DEVOPS STABILITY IMPACT ON SCALABILITY OF DISTRIBUTED SYSTEMS

Abstract

The active growth of cloud computing and distributed computer systems has led to the introduction of microservice architectures and cloud development paradigms. Given the diversity of modern systems that combine several software technologies, machine learning, and IoT elements, there is a need to develop flexible scalable systems. The main approaches to address the scalability issue are the implementation of load balancing stages, container orchestration, and strategies for allocating additional resources. Modern software ecosystems increasingly integrate heterogeneous technologies, including machine learning components, Internet of Things devices, and large-scale data processing services, which significantly increases architectural complexity and operational demands. Under these conditions, ensuring system scalability becomes a critical requirement for maintaining performance, reliability, and service continuity. Although DevOps is widely recognized for improving software delivery efficiency and operational collaboration, its quantitative impact on scalability in distributed environments remains insufficiently investigated. Existing solutions are based on the development of specialized architectures that do not always include a sufficient number of DevOps elements, which in turn does not allow for maximum performance. Despite the practical aspect, the quantitative relationship between the use of DevOps and the scalability of distributed systems remains insufficiently studied. This article introduces the quantitative DevOps Stability Index and examines its impact on scalability indicators in distributed microservice systems. Using this index, the study investigates the relationship between DevOps adoption and scalability indicators in distributed microservice systems.