ANALYSIS OF MONOLITHIC AND MICROSERVICE ARCHITECTURES FEATURES AND METRICS

Abstract

In this paper the information technologies stack is presented. Thesetechnologies are used during network architecture deployment. The analysis of technological advantages and drawbacks under investigation for monolithic and network architectures will be useful during of cyber security analysis in telecom networks. The analysis of the main numeric characteristics was carried out with the aid of Kubectl. The results of a series of numerical experiments on the evaluation of the response speed to requests and the fault tolerance are presented. The characteristics of the of monolithic and microservice-based architectures scalability are under investigation. For the time series sets, which characterize the network server load, the value of the Hurst exponent was calculated.

The research main goal is the monolithic and microservice architecture main characteristics analysis, time series data from the network server accruing, and their statistical analysis.

The methodology of Kubernetes clusters deploying using Minikube, Kubectl, Docker has been used. Application deploy on AWS ECS virtual machine with monolithic architecture and on the Kubernetes cluster (AWS EKS) were conducted.

The investigation results gives us the confirmation, that the microservices architecture would be more fault tolerance and flexible in comparison with the monolithic architecture. Time series fractal analysis on the server equipment load showed the presence of long-term dependency, so that we can treat the traffic implementation as a self-similar process.

The scientific novelty of the article lies in the application of fractal analysis to real time series: use of the kernel in user space, kernel latency, RAM usage, caching of RAM collected over 6 months with a step of 10 seconds, establishing a long-term dependence of time series data.

The practical significance of the research is methodology creation of the monolithic and microservice architectures deployment and exploitation, as well as the use of time series fractal analysis for the network equipment load exploration.