Contact between mobile hosts and database servers presents many problems in the Mobile Database System (MDS). It is harmed by a variety of causes, including handoff, inadequate capacity, frequent transaction updates, and repeated failures, both of which contribute to serious issues with the information system’s consistency. However, error tolerance technicality allows devices to continue performing their functions in the event of a failure. The aim of this paper is to identify the optimal recovery approach from among the available state-of-the-art techniques in MDS by employing game theory. Several of the presented recovery protocols are chosen and evaluated in order to determine the most critical factors affecting the recovery mechanism, such as the number of processes, the time required to deliver messages, and the number of messages logged-in time. Then, using the suggested payout matrix, the game theory strategy is adapted to choose the optimum recovery technique for the specified environmental variables. The NS2 simulator was used to carry out the tests and apply the chosen recovery protocols. The experiments validate the proposed model’s usefulness in comparison to other methods.
Through the advancement in networking capabilities, mobile communication has been one of the most vital aspects of our lives. Mobile Computing (MC) refers to a variety of nodes or devices that allow citizens to access information or data regardless of their location [
Due to the fact that the majority of nodes are mobile, the MD recovery mechanism becomes extremely difficult. Despite this, numerous parameter requirements are required for the recovery module in order to achieve a dynamic recovery protocol that adapts to changing environmental conditions [
The majority of current database recovery procedures make a little exception for the conditions surrounding the disconnection in a mobile world. Thus, the objective is to create a novel intelligent strategy for mobile database recovery that takes into account a variety of environmental variables through the use of game theory. Game theory is used here since no one optimum approach exists for all recovery scenarios using available recovery protocols. Game theory is an appropriate method for determining the optimal strategy (best solution) for each player based on their utility function (alternative recovery approaches are represented as players) [
Mobile systems are often exposed to a variety of external conditions, which can result in data loss or contact failure. As a result, traditional recovery methods cannot be strictly applied to these systems. The following are the primary problems confronting conventional mechanisms: (1) Some recovery protocols rely on unstable and limited MH storage; (2) certain protocols affect the logging scheme, which can result in machine load overhead; (3) certain schemes necessitate a large amount of data transfer; (4) certain protocols may be slow during the recovery process, depending on the distance between the MH and the Base Station (BS); and (5) some of the algorithms failure in terms of the number of repeated processes and exchanged messages. In general, considering the attempts made to resolve mobile database recovery, there is still room for substantial improvement.
The novelty of the proposed model is that it enables efficient recovery treatment in MDS by using a novel smart strategy focused on players (different recovery protocols) within the game theory paradigm as a decision-maker for selecting the most efficient recovery procedure. Since the main issue is not selecting one of the well-known recovery methods, but rather selecting the most suitable approach in light of the adjustments introduced by the operational environment, which is often ambiguous and unpredictable? In this regard, the current work would ensure that the best possible recovery approach through game theory model is chosen based on its critical parameters. The proposed study compares a variety of various recovery strategies. These techniques demonstrate the impact of various parameters on the complex efficiency of the protocol under consideration. The work shows that using ideal parameters greatly enhanced efficiency, demonstrating the proposed method’s enormous potential. Additionally, the proposed model exhibits a strong degree of versatility by incorporating cutting-edge regeneration techniques that can significantly increase the performance.
This paper is a substantial extension of our conference paper [
Apart from this introduction, the following sections are planned: Section 2 discusses current state-of-the-art MD recovery strategies, Section 3 outlines the proposed mobile database recovery model, Section 4 describes the standards for evaluating the proposed game theory strategy of MD recovery and presents the results, and Section 5 concludes and suggests potential work directions.
While recovery in the MDS field of research is not recent, there are still numerous opportunities for improving established protocols and developing new ones [
Authors in [
In [
In [
Checkpointing is a time-consuming mechanism since it requires all systems to take a checkpoint in order to maintain global continuity. This will sometimes result in needless checkpointing and the exchanging of several messages. A decrease in the period required for log unification or checkpointing results in a decrease in recovery time. It is possible to create a list of dependent processes that need checkpointing and insert it in a mobile agent that travels with the mobile host. The wok resented in [
The authors of [
The authors in [
In their work, the message digest is a hash function that is used to compare hash values in order to identify rows that need synchronization. If the values are identical, no synchronization (recovery) is required; if they are not identical, an error occurs, and synchronization is required and proceeds according to the algorithm’s rules. The drawback of this algorithm is that it does not make use of database objects such as triggers, recorded procedures, or timestamps, which means that there is no additional expense associated with the retrieval mechanism on the database side. Additionally, the SAMD algorithm is very stable due to its use of a message digest and is simple to enforce on both the server-side and mobile database. One disadvantage of this algorithm is that, although it makes use of hash functions, it does not ensure data integrity during transmission to the server, since the hash values are stored in a database table on both ends.
According to the review given, the majority of the works presented were classified as follows: (1) The majority of recovery studies used a variety of techniques in the recovery process, including log management, checkpointing, movement-based checkpointing, and an agent-based logging scheme; (2) Since these methods are so dissimilar, one of them does not work as a substitute for another; this means that each algorithm has a unique parameter set and different assumptions. (3) Although certain plans attempted to combine several techniques into a single contribution (hybrid method), they were still harmed by the difficulty of choosing the right fusion from this pool of approaches. As a result, recovery costs may be large, and the recovery mechanism may be overly complex; (4) Finally, most schemes did not regard environmental variables as influencing factors in the recovery process. As a result of the above, the realistic application of recovery algorithms is constrained. It is necessary to create a scheme that optimizes success by selecting the most appropriate recovery strategies for the current scenario. Thus, game theory was used due to its importance for decision making, as it utilizes conflict analysis or interactive decision theory to choose the optimal solution through competition between the strategies presented for each recovery protocol.
A traditional MDS architecture contains a tiny database fragment originating from the main database that resides on the MH. This architecture is intended to manage the accessibility constraints that MHs and Mobile Satellite Services (MSS) can alleviate. If the MH is present in the cell served by the MSS, a nearby MH may interact directly with it. The MH will freely switch between cells, and each cell contains a BS and many MHs. The BSs also configured stations to provide a wireless gateway that allows them to connect with the MHs and transmit data through the wireless network. Each MH can communicate with the BS
To illustrate the technical significance of the system recovery model in the MDS, we analyzed the most important developed algorithms for MD recovery in order to determine which of these algorithms should be investigated. In this scenario, we grouped the usable recovery algorithms according to their operation or characteristics. As mentioned in [
Each player must develop a set of strategies in order to compete against other players. To obtain these strategies, feature analysis and extraction are performed on each selected recovery protocol to ascertain the most powerful features of each protocol. Thus, in the game theory, each chosen protocol is described by a player, and each player’s strategies are determined by the way each protocol’s variable is used. For example, the first protocol (player 1) took into account many variables, including log arrival rate, handoff rate, average log size, and mobility rate. Similarly, the second protocol (player 2) made use of many variables, including the number of processes in the checkpoint, a handoff threshold, and the log duration. The third procedure makes use of many parameters, including the total number of registrations in the region, the total number of regions, and the total number of hand-offs. For further detail about how these protocols operate, see [
To prepare the parameters for the requisite recovery algorithms using game theory as a decision-making strategy, we first apply the selected protocols using the selected critical factors on which each protocol is dependent. Each algorithm is applied using actual database transactions to evaluate the strategies of each player. A package with an objective function for overall recovery cost is measured, which is calculated differently for each algorithm. Based on the preceding steps, we construct the payoff matrix for each protocol output value in game theory. These outcomes are referred to as each player’s utility or payoff. These payoffs or benefits estimate the satisfaction degree of a player extracted from the conflicting situation. Generally, the game theory could be described as follows: (1) a set of players (the selected algorithms for negotiation); (2) a pool of strategies for each player (the strategies reflect the assumed values of significant coefficients in each protocol that also reflect the possible environmental changes); (3) the benefits or payoffs (utility) to any player for every possible list of the strategy chosen by the players.
Within the game of the suggested model, a finite set of players
Symbol | Meaning |
---|---|
The game model | |
Number of players | |
Game player | |
Player’s strategy | |
Payoff function or utility | |
Best response for a player |
We construct one matrix for each of player 3’s actions (strategies); thus player 1 chooses a row, player 2 chooses a column, and player 3 chooses a table. The bi-matrix for three players’ game with its payoff is illustrated in
for every list of
Estimating the amount of payoff in the game depends on other players’ decisions by their choices. Therefore, choosing a strategy for a player impacts the gain value of the other player. In this proposal, there are three utility functions that are used as benchmarks for performance and evaluation of the candidate protocols. The functions are; the amount of time used during the operation of the protocol (
where
Finally, with regard to estimating the recovery process’s completion level
Thus, the player’s total gain in this game is the sum of the reward values of the variables (
The simulation is used to evaluate the proposed game theory-based recovery model in MD. In this regard, we used two software to implement the prototype NS2 software and Matlab. The NS2 simulation software is a simulator for a discrete event intent at networking research that helps developers to improve their business in real-time. It also supports many protocols like TCP, routing, and multicast protocols over wired and wireless networks, and works on several platforms such as Linux, and Windows [
From the literature, based on the evaluation of selected recovery protocols, the most appropriate values for the set of protocol’ factors (strategies) were chosen to reflect the performance of the protocol in different environments. The first set of experiments was conducted to assess the proposed recovery model performance in terms of real execution time as a function of log file size. In general, the recovery cost for any scheme is very low as the entire log information is present at the current base station. When the MH travels far away from the first BS the degree of recovery algorithm’s complexity increases to find and transfer the log file. Increasing the file size leads to an increase in the cost of the transfer. As revealed in
Variable | Meaning |
---|---|
Channel type | Channel/wireless channel |
MAC type | Mac/802_11 |
Radio-propagation model | Propagation/two ray ground |
Network interface type | Phy/wireless Phy |
Interface queue type | Queue/drop tail/priqueue |
Antenna model | Antenna/omni antenna |
Link layer type | LL |
Routing protocol | Destination-sequenced distance vector (DSDV) |
Coordinate of topology | 670 |
Max packet in |
500 |
Time to stop simulation | 250 |
The proposed recovery model depends in its work on building a knowledge base, which is only built once and through which the most appropriate protocol is chosen based on the payoff matrix and the dominant equilibrium technique. Herein, the selection is made based on the integration of the three utility functions that are used as benchmarks for performance and evaluation of the candidate protocols.
For a recovery process’s completion, there is a positive relationship between the increase in the handoff rate and the possibility to complete the recovery. Handoff is the process of transferring a mobile station (MS) from one base station (BS) or channel to another. A handoff algorithm with fixed parameters cannot perform well in different system environments. Specific characteristics of the communication systems should be taken into account while designing handoff algorithms. When a mobile node moves from one network to another, if the preparation time of fast handoff is larger than WLAN sojourn time related to mobile node speed, the handoff failed and occurred the packet loss. If the mobile node speed is too slow in the case of the fixed threshold value, instead, handoffs are triggered too late and thus WLAN service time is reduced. The handoff cost includes the cost of transferring the checkpoint state, message log, and an acknowledgement.
In the same context,
This article aims to suggest a novel game theory model for determining the optimal recovery solution in MDS. The new algorithm was demonstrated in a competitive setting against two of the most widely used MDS recovery protocols. The concept behind game theory is that each algorithm selects the most suitable approach in terms of message delivery time and message count to detect the correct recovery solution based on environmental variables. A critical phase in a game-theoretic study is determining which approach is the better solution of a recovery procedure to the strategies selected by others. The suggested recovery model is focused on the creation of a knowledge base that is created once and used to choose the most suitable procedure based on the payoff matrix and dominant equilibrium technique. The experimental results support the proposed recovery model’s superiority. In the future, it could be necessary to allow for additional recovery protocols to maximize the performance of the proposed model. Additionally, a hybrid approach focused on game theory and the proposed paradigm of cloud algorithms was used to achieve a better outcome.