# Quantum Game Application to Recovery Problem in Mobile Database
## 3 Quantum Game-Based Recovery Model
### Recovery Modeling Using Quantum Game
#### 3.2.1 Recovery Algorithms and Its Parameters  
To demonstrate the technical importance of the system recovery model in the MDS, we evaluated the most widely used MD recovery algorithms in order to decide which methods should be explored further. We classified the recoverable algorithms in this situation according to their operation or features. As stated in [9; 10; 11; 12; 13], various groups differ in their approach to recovery. For our proposal, we selected three recovery protocols: log monitoring (as player 1) [27], mobile agent (as player 2) [24], and a hybrid method that combined movement-based check-pointing and message recording as a (as player 3) [22]. Because the real problem is not whether to adopt one of the well-known recovery techniques, but rather which strategy is most appropriate in light of the changes imposed by the operational environment, which is often unclear and changing. In this regard, the current research will ensure that the optimal recovery method is selected via the use of game theory and its essential variables. Figure 4: Game Theory Interrelationship Diagram. To compete against other players, each player must create a set of strategies. To develop these methods, feature analysis and extraction are used for each chosen recovery procedure in order to determine the treatment's most strongest characteristics. Thus, in game theory, each selected protocol is defined by a player, and the way each protocol's variable is utilized determines the player's strategies. For instance, the first protocol (player 1) considered a variety of factors, including the log arrival rate, the handoff rate, the average log size, and the mobility rate. To summarize, the first player method involves retrieving the log file that was stored in the BS before to the failure, moving it to a new location linked to another BS, and updating the MH. The second protocol (player 2) makes extensive use of variables, including the number of processes in the checkpoint, a handoff threshold, and the length of the log. The second approach reduced recovery time by using a framework based on mobile agents. Here is a collection of processes in a list format. The home agent's list is included in the MH. The mobile agent traveled beside the MH and relayed information. The third process takes into account a variety of factors, including the total number of registrations in the area, the total number of regions, and the overall number of hand-offs. The work environment is split into several zones, and the checkpoint is only used once when MH enters and exits the region. For further information on how these protocols work, see [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27]. It should be emphasized that any number of strategies for each player (protocol) may be produced by conducting any number of trials with various parameter values, although this increases the model's complexity. As a result, we believe it is important to choose several methods that reflect varying degrees of performance that may be depended upon in decision-making. To create the parameters for the necessary recovery algorithms using game theory as a decision-making method, we first apply the chosen protocols to the chosen key variables on which each protocol is reliant. Each algorithm is used for real database transactions in order to assess each player's strategy. A package is assessed that has an objective function for the total cost of recovery, which is computed differently for each method. In game theory, we build the payoff matrix for each protocol output value based on the previous stages. These outcomes are referred to as the utility or reward of each player. These payoffs or benefits are used to evaluate a player's level of satisfaction in a conflicting scenario.