# Analysis of Mobile Edge Computing for Vehicular Networks +
## 2 CAMEVAN: Contextual Architecture for Mobile Edge-Computing in Vehicular Networks
Results from recent queries can be saved and used for subsequent requests with a threshold defining the lifetime of a query result. If a query is not recent enough, a new query is initiated and the results are saved to a resource table. The time required for a new query of all resources is equal to the highest round trip delay observed for all resources, frequent queries may create additional delay. In our experiments the observed round trip delay was on the order of a couple hundred milliseconds for a geographically diverse test-bed of resources. Increasing the lifetime of query table results could reduce latency and network overhead when during task delegation. However, if this threshold is too high the status of available resources may not be accurately reflected and values may become stale. In either case the task manager will delegate a given task to the best available resource based on real-time metrics. In our architecture, V2I infrastructure and remote resource layers are not like those found in existing MEC architectures. Our architecture varies from others by seeking to delegate computational tasks to the most logical resource in a contextual way. The decision-making process is handled by a multiple-criteria decision analysis (MCDA) method, which takes into account both beneficial and non-beneficial criteria. The MCDA method we employ is the technique for order of preference by similarity to ideal solution (TOPSIS) [19], that works by assuming that the chosen alternative should have the shortest geometrical distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS). TOPSIS assumes that each criterion should be maximized or minimized. In our case, we have a mixture of both beneficial and non-beneficial criteria. The alternatives are the different resources available for task delegation. TOPSIS provides a ranking of these alternatives, which ranks our resources from best to worst with respect to the parameters of the individual computational task. For example, a task with a high memory requirement may be delegated to the resource with the most free memory. One the other hand a task with a small memory requirement can be delegated to a resource with available processors but little free memory. The first step of TOPSIS is to create a decision matrix (DM). For our application, the decision criteria were parameters such as computational complexity, memory impact, data size, and delay  
Figure 2: Design of CAMEVAN task manager. which we refer to as \(C_{1},C_{2},\ldots,C_{n}\). Our alternatives use different computational resources which we refer to as \(A_{1},A_{2},\ldots,A_{n}\). The elements of the matrix correspond to the values of criteria \(i\) with respect to alternative \(j\). \[DM= \begin{array}{cccc}C_{1,n}&C_{1,2}&\cdots&C_{1,n}\\ A_{1,1}&\left(\begin{array}{cccc}x_{1,1}&x_{1,2}&\cdots&x_{1,n}\\ x_{2,1}&x_{2,2}&\cdots&x_{2,n}\\ \vdots&\vdots&\ddots&\vdots\\ x_{m,2}&\cdots&\cdots&x_{m,n}\\ \end{array}\right).\end{array} \]  
Next, we create a normalized decision matrix (NDM) \(R\) from the \(DM\), \(x_{ij}(mn)\). \(R=(r_{ij})mn\) where the normalization method is defined as:  
\[r_{ij}=\frac{x_{ij}}{\sqrt{\sum_{k=1}^{m}X_{kj}^{2}}},i=1,2,...,m,j=1,2,...,n.\]  
We then determine the weighted decision matrix \(t\) with:  
\[t_{ij}=r_{ij}\cdot w_{j},i=1,2,\cdots,m,j=1,2,\cdots,n,\]  
where  
\[w_{j}=\frac{W_{j}}{\sum_{k=1}^{n}W_{k}},j=1,2,\cdots,n.\]  
We can then find the PIS and NIS.