## 5 Proposed Multi-EC Enabled Air-Ground VN Scenarios
### ML for Computation Offloading in Multi-Service Multi-EC Vehicular Environments
#### 5.2.3 Proposed Solutions  
We have considered two different approaches for efficiently manage the computation offloading challenges in a vehicular scenario. V2V Technology for Finding the Proper VN-EC Pair during Offloading  
With recent advances in communication technologies, connected VUs are able to communicate with each other over short distances by using V2V communication links. Such communication capabilities allow them to share important information with each other. Through the V2V communication links, VUs can share their offloading experiences, helping to select proper EC platforms and their nodes for computation offloading. For example, if a particular EC server is compromised in terms of security or has a low resource pool for serving new VUs, new VUs can be warned over V2V communication links for avoiding such EC for offloading data. It is not practical for VUs to communicate with other vehicles, which are far away from them due to the short distance communication capabilities. Furthermore, individual VUs can not always collect information from each other and analyze it in real-time for taking decisions. By taking this consideration into account, here we propose a cluster-based approach, where a cluster head is selected to perform the data collection, data analysis, and sharing of findings with requesting VUs. The selection of a cluster head can be based upon several criteria, such as available resources, its distance from other VUs, etc. For solving the data analysis problem to find and forward the adequate set of EC nodes, various solution techniques can be adapted. However, conventional solution methods such as convex optimization, heuristic, and metaheuristic methods need to be avoided since implementation costs can be huge. An ML-based solution technique, where a pre-trained ML model deployed over a cluster aimed at analyzing the real-time input data from other VUs over a cluster head can be an excellent solution to be considered. Once analyzed, the findings can be either broadcast to individual VUs or can also be provided in terms of request-response basis to avoid additional costs. Figure 9 shows the important steps considered. Multiple Edge Connectivity Mode for VU-EC Server Task Offloading  
By resorting to one of the possible management solutions for managing EC nodes in 5G, we introduce here the possibility of integrating the presence of logical anchor points in our network [58]. Different anchor point-based connectivity strategies can be adapted for computation offloading between VU and EC platforms. Figure 9: Intelligent solution for the offloading problem. A simple distributed anchor point-based strategy allows VUs to select different EC servers from multiple EC platforms available for offloading. In another case, a primary-secondary server approach can be adapted. In this case, VU transfers its data to the selected EC server, which acts as a primary anchor point. After receiving VUs data, it can then further offload to the nearby secondary EC servers for reducing its workload. This strategy can reduce the overall computation time required for processing the overall data. In another case, VU can offload its data to multiple EC platforms simultaneously. Figure 10 shows these three strategies adapted over a proposed Multi-EC enabled VN architecture. VU1 uses a distributed anchor point-based strategy and selects a BS-EC platform for computation offloading. VU2 uses a primary-secondary server-based strategy and offloads data to the BS-EC server. Being a primary anchor point, the BS-EC server selects two UAVs as secondary anchor points for reducing the overall computation time. This approach allows EC servers that are not able to server VUs in the first place mainly because of coverage limitations to act as a secondary anchor point for improving VUs operations. In the third case, VU itself selects multiple anchor points for data offloading. With multiple splits, it offloads a portion of the computation load to the selected anchor points.