovery efficiency.

Security metrics involve unauthorized access attempts, successful attacks, and detected vulnerabilities-those are so important in threat monitoring and prevention [3]. Security analysis is about detecting incidents, such as data breaches or attacks, in monitoring systems [4]. For instance, log monitoring of access attempts enables prompt responses to unusual activities and thus potential threats.

Additionally, user experience (UX) metrics can be highlighted, which include indicators such as page load time, client-side error frequency, and user satisfaction metrics. These metrics are crucial for assessing the quality of user interactions with the platform and can provide insights into how various system changes affect user perceptions. For instance, an increase in page response time or a rise in errors might indicate frontend issues that require deeper optimization.

The structure of metrics for the monitoring of the platform should be flexible and change with respect to the characteristics of a specific system. It enables responses against changes in architecture, functional requirements, and operating conditions effectively by providing necessary data completeness and accuracy for analysis (table 1).

Table 1.

Structure of metrics for platform monitoring

Models of metric representation play a key role in organizing effective monitoring and analysis of platform conditions. Among the important notions here is the differentiation between the concepts of measurements, indicators, and metrics. Measurements are specific numeric values that reflect some aspect of the current state of the system at a given moment in time. They can be the primary data that will be collected for platform monitoring, such as response time or CPU load. Indicators are measures that, interpreted in a certain context, convey a broader meaning about the state of the system. Indicators often support decision-making based on predefined thresholds or trends. For example, response time, if it exceeds some predefined value, may act as an indicator of a performance problem. Metrics, in turn, are more complex data gathered in order to analyze and assess overall system performance. They can merge measurements and indicators, enabling profound insight not only into the current state but also into the dynamics of changes in various aspects of the platform. This is very important in order to reveal anomalies and perform the optimization of system performance (table 2).

Table 2.

Examples of differences between indicators, metrics, and measurements for platform monitoring

The theoretical justification of the importance of different categories of metrics varies depending on the stage of the platform lifecycle. During the development stage, attention is given to metrics that will help the development team monitor system performance, identify code errors, and ensure high-quality testing of features. At this stage, key metrics include performance indicators, response times, and code error rates, as well as indicators that can assist in the timely identification and resolution of problems.

During the testing phase, the metrics become more specific: stability indicators, failure frequencies, and recovery times. These metrics will give the degree to which the system is capable of operating under real conditions and resisting various levels of load.

At the operational stage, the focus would then shift to ensuring stability and performance over long-term use. Here, operational metrics in terms of uptimes, frequency of errors, and security metrics are crucial to reduce risks and threats to data and to the system at large.

Thus, at each stage of its life cycle, different categories of metrics are important, which allow for system stability, security, and efficiency. Proper classification and structuring of metrics are vital for an efficient monitoring system. One can control on a high level of abstraction in such a case: the current state of the platform with minimal risk from performance, reliability, and security viewpoints.

The theory of metric collection and analysis in the development process

Metric collection and analysis in platform development should be a systematic process involving technical tools, ways of data processing, and integration with development workflows. At the stage of development, metrics have numerous functions: these are code performance evaluation, potential errors detection, and testing processes optimization. So, to perform effective monitoring, the implementation of several crucial steps is called for: collection, preprocessing, data analysis, and visualization of the results.

In practice, metrics are collected using specialized monitoring tools such as Prometheus, Grafana, Datadog, or Elastic Stack (table 3).

Table 3.

Monitoring tools for platforms [5]

Data collection behind platform monitoring is based on several principles, which guarantee the efficiency and reliability of the process. Aggregation means the process of consolidating data from various sources into a single, unified system for further processing and analytics. This is particularly important in distributed systems where metrics are gathered from many services, nodes, or containers. Sampling describes the regularity with which metrics are collected and at what level of detail. For example, all metrics regarding CPU load and memory usage can be sampled every 5-10 seconds with good enough accuracy for subsequent analysis. Finally, scalability is again crucial for highly loaded systems: monitoring solutions are supposed to digest an increasing volume of data without performance deterioration. To pursue this goal, it heavily relies on horizontal scaling together with distributed data storage systems, such as Prometheus, which efficiently process time series data.

Usually, data needs some preprocessing before the analysis. This may include such processes as normalization, deduplication, and filtering out noise in large datasets. This is especially true for log-related metrics, as raw data may be redundant. Such processing can be done with the help of tools like Fluentd or Logstash, which transform raw data into structured data ready for analysis (fig. 2).

Figure 2. Example of Logstash in operation

Data aggregation is also important, as it allows the combination of metrics from multiple sources. For instance, if response time metrics are gathered from several servers, aggregated data can represent average, minimum, or maximum values, simplifying high-level analysis.

Both basic statistical methods and advanced approaches, such as forecasting and modeling, are used to analyze collected metrics. Statistical methods help identify important trends, averages, medians, and anomalies. For example, the average response time for a given period of time gives an overview of general performance, while standard deviation is indicative of significant deviations from the average. Forecasting methods predict future values of the metric, such as server load or system failure. For example, seasonal data can be analyzed for load peaks using models such as ARIMA or Prophet. On the other hand, modeling allows the creation of simulations to study how different configurations of the platform affect performance and reliability. This approach is particularly fit for testing scalability and fault tolerance. The most sophisticated of these are machine learning algorithms. For example, various forms of classification or clustering can predict the likelihood of system failures. For instance, a time series algorithm applied to predict server load might automatically scale resources to proactively avoid overload.

The results of the analysis should be provided in a form that is easily interpretable [6]. Visualization of metrics with tools such as Grafana allows the creation of dashboards displaying platform state in real time (fig. 3).

Figure 3. Visualization of time-series status data using Grafana

Prometheus is also used for data visualization. It is a powerful tool that is not only used for collecting and storing metrics but also plays an important role in data visualization. While Prometheus has its own basic visualization capabilities, it is often integrated with advanced tools like Grafana to provide a more comprehensive and interactive monitoring experience [7]. Through this integration, Prometheus enables the creation of detailed dashboards that present real-time data on key platform metrics, such as CPU usage, memory consumption, network performance, and system uptime (fig. 4).

Figure 4. Example of node status monitoring using Prometheus

Of course, the inclusion of monitoring systems into development processes such as CI/CD assures maximum efficiency. The approach will automate the collection and analysis of metrics in every stage of development and deployment. Metrics can be collected automatically upon every build regarding test coverage and performance, while results are presented to the developers for assessments of the change quality.

Metrics are a backbone in both predictions and the avoidance of problems; therefore, these are especially vital for ensuring reliability. Time series analysis of metrics provides the indication of trends to predict further load. Thus, resources can be scaled well in advance before performance degradation is noticed. Such an example can be scheduling more computational resources during times of day when analysis indicates server load spikes.

Besides, automation of quality and reliability management is heavily based on metrics. Modern systems of monitoring are integrated with tools of orchestration and alerting, and perform certain actions automatically according to changes of metrics [8]. By way of example, if an error rate grows higher than an expected threshold, all traffic can be automatically routed into backup servers, preventing platform disruption.

Therefore, metrics acquisition and analysis during development should be treated with due care: tool selection, proper configuration of the data processing flow, and integration into the operational processes. Metric collection done pragmatically not only makes it possible to gauge the current state of the platform but also allows for predictions of forthcoming issues, thus improving the stability and efficiency of the platform.

Conclusion

Effective collection and analysis of metrics are critical for ensuring platform stability, performance, and security. Metrics help assess the system's state, identify problem areas, predict potential failures, and optimize resources. Throughout the platform lifecycle – development, testing, and operation – metrics play a vital role in improving system quality, adapting to increasing loads, and minimizing risks. Tools like Prometheus, Grafana, and Fluentd automate monitoring and visualization, enhancing platform transparency and manageability.

Correct metric classification, selection, and integration with development processes like CI/CD ensure that the monitoring system will be flexible and scalable, especially for complex, microservice-based platforms. Metrics provide the basis for real-time decision-making and long-term strategies toward better user experiences, reliability, and SLA observance-thus, they lay the foundation for robust, efficient platforms.

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