## 2 Background and Related Work  
In today's dynamic IT landscape, the deployment of applications in cloud computing environments has undergone a paradigm shift, moving away from traditional on-premises solutions towards the adoption of cloud-based platforms. This transition was driven by the desire for higher availability, scalability, and the removal of constraints on computational resources, all while enabling rapid provisioning of services and pay-as-you-go cost models. Amidst this evolution, Kubernetes has emerged as the de facto standard for container orchestration, automating the deployment and management of containerized applications within cloud clusters. It is worth noting that this shift has not only been observed by the scientific community but has also been actively fueled by researchers and practitioners, as evidenced by numerous publications on this topic. Interesting insights on the topic of deploying containerized systems to cloud environments are presented by [PERSON], [PERSON], and [PERSON] [4], where the authors addressed the challenges brought about by the increasing demand for cloud-based applications and the necessity for efficient resource utilization. This analysis encompasses a thorough exploration of container technologies, most notably Docker, and a meticulous investigation of orchestration tools such as Docker Swarm and Kubernetes. Through their research, the authors reveal the impact of these technologies on service availability within clusters, offering valuable insights through comparative analyses. The paper culminates with the presentation of research findings, which aim to enhance the performance of distributed computing systems in cloud contexts. On the other hand, in other studies [5], researchers delve into the domain of virtualization technologies within high-performance computing (HPC) environments. Historically, the application of virtualization in HPC has been discouraged due to perceived performance overhead. However, with the emergence of container-based virtualization solutions such as Linux VServer, OpenVZ, and Linux Containers (LXC), a significant transformation has occurred, promising minimal overheads and performance nearly comparable to native setups. Within this research, the authors have conducted a meticulous series of experiments with the aim of providing an extensive performance evaluation of container-based virtualization in HPC. These results dispel any doubts regarding the effectiveness of container technology in the context of deploying applications in the cloud. However, while containerization has proved its worth in building efficient applications, its application alone is not a sufficient guarantee of achieving optimal resource utilization. Hence, the key focus shifts to building scalable applications, which can dynamically adjust the utilization of individual cloud computing resources to the current system load. Kubernetes, as an orchestration platform for running microservices in computing clusters, implements various approaches to scale the applications. However, in the context of deployment in cloud computing environments, the most critical component is the horizontal pod autoscaler (HPA), which is responsible for monitoring the current workload and dynamically allocating computing resources, including provisioning new cluster nodes, to ensure the entire application can meet the current system load. It is, thus, clear to see why horizontal scaling and fine-tuning of HPA in Kubernetes clusters constitute hot topics in scientific research. For example, the study titled \"Leveraging Kubernetes for Adaptive and Cost-Efficient Resource Management\" [6] aims to investigate how Kubernetes, a prevalent container orchestration framework, can enhance applications with adaptive performance management. The research emphasizes the utility of Kubernetes' horizontal and vertical scaling conceptsfor adaptive resource allocation, along with an assessment of oversubscription as a method to simulate vertical scaling without application rescheduling.