## 4 Performance Evaluation and Analysis
### Efficiency Evaluation Result
#### 4.2.2 QR Code Recognition Testing of the Proposed Orchestration System  
The evaluation of the proposed cloud structure method of QR code identification was done using the test bed explained previously. While the previous evaluation was done using network function, this identification method was done as a means of user service evaluation. Before discussing the detailed disposition of the service as a user service, the comparison between QR code identification and the previous identification should be made to establish bases on which to evaluate user service. An environment had to be created so that QR code image identification could be compared with previous means of identification. In order to use the image-matching method, 10 QR sample images were photographed from 15 different angles from 0\({}^{\circ}\) to 70\({}^{\circ}\) at 5\({}^{\circ}\)-intervals, as shown in Figure 16 and each image had a width \(\times\) height of 6.2 cm \(\times\) 6.0 cm. Each code was photographed from 15 cm away using a smart phone camera and saved after being binary-encoded. The smartphone used for the test was a Nexus 4, which had an 8.0-megapixel digital camera. Before the evaluation, in order to prove that the proposed method has a wider identification angle compared to the previous one, verification was done to check that the test was conducted under the same conditions. The verification was done with a QR code identification application available on the market to check how the identification of the QR01 code, which had an angle of 0\({}^{\circ}\)-70\({}^{\circ}\), was done at different angles. The applications used were the Naver app (Naver) and Zxing's Barcode Scanner app [26; 27]. The results of the test are shown in Table 3.  
When the Naver application was used, the application was able to identify the code from an angle of 35\({}^{\circ}\) at a 15-cm distance but was not able to identify the code at a 40\({}^{\circ}\) angle. The Barcode Scanner application was able to identify a code at a 40\({}^{\circ}\) angle at the same distance but was not able to identify the code at a 45\({}^{\circ}\) angle. This shows that the application using the previously explained finer pattern method has some weakness in handling images taken at different angles. On the other hand, the proposed method went through similarity testing to verify whether it was able to identify images at angles of 45\({}^{\circ}\) or more. For the test, we used a 60\({}^{\circ}\) QR01 code with 150 sample QR images and conducted a similarity comparison to determine whether the proposed method was able to identify the image. The test was done to examine whether the QR01 code captured at different angles had similarity to the 60\({}^{\circ}\) image to investigate whether there was a high rate of similarity with other sample QR codes captured at 60\({}^{\circ}\). As shown in Figure 17, QR01's 60\({}^{\circ}\) image showed a critical value of above 90%. This method was able to identify previously unidentified The proposed virtual network orchestration system has the special characteristic of sharing a network service system to set the service. The previous 150 QR code images were sent to different nodes connected to the database to test performance. For performance testing, the test was done to check the identification speed when the image database was set at different nodes for the image-matching process. The test was conducted while using one to five nodes in a database and was conducted five times for different numbers of nodes and the average rate was analyzed. The time required for the client to send and receive an image to and from the server as well from the server to different nodes for decoding was not included in the test. The results of the test are shown in Figure 18. 36.78 s were required to compare one QR code to 150 images in the database one by one for similarity testing. When two databases were used to match the image, 18.57 s were required, which was half the time required when using one database.