# Networking for Cloud Robotics: The DewROS Platform and Its Application
## 6 Experimental Evaluation
### Tools and Methodology  
In this section, the methodology and tools used for our experimental analysis are presented. Regarding the Cloud services provided by Google and Amazon, the client can send an annotation request for a video stored locally or in the Cloud, obtaining a list of annotations with their level of confidence. The following label detection modes are available for CVI: _Unspecified_ Mode--coarse-grained labels extraction, providing contextual information at the level of the entire video, seen as a single large segment; _Shot_ Mode--provides contextual information at the shot level, so that every label can be detected in multiple video segments; _Frame_ Mode--fine-grained label extraction, providing contextual information at the frame level; _Shot&Frame_ Mode--this includes all the previously described label detection modes. At first, the labels obtained from the cloud services at four different resolutions of the same input video are analyzed to find out whether and how this factor influences the outcomes. To perform an extensive assessment, three videos have been considered, labeled as A, B, C, each one downloaded at the maximum resolution 1080p and scaled down to 720p, 360p, and 240p. In this context, the labels obtained at different video resolutions have been compared in terms of quantity, confidence level, and pertinence. In particular, the label associated with a video is classified as non-pertinent if it is clearly out of context, namely, if it has nothing to do with the entities seen in the video. Moreover, the level of confidence indicates how certain the service is about the detected entity. This expresses the probability that the service would return the same label if it analyses the same video several times. Afterward, the behavior of the network connection used to reach the Cloud Video intelligence platform was analyzed, studying the CVI response time of label detection requests with different test parameters. To validate the performance of the satellite technology, the satellite network (SAT) was compared with two other Internet connections: one from the University of Naples Federico II, using a Gigabit Ethernet for local connectivity and part of the Garr/Geant network, and a residential broadband connection using FTTH from one of the main Telecom Operators in Italy, respectively defined as UNI and HOME. Regarding the label detection modes, the _Unspecified_ and _Shot&Frame_ label detection modes were considered as the most simple and most difficult label detection tasks. In addition, five video duration values and sizes were considered, in order to evaluate the effect of these variables on the CVI response time. In particular, three different sizes were considered for each video type. Namely, _small_ (2.1 MB), _medium_ (8.8 MB) and large (23.7 MB) and two different lengths, namely _Short_ (3 s) and _Long_ (71 s). Finally, the videosthat need to be analyzed were placed on the host connected through the network under test, as well as directly onto _GCS_. Each test case considered in this section is represented by a specific combination of these parameters and replicated 10 times, obtaining a total of 600 observations. The considered variables are reported in Table 2.  
The observations were taken on several consecutive days, at different times. The basic performance parameters of the network connections under test are shown in Table 3. The values are measured using python-version of Ookla speed test, _speedtest-cli_, with Ubuntu Linux 16.04. Furthermore, several traffic traces using _Wireshark_ were captured while making the API call to CVI, and varying the video size, length, and label detection mode. The capture process was repeated five times for every specific combination of these factors. Then, every trace was manually inspected to detect the similarities and differences.