## 2 Related Work  
Ansible has efficiently managed physical and virtual machines remotely in university lab administration. The importance of Ansible was demonstrated in various research areas in computer science. One of the earlier research papers from 2015 discusses the use case for Ansible to deploy applications. The applications deployed over the cloud are dynamic, i.e., the resource requirements of such applications are amended very quickly. DevOps needs to support these changes in deployment conditions at run time. Ansible is one of the prominent answers to such dynamic behavior of applications [1]. The capabilities of Ansible can also be used in different environments where the mobility of non-cloud-native applications is a concern. An innovative way utilizes Ansible playbooks to enable application migration between various Linux distributions, circumventing the constraints of conventional techniques like kickstart and Autoyast. This approach facilitates fast cross distribution portability without system downtime, demonstrating Ansible's adaptability in resolving persistent challenges in application transfer [2]. A further study addresses the security ramifications of Ansible scripts, concentrating on \"task infections\" where security flaws, including hardcoded passwords, disseminate through infrastructure management tasks. The research identified substantial hazards, including 1805 task infections among 27,213 scripts. This study highlights the necessity of addressing security issues in Infrastructure as Code methods, especially in sensitive contexts such as cloud infrastructure [3]. A critical study from 2024 highlights the quality and capability of implementing Ansible playbooks. An empirical review of 2952 open-source Ansible playbooks revealed that 71.5% were inoperable due to flaws, underscoring significant quality issues in publicly accessible resources. The primary problems arise from discrepancies in configurations and diverse categories of failures. This analysis highlights the necessity for more stringent creation and validation procedures in community-shared playbooks [4]. Making correct workload scheduling decisions using Kubernetes is paramount, especially in the HPC environments. For example, contemporary research in scheduling HPC workloads might benefit from implementing this mechanism to obtain even more data to base ML-based Kubernetes scheduling decisions [5]. This ensures we place the workload optimally based on computer resources and network capabilities. Enhancements to Ethernet networks such as Priority-based Flow Control (PFC), RDMA over Converged Ethernet (RoCE), and Enhanced Transmission Selection (ETS) on a 100 Gb/s Ethernet network configured as a tapered fat tree have been researched in the past [6]. The authors conducted tests that simulated various network operating conditions to analyze performance results. Their findings indicated that RoCE running over a PFC-enabled network significantly increased performance for bandwidth-sensitive and latency-sensitive applications compared to TCP. Additionally, they found that ETS can prevent network traffic starvation for latency-sensitive applications on congested networks. Although they did not encounter notable performance limitations in their Ethernet testbed, they concluded that traditional HPC networks still hold practical advantages unless additional external requirements drive system design. Research has also been done regarding the Quality of Service (QoS) of Ethernet networks based on packet size, addressing the need for predictable, reliable, and guaranteed services in Ethernet networks, as standardized by IEEE 802.3 [7]. Through simulations using the Riverbed modeler 17.5, they analyzed QoS metrics like end-to-end delay, throughput, jitter, and bit error rate for varying packet sizes, mainly focusing on sizes more significant than the 1500-byte Maximum Transmission Unit (MTU) of Ethernet.