# NetAP: Adaptive Polling Technique for Network Packet Processing in Virtualized Environments
## 3 Design
### Adaptation Using Golden-Section Search
#### 3.4.1 Considerations for Alternative Algorithms
The VM specifications and the network configurations are the same as in previous experiments. This experimental setup shows the impact of CPU performance, which has a critical effect on network performance in a virtualized environment. In terms of BogoMips, a single CPU core that was used in the previous experiment recorded 6192, whereas a single CPU core in this experiment recorded 5200. Thus, the CPUs that wew used in this experiment approximately exhibit a 20% lower performance. We applied NetAP to this system without additional modifications. The experimental configuration and method are identical to those presented in Section 4.2. Figure 6 shows similar results to Figure 4, which indicates that NetAP improved the aggregate bandwidth as compared



## 5 Conclusions

This paper presented NetAP, an adaptive polling technique for improving the performance of packet processing in VMs. The periodic polling approach of NetAP resolves the inefficient packet processing of the existing VirtIO, which is interrupt-based. We used the golden-section search algorithm and implemented NetAP on KVM hypervisor to find the optimal polling interval for NetAP. We implemented NetAP in Linux v5.1.5 and evaluated it with up to six VMs. The evaluation results showed that NetAP improves the network performance of VMs by up to 31.16%, while using the host CPU for packet processing at only 32.92% of the usage exhibited by the existing VirtIO technique. In this paper, we discovered areas for future work in adaptive polling-based network packet processing. First, accurate control or adaptation for the performance isolation between VMs should be provided for real-world cloud systems. When the VMs share the same host machine, the workloads of the VMs influence each other. This \"neighborhood effect\" of the virtualized system incurs the performance interference between VMs on the same host machine. When the performance of a VM decreases due to other co-located VMs, the golden-section search algorithm receives erroneous performance by the neighborhood effect and it has difficulty in finding the near-optimal polling interval to achieve the maximum performance. Another issue is the estimation of the optimal polling interval. If we can determine the optimal polling interval for a workload without carrying out adaptation, the performance degradation during \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**4 VMs, 2 Threads Each**} & \multicolumn{4}{c}{**5 VMs, 1 Thread Each**} \\ \hline
**Period (s)** & **0-20** & **20-40** & **40-60** & **Comparison** & **0-20** & **20-40** & **40-60** & **Comparison** \\ \hline Bandwidth (Gbps) & 3.54 & 4.48 & 4.58 & 129.15\% & 2.14 & 2.76 & 2.81 & 131.16\% \\ \hline CPU-guest (\%) & 708.88 & 761.52 & 762.00 & 107.49\% & 617.28 & 552.32 & 603.76 & 97.81\% \\ \hline CPU-host (\%) & 74.40 & 26.32 & 25.60 & 34.41\% & 102.56 & 34.88 & 33.76 & 32.92\% \\ \hline Latency-mean (us) & 0.98 & 0.83 & 0.85 & 87.07\% & 1.19 & 0.91 & 0.92 & 76.88\% \\ \hline Latency-stddev (us) & 40.42 & 19.74 & 20.29 & 50.18\% & 6.97 & 2.58 & 2.08 & 29.77\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Detailed performance and CPU utilization in 4 VM-1T and 5 VM-1T for the additional experiment setup. Figure 6: Aggregate bandwidth and polling interval of each VM over time for the additional environment. adaptation will disappear. The data that can be employed for the estimation of the optimal polling interval include the average packet size, packet incoming rate, current throughput of the physical NIC, and the number of vHost and client threads. The correct model cannot be easily developed because several of the measurements are related to the interval. In our future work, we aim to develop an AI-based model in order to solve this problem. The work presented here was completed in collaboration between all authors. conceptualization, H.P., K.L., and C.-H.H. ; validation, H.P., K.L., and C.-H.H. ; formal analysis, H.P. ; investigation, H.P., J.S. and M.L.