# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 4 The PingBasedDown Algorithm
### Decision Model
#### 4.2.2 Leader Determination Module
We then define the detection rate to be \(\mathcal{R}_{D}=\frac{\mathcal{N}_{f}\times T_{\mathit{setup}}}{S_{f}\times T _{\mathit{Trun}}}\), where \(\mathcal{S}_{f}\) denotes how many faulty nodes are there in this test case, \(N_{fr}\) denotes how many times faulty nodes being removed and \(T_{\mathit{run}}\) denotes the total testing duration of this test. The mis-kicking rate \(\overline{R}_{k}\) indicates the severity of incorrect Downing process which causes healthy nodes being removed from the cluster. We define the mis-kicking rate to be \(\overline{\mathcal{R}_{k}}=\frac{N_{r}-N_{r}}{N_{r}}\), where \(N_{r}\) denotes the total quantity of downing actions. A higher \(\mathcal{R}_{D}\) and a lower \(\overline{\mathcal{R}_{k}}\) indicate a better performance. #### 5.1.3 Simulation Scenarios

We run three groups of experiments to check the accuracy of our algorithm compared with baseline methods. In the first group of experiment, we compare the precision-recall and F1 score with fixed optimized parameter and varying packet loss rate. In the second group of experiment, we compare the precision-recall and F1 score with a fixed packet loss rate and varying parameters, i.e., number of records of RTT and thresholds. In the last group, we apply our algorithm on a real-world product and compare the result with the baseline method. ### Evaluation Results

#### 5.2.1 Adaptability of Environment

We present the link evaluation results with varying packet loss rate of the three algorithms in Figure 4. This group of experiment indicates the adaptability of each algorithm with different packet loss rates. We fix the parameters of each algorithm. The parameters of each algorithm are set as follows. For PFD, the size of historical heartbeats is set to 100, and the threshold \(T_{\phi}\) is set to 0.45. For EDFD, the size of historical heartbeats is set to 1000, and the threshold \(T_{ed}\) is set to 0.65. For 2 WFD, the size of large window \(n_{1}\) is set to 2000, the size of small window \(n_{2}\) is set to 10, and the decision time \(\alpha\) is set to 0. For AV and CV, \(N_{w}\) (number of records) is set to 30. The thresholds for AV are: \(T_{safe}=0.6\), \(T_{alert}=1.5\). The threshold for CV is \(T_{c}=1.0\). From the results, we can observe that our algorithm achieves the highest F1 score in each network environment, which proves that our evaluation method is accurate in detecting the severity of different packet loss rates. #### 5.2.2 Impact of Parameters

Figure 5 shows the impact of parameters on the performance of each algorithm. In this group of experiment, we fix the packet loss rate to 12.5%. Figure 4(a) shows the result with varying thresholds. We select 4 groups of typical thresholds for each algorithm. Table 5 shows the thresholds we use. We fix the size of historical heartbeats of PFD, EDFD, 2 WFD the same as the latter group of experiment, and fix the length of records \(N_{w}\) of AV and CV to be 30. Figure 4(b) shows the result with varying record

Figure 4: Performance of Evaluation model vs. packet loss rates. sizes. We check four different groups of sizes of records and fix the other parameters as same as the former experiment (Adaptability of Environment). Table 6 shows the count of records we use in this group of experiment. Results show that, with different parameters, the performance of our algorithm is higher than the other four algorithms. Specifically, the accuracy can be improved by adjusting the number of records \(N_{w}\) to higher values. Results of Real-world Testing To test and verify the reliability of our algorithm, we apply the algorithm on a product named AgileController. This product is provided by Huawei Inc. It is developed based on a famous open source SDN controller, i.e., OpenDaylight. OpenDaylight supports the feature of constructing a SDN controller cluster to provide most of the advantages brought by distributed systems. Specifically, the OpenDaylight project adopts Akka cluster service, which provides a decentralized and gossip protocol-based membership management service.