## 2 Related Work
Flannel and Calico followed in performance rankings. The research highlights the critical role of CNI plugin selection in optimizing Kubernetes network performance and resource efficiency. A comprehensive assessment of various CNI plugins in Kubernetes, focusing on functionality, performance, and scalability, was done in 2021 [13]. The authors compared open-source CNI plugins through detailed qualitative and quantitative measurements, analyzing overheads and bottlenecks caused by interactions with the datapath/iptables and the host network stack. The study highlighted the significant role of overlay tunnel offload support in network interface cards for achieving optimal performance in inter-host Pod-to-Pod communication. Scalability with increasing Pods and HTTP workloads was also evaluated, providing valuable insights for cloud providers in selecting the most suitable CNI plugin for their environments. A study also focused on benchmarking different network visualization architectures in Kubernetes deployments tailored for telco data plane configurations. The study evaluated the performance of various architectures, including OvS-DPDK, Single Root Input Output Virtualization (SR-IOV), and Vector Packer Processing (VPP), leveraging industry-standard testing methodologies and an open-source testing framework [14]. The research examined the effects of different resource allocation strategies on data plane performance, such as CPU-pinning, compute and storage resource variations, and Non-Uniform Memory Access (NUMA). The findings underscored the importance of optimizing resource allocation strategies to enhance data plane performance in telco Kubernetes deployments. Researchers also investigated the sufficiency of Kubernetes' resource management in isolating container performance, focusing on performance interference between CPUintensive and network-intensive containers and multiple network-intensive containers [15]. Their evaluation revealed that containers experience up to 50% performance degradation due to co-located containers, even under Kubernetes' resource management. The study identified CPU contention, rather than network bandwidth, as the root cause of performance interference. The authors recommended that Kubernetes consider CPU usage of network-related workloads in resource management to mitigate performance interference and improve overall service quality. A GENEVE (Generic Network Virtualization Encapsulation) tunnel in a multi-cloud environment at Trans-Eurasia Internetworking (TEIN) with sites in Malaysia and South Korea was deployed for a performance evaluation [16]. The study highlighted GENEVE's advantage in transferring larger payloads due to its smaller header size than other Layer 2 tunnels like GRE or VXLAN. They emphasized the importance of monitoring tools for network and instance performance forecasting. Machine learning algorithms can use data from these tools to predict optimal cloud and instance performance, ensuring resource sharing is efficient and not overutilized. Their implementation demonstrated that GENEVE tunnels could effectively facilitate communication and resource sharing between cloud instances. Researchers also proposed an SDN-based optimization for VXLAN in cloud computing networks [17]. They introduced an intelligent center to enhance multicast capabilities and facilitate VM migration within the VXLAN architecture. This new architecture addresses issues like signaling overhead during multicast periods and communication interruptions with migrating VMs. Their Mininet emulation demonstrated effective load balancing and highlighted the potential improvements in VXLAN's multicast efficiency and VM migration facilitation. This work provides a blueprint for future cloud computing networks to enhance their performance and reliability. The Segment-oriented Connection-less Protocol (SCLP), a novel Layer 4 protocol for existing tunneling protocols like VXLAN and NVGRE, was introduced and evaluated by researchers [18].