# Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review
## 7 Virtual Resource Allocation Algorithms
### Selected Papers  
Infrastructure sharing is made possible with the help of virtualization technologies, and this fact transformed the network architecture itself into a novel stakeholder called the infrastructure provider (InP) [50; 96]. To maximize InP profit in FiWi access networks [96], propose resource allocation in both wireless and optical subnetworks with a wireless channel allocation algorithm based on breadth first search and a DBA algorithm in FiWi for both radio and virtual resources. A revenue based bandwidth resource allocation is provided in [50] to map the idle resources of a virtualized FiWi access network architecture \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Optimization Algorithm** & **Advantages** & **Limitations** & **Performance Metrics** \\ \hline
[91] & achieve social welfare with an approximation algorithm & temporal demand short-term auctioning & exact graph sub-sectioning algorithm is not feasible for large solution space size & welfare, resource utility \\ \hline
[92] & Dijkstra algorithm with a protection scheme & protection scheme guarantees service connection and high resource occupancy under heavy traffic load & scalability of the protection scheme is not considered & transmission success, utility \\ \hline
[50] & traffic prediction with Q-Learning to maximize InP revenue & InP uses global view of practical resources to predict the traffic load & service behavior and dynamic demands are not part of the solution & revenue, VN acceptance ratio \\ \hline
[47] & Vickrey–Clarke-Groves auction solved with matching game & auction provides pay-off gains to operators with distributed slice allocation & the problem has exponential complexity dependent on the number of users and BSs & average sum QoE \\ \hline
[93] & QoS-aware region division algorithm & global monitoring is used to decrease energy consumption for service profit & the algorithm introduces extra VN embedding delay & profit, energy consumption \\ \hline
[94] & revenue-based VNE allocation with two greedy algorithms & increases the acceptance ratio and profit rate by considering the gains of service requests & dynamic service requests of nodes are not part of the VNE algorithm & VN acceptance ratio, profit \\ \hline
[95] & centralized and mobility-aware resource pooling algorithm & resource pooling gain increases with the average speed of users & overhead and delay caused by user location tracking are not considered & resource pooling gain \\ \hline
[96] & breadth first search channel allocation to maximize profit & algorithm provides a higher InP profit for varying average VN arrival rates & exponential complexity, not feasible for large network graphs & profit, VN acceptance ratio \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Analysis of Virtualized Resource Allocation Papers. to the service provider requests. The InP uses Q-learning to predict idle bandwidth resource and the traffic load on each physical link. This algorithm enables InPs to accept more VN requests and obtain higher revenue. Virtualized network resources pave the way for dynamic resource allocation between the InPs and the network operators over auctioning algorithms. An infrastructure sharing scenario is created for mmWave radio networks in [47], in which the operators use a distributed auction mechanism (Vickrey-Clarke-Groves) to allocate mmWave AP resources by obtaining the slices. The results showed that applying these algorithms after the auction provides pay-off gains to the network operators. Another resource allocation scheme based on auctioning is discussed in [91]. In this scheme, operators submit bids to capacitate a C-RAN subnetwork, and the infrastructure owner (auctioneer) aims to maximize the aggregate social welfare, defined as the sum of the aggregate operators' utility and the C-RAN's revenue.