# Analysis of Mobile Edge Computing for Vehicular Networks +
## 3 Comparison of Task Delegation Scheme with Experimental Results
### Experimental Results
#### 3.3.1 CAMEVAN Example with the TOPSIS Decision-Making Method  
To demonstrate how decision-making is handled in our architecture, we work through an example using various test cases. Recall that the TOPSIS method requires a decision matrix \(DM\). To create this matrix, we label the columns with our criteria and the rows with our alternatives. Non-normalized attribute values along with the weights assigned to each criteria, are shown in Table 1.  
Figure 5: Intersection geo-location.  
The \"cpu\" criteria represents the real-time processor load at the corresponding alternative and is represented as a percentage of current processor usage. The \"mem\" denotes the amount of free memory available in Gigabytes and delay is an indicator of the round-trip communication delay in milliseconds. In each alternative, the computational resources are kept identical with the exception of the \"cin\" alternative. In the case of the \"cin\" alternative, we were limited in options for available infrastructure. The only notable difference between the \"cin\" node and the others is a reduced amount of memory. The \"cin\" node was limited to 4 GB of memory while others were allocated 15 GB, which was due to physical hardware limitations. At any rate, this difference in memory does not adversely affect any testing, as no test case required over 2 GB of memory. However, it may make this asset less-favorable in the TOPSIS ranking due to lower amount of memory available. Each virtual server is configured with a four-core Intel Xeon processor. Additional testing was conducted with singe and dual core configurations, but the best performance was observed with the four cores. Thus, run-time results presented were obtained with the four-core configuration. Configurations with fewer processing cores showed very similar results in terms of ranking and had only slightly longer run times.  
Before creating a ranking of our alternatives, we must transform the decision matrix into a normalized-weighted decision matrix (NWDM). Recall that the normalized decision matrix \(R\) is found as:  
\[r_{ij}=\frac{x_{ij}}{\sqrt{\sum_{k=1}^{m}x_{kj}^{2}}},i=1,2,...,m,j=1,2,...,n\]  
NWDM was found by multiplying each attribute in \(R\) with it's respective weight. Applying this to the matrix values in Table 1, we obtain the NWDM shown in the first four columns of Table 2. From this matrix we can easily find the PIS \(A_{w}\) and NIS \(A_{b}\) with the formulas previously defined. From the NWDM we can see that:  
\[A_{b}=\{0.0483412,0.132433,0.0\}\]  
\[A_{w}=\{0.23687188,0.02417878,0.23759193\}.\]  
With these values known, we can then calculate the separation distance of each alternative to the best and worst solution. Finally we measured the closeness of each alternative to the ideal solution and rank each them accordingly. The rankings obtained for each alternative are shown in last column of Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **Criteria** & \\ \hline
**Alternatives** & **cpu (\(w=0.35\))** & **mem (\(w=0.25\))** & **delay (\(w=0.4\))** & **Rank** \\ \hline \(A_{central}\) & 0.225822 & 0.132433 & 0.173015 & 5 \\ \(A_{nust}\) & 0.0818347 & 0.130647 & 0.166586 & 2 \\ \(A_{uest}\) & 0.079763 & 0.130746 & 0.237592 & 4 \\ \(A_{cin}\) & 0.0483412 & 0.0241788 & 0.214161 & 3 \\ \(A_{local}