# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 4 MC-BDP Prototype Evaluation
### Experimental Results
#### 4.1.7 Container Network Utilisation (GB Sent and Received by the Job Manager)  
Based on the summary results presented in Table 9 for total number of GB received and sent over the network by the job manager during the technology agnosticism experiments, the following conclusions were drawn: (i) total number of GB received by the job manager over the network by velocity for technology agnosticism--all means for three or six workers (Beam, Flink) x (single, multiple) are not significantly different from each other event from each other except for six workers (multiple) x six workers (multiple). However, means are significantly different for all possible combinations [Beam, Flink] [(three workers (single, multiple) x six workers (single, multiple)], except for [Beam] [(three workers (multiple) x six workers (multiple)]; (ii) total number of GB sent by the job manager over the network by velocity for technology agnosticism--all means for three or six workers (Beam, Flink) x (single, multiple) are not significantly different from each other. However, means are significantly different for all possible combinations [Beam, Flink] [(three workers (single, multiple) x six workers (single, multiple)]. This corroborates the premise that the greater the number of workers in the cluster, the greater the parallelisation of work, which leads to increased communication between containers across the network.  
Results (in the form of graphs and regression models) for the number of gigabytes received and sent by the job manager during the technology agnosticism experiments by velocity are tabulated in Table 11.  
Based on the summary results presented in Table 10 for the total number of GB received and sent by the job manager over the network during the windowing rate versus resource utilisation experiments, the following conclusions were drawn: (i) total number of GB received by the job manager over the network by velocity for windowing rate--all means for (three, six, ten) workers are significantly different from each other. This is consistent with the results observed in previous experiments and explained by the fact that parallelisation of work is higher in larger clusters, leading to increased communication between containers across the network. While this model is a good fit for distributed systems hosted entirely on-premises, it could result in additional costs when commercial clouds are used.  
Results (in the form of graphs and regression models) for the number of gigabytes received and sent by the job manager during the windowing rate versus resource utilisation experiments by windowing rate are tabulated in Table 12.  
Based on the summary results presented in Table 11 for the total number of GB received and sent by the job manager over the network during the container co-location experiments, the following conclusions were drawn: (i) total number of GB received by the job manager over the network by number of containers per node--all means for (one, two, four) clusters are not significantly different from each other; (ii) total number of GB received by the job manager over the network by number of containers per node--all means for (one, two, four) workers are not significantly different from each other, except for (three workers x six workers). This is an important finding when considered alongside the network utilisation readings for workers. It establishes that, generally, when a cluster increases in size, the effect on manager-to-worker communication is minimal. Worker-to-worker communication, on the other hand, increases significantly since there is greater parallelisation.  
Results (in the form of graphs and regression models) for the number of gigabytes received and sent by the job manager during the container co-location experiments by windowing rate are tabulated in Table 13.