## 2 Related Work
The paper \"Catfish: Adaptive RDMA-Enabled R-Tree for Low Latency and High Throughput\" presents an RDMA-enabled R-tree designed to handle large multidimensional datasets in distributed systems [25]. The authors address performance challenges where R-tree servers are often overloaded while networks and client CPUs need to be more utilized. Catfish uses RDMA to balance workloads between clients and servers, employing two fundamental mechanisms: fast messaging, which uses RDMA writes to achieve low query latency, and RDMA offloading, which offloads tree traversal to clients. The adaptive switching between these methods maximizes overall performance. Experiments demonstrate that Catfish significantly outperforms traditional TCP/IP schemes, achieving lower latency and higher throughput on InfiniBand networks. Future research is suggested to optimize adaptive mechanisms further and explore deployment in diverse network environments. In 2019, researchers proposed LLDP-looping, a novel latency monitoring method for software-defined networks (SDNs) [26]. This method uses LLDP packets injected repeatedly in the control plane to determine latency between switches, providing continuous and accurate latency monitoring without dedicated network infrastructure. LLDP-looping minimizes overhead and achieves high measurement accuracy, with evaluations demonstrating over 90% accuracy compared to Ping in an SDN with link latency as small as 0.05 ms. The method can be applied to various networking scenarios with minimal modifications to SDN switches. In 2020, a multimodal deep learning-based method for estimating network latency in large-scale systems was also researched. The system uses a deep learning algorithm to predict latencies from a small set of RTT measurements, significantly reducing the overhead associated with traditional measurement methods. The AI model achieved high accuracy, outperforming existing techniques, with a 90 th percentile relative error of 0.25 and an average accuracy of 96.1%, demonstrating its effectiveness for large-scale network latency estimation [27]. In 2020, researchers proposed an ultra-low latency MAC/PCS IP for high-speed Ethernet, targeting data centers with high data throughput and transmission delay requirements. The study analyzed low-latency technologies and implemented a simulation experiment to measure latency on different platforms. The proposed MAC/PCS IP showed significant latency improvements compared to existing solutions, making it suitable for applications requiring ultra-low latency in high-speed Ethernet environments [28]. The architecture used in this paper significantly differs from any of the papers mentioned in this section. It's based on an automated approach to deployment and performance evaluation of selected Kubernetes CNIs (Antrea, Cilium, Calico, and Flannel) using a set of built-in and researched, customized tuned profiles. It's also cross-platform and can be easily deployed across x86 and ARM architectures while the RISC-V ecosystem works on achieving full Kubernetes compatibility. Let us now briefly overview the technology and services used for this methodology. To better understand our methodology's architecture, Ansible and a stack of backend services (MySQL and Grafana) will be discussed next.