## 2 Related Work
The results showed distinct variations in network performance based on packet size, highlighting the importance of managing these parameters for optimizing network QoS. In their study, the authors underscore the necessity of proper segmentation to improve performance and reduce packet loss and delay. Evaluation has also been done for network processing performance and power efficiency over 10 GbE on Intel Xeon servers [8]. Authors broke down per-packet processing overhead, revealing that driver and buffer release accounted for 46% of processing time for large I/O sizes and 54% for small I/O sizes, alongside data copy. They identified bottlenecks in the NIC driver and OS kernel using hardware performance counters. Furthermore, their study examined power consumption using a power analyzer and an external Data Acquisition System (DAQ) to profile components like CPU, memory, and NIC. These findings guided recommendations for a more efficient server I/O architecture to enhance performance and reduce power consumption in high-speed networks. Performance measurements on 10 Gigabit Ethernet have been done using commodity hardware for high-energy physics experiments [9]. This study evaluated maximum data transfer rates through a network link, considering limitations imposed by the memory, peripheral buses, and the processing capabilities of CPUs and operating systems. Using standard and jumbo Ethernet frames, they measured TCP and UDP maximum data transfer throughs and CPU loads for sender/receiver processes and interrupt handlers. Additionally, they simulated disk server behavior in a Storage Area Network, forwarding data via a 10 Gigabit Ethernet link. Their findings emphasized the impact of jumbo frames in reducing CPU load and improving throughput. Researchers also explored the implementation of Energy Efficient Ethernet (EEE) in HPC systems [10]. The study analyzes EEE's power savings and performance impact, showing that EEE can achieve up to 70% link power savings but may lead to a 15% increase in overall system power consumption due to performance overheads. The authors propose a \"Power-Down Threshold\" to mitigate this, reducing the on/off transition overhead from 25% to 2%, thus achieving overall system power savings of about 7.5%. Future research should optimize these strategies for better integration into HPC systems. They highlight the need for vendor-specific design decisions to fully leverage EEE in HPC environments, emphasizing the balance between energy savings and performance impacts. Challenges include addressing latency overheads and ensuring seamless integration with existing HPC infrastructure to maximize efficiency gains. Additionally, exploring adaptive mechanisms to further reduce power consumption without compromising performance is recommended. The benchmarking study has been performed on four CNCF-recommended CNI plugins in a physical data center environment to evaluate their performance in Kubernetes [11]. The study assessed latency and average TCP throughput across various Maximum Transmission Unit (MTU) sizes, the number of aggregated network interfaces, and different interface segmentation offloading conditions. Results were compared against a bare-metal baseline, revealing significant performance variations among the plugins. Kube-Router demonstrated the highest throughput, followed by Flannel and Calico. The findings underscore the importance of selecting appropriate CNI plugins to optimize Kubernetes network performance, particularly in environments with varying network interface configurations. Other research has been done to analyze the performance of several CNI plugins, including in Kubernetes environments [12]. Benchmarking tests conducted under various scenarios in this paper aimed to identify plugins that provided higher throughput and lower resource usage (CPU and memory), focusing on compute overhead. The study found that Kube-Router achieved the highest throughput in inter-host data exchanges, utilizing more than 90% of the nominal link bandwidth on TCP data transfer.