Game-Theoretical Approaches for Service Provisioning in Network Virtualization: Survey, Taxonomies and Open Challenges
Abstract
:1. Introduction
2. Network Virtualization
2.1. NV Typical Architecture
2.2. NV Enabling Technologies
- Software Defined Networking [7]: The traditional hardware-based networks cannot meet the computing and storage needs for the user’s service requests in an unstable and a changing environment. SDN provides a better fit in such situations by including simplified and enhanced network control, flexible and efficient network management, and improved network service performance. Moreover, the SDN separates the control plane from the data plane through a well-defined programming interface, such that the centralized controller can have a complete view of the entire network as shown in Figure 2. Thus, SDN ensures a new virtualization level through open protocols, such as OpenFlow (OF) protocols, and standardized interface between the control plane and the data plane.
- NFV: The NFV paradigm aim is to decouple network functions from dedicated physical equipment by using virtualization technology, and runs the virtual network functions (VNFs) in the general purpose physical or virtual network appliances. To conclude, the main difference between the SDN and NFV architectures is the overall focus network management responsibilities, and the standards controlling the architectural and functional deployment.
- SDN-enabled NFV architecture: Several recent approaches [8,9,10] were aimed at integrating the SDN into NFV architecture to enhance the network performance by combining the benefits of the two paradigms described by Figure 3. This revolutionary architecture concept is dedicated to adapt the increasing on-demanding services for the next-generation mobile networks such as the 5G [11] technologies and other applications, by providing more flexibility and more scalability.
3. An Overview of Game Theory
3.1. Concept and Characteristics
3.2. The Nash Equilibrium
3.3. Mixed Strategies
3.4. Pareto Efficiency
4. Taxonomy: Classification of GT Approaches in Network Virtualization
4.1. Game Theory Characteristics
4.1.1. Player’s Behavior
- Non-cooperative games: Each player takes the best strategy to maximize their winning comparing with the strategies of other players. Each player takes their decision and reason selfishly and individually without cooperating with other agents. In [40], a non-cooperative congestion game mechanism has been proposed to solve the service chain composition problem in NFV. Thus, the service chain configuration is converted to a mathematical game model for the seeking of Nash equilibrium.
- Cooperative games: The players cooperate by exchanging information to maximize the collective gain that does not necessarily favor a player’s victory at the expense of others. We can also talk about groups of players who can form coalitions and groups and choose a panel of strategies to increase the typical gain. Jose et al. [41] investigated a new security mechanism based on a cooperative game for resilient cyber-physical systems. Several cooperative and virtualized agents decide on a set of orchestrated management decisions to enable resilient system operation in the presence of a malicious agent or severe security threat.
4.1.2. Decision Nature
- Simultaneous games: Each player chooses at the beginning of the game his strategy, once and for all, without having information about what the other players will play. Otherwise, each participant makes the decision at the same time without exchanging information with other agents. In [42], a new simultaneous game model was proposed to optimize the network slicing placement problem for many NFV network slice use cases.
- Sequential games: The game’s exact sequence is specified from the beginning of the game. Each agent considers the set of actions available when it is its turn to play. According to the strategy taken previously by the opponent, the player takes the best strategy to win the game. Typically, a simultaneous game is represented in normal form as in the previous example, while a sequential game is naturally represented in the form of a tree. Laszlo et al. [43] proposed a new sequential pricing game of NFV infrastructure providers. The interaction between the infrastructure providers and the customers was formulated as a trade-off problem to be optimized in terms of price and resource allocation.
4.1.3. Information Awareness
- Complete information games: In the simplest case where players have the full information about the game (such as check games and ladies game), they know the entire game until the current stage. It is a sequential game where the players have an overview of the opponent’s game and possible strategies—for example, chess games. Therefore, the player’s current action influences the next action of the other adversaries. The authors, in [44], proposed a new complete information game-theoretical model in order to ensure the relatively resources allocation among tenants in 5G systems. To this end, a bankruptcy game based on new allocation rules was formulated to meet tenants’ needs by respecting the resource’s fair distribution.
- Incomplete information games: When the player has incomplete and limited knowledge about the opponents’ game and strategies, we talk about the game with incomplete information (for example, dominoes and poker). It is a game where a part of the game is hidden, and the player has only a portion of the information about other players. Decision-making is more critical and crucial than a perfect information game. In [45], a novel incomplete information game-based model was proposed for the cloud resource allocation. An auction game model was formulated to optimize the resource allocation between multiple infrastructure providers and the service providers. A Bayesian Nash equilibrium solution is reached to improve the network performance.
4.1.4. Game Continuity
- Finite games: This game is characterized by consistent and well-known boundaries and rules. During the game, the players focus on maximizing their utility functions because the purpose of the game is winning. Shu-Ting et al. [46] introduced a new finite game-based model to provide an optimized and efficient solution to the service chain composition problem in the network function virtualization paradigm. The service chain composition is converted to the seeking of the Nash equilibrium under the formulated game model.
- Infinite games: The game is repeated where the boundaries and the rules are dynamic and infinite. Thus, the players do not have any knowledge about the beginning or end of the game. The player’s purpose is to continue playing and to sometimes bring more players into the game. However, the infinite games are the less investigated concept in the NFV paradigm compared with the finite game modeling. No research paper solved the optimization problems in the NFV through the infinite game.
4.2. Game Goals
- 1
- Maximize bandwidth allocation: The importance of this objective becomes apparent with data-intensive applications. Real-time video services systems are the best examples [47].
- 2
- Maximizing power allocation: Applications that consider this objective are those with very limited energy capacity. The sensor network represents a good use case [48].
- 3
- Fairly allocate the physical resource: One of the most addressed goals for cellular applications such as LTE networks [49] is to efficiently manage the physical resources by sharing them fairly among mobile devices and user applications. Network slicing is a good example.
- 4
- Ensure load balancing: Nowadays, load balancing is becoming a necessity for many emerging applications that are very resource-intensive and require a balanced allocation in cloud computing environments [50].
- 5
- Reduce congestion: the object is to ensure secure and reliable communications with the congestion problem. The need for congestion control depends on the type of applications. For example, in the vehicular ad hoc network, this issue is crucial because a congested network can have a negative impact on the reaction time of the safety equipment and thus make driving less safe [51].
- 6
- Improving Fault Tolerance: Fault tolerance is an indispensable goal for many applications such as wireless sensor networks (WSNs). WSNs are characterized by limited sensor hardware resources, resulting in power depletion, non-rechargeable batteries, and physical failures. Improving fault tolerance [52,53] by maintaining network connectivity and coverage in the presence of faulty sensors is a challenge for WSNs.
- 7
- Security: Nowadays, and with the increasing number of attackers and cybercrimes, security is increasingly becoming a key challenge for any applications or environments where new threats and vulnerabilities are raided to target network features [54].
4.2.1. Goal 1: Maximize the Bandwidth Allocation
- Resource negotiation game: During the game, the SP starts by reclaiming the resource from the InP; then, it chooses to lease the resource from InP or to leave and to look for another one depending on the required QoS. Thus, the InP can admit or reject the request based on the resource sufficiency. The SP has a contract with an InP, so any direction to another InP can apply a penalty cost. When the two players are satisfied, the resource is allocated to the SP. However, if both players are dissatisfied, SP can lease the resource from another InP without any charged monetary penalty. Then, in case of only InP being satisfied, the SP must pay a penalty. The last case occurs when the SP is the only satisfied player; consequently, it is redirected to another InP without any charge. The players aim at maximizing their gains by reaching the Nash equilibrium.
- Node and link allocation games: two game models are proposed to fairly allocate the node and link resources for the SPs. The authors consider the SPs as a competitive player with selfish behaviors which aim at maximizing their utility function by consuming the maximum of the resources. The optimal solution for all players is achieved by the Nash equilibrium for each game.
4.2.2. Goal 2: Maximize the Power Allocation
4.2.3. Goal 3: Fairly Allocate the Physical Resource
4.2.4. Goal 4: Ensure the Load Balancing
4.2.5. Goal 5: To Reduce the Congestion
4.2.6. Goal 6: To Enhance the Fault Tolerance
4.2.7. Goal 7: To Ensure Security
4.3. Players during the Game
- The real players are nominated to present physical entities such as the service provider and the cloud provider. The game schemes based on those players try fundamentally to allocate enough resources to the lessees and to maximize the revenues of the lessors.
- The virtual players are designed to model virtual entities such as the virtual network, the task, and virtual machines. Thus, the interaction between the virtual players can address the virtual resource allocation by proactively preventing the network congestion and to prevent the future allocation to support the sharing of the resources between multi-tenants.
4.3.1. Virtual Network (VN)
4.3.2. Service/Cloud Provider
4.3.3. Virtual Operator
4.3.4. Virtual Machine
4.3.5. Task
5. Discussion and Future Research Directions
5.1. Cooperative Environment
5.2. Security and Resiliency
5.3. Traffic Management
5.4. Pricing
5.5. Resource and Service Management
5.6. Scalability
5.7. Decentralized Networks
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Approaches | Game’s Behaviour | Decision Nature | Information Awareness | Game Continuity | |||||
---|---|---|---|---|---|---|---|---|---|
Cooperative | Non-Cooperative | Sequential | Simultaneous | Complete Information | Incomplete Information | Finite | Repeated | ||
Wang et al. [17] | ✓ | ✓ | ✓ | ✓ | |||||
Zhou et al. [18] | ✓ | ✓ | ✓ | ✓ | |||||
Seddiki et al. [19] | ✓ | ✓ | ✓ | ✓ | |||||
Wang et al. [20] | ✓ | ✓ | ✓ | ✓ | |||||
Fangwen et al. [21] | ✓ | ✓ | ✓ | ✓ | |||||
Guo et al. [22] | ✓ | ✓ | ✓ | ✓ | |||||
Wei et al. [23] | ✓ | ✓ | ✓ | ✓ | |||||
Fan et al. [24] | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Yuan et al. [25] | ✓ | ✓ | ✓ | ✓ | |||||
Teng et al. [26] | ✓ | ✓ | ✓ | ✓ | |||||
Iyer et al. [27] | ✓ | ✓ | ✓ | ✓ | |||||
Wei et al. [28] | ✓ | ✓ | ✓ | ✓ | |||||
Liu et al. [29] | ✓ | ✓ | ✓ | ✓ | |||||
Subrata et al. [30] | ✓ | ✓ | ✓ | ✓ | |||||
Khan et al. [31] | ✓ | ✓ | ✓ | ✓ | |||||
Ardagna et al. [32] | ✓ | ✓ | ✓ | ✓ | |||||
Niyato et al. [33] | ✓ | ✓ | ✓ | ✓ | |||||
Ye et al. [34] | ✓ | ✓ | ✓ | ✓ | |||||
Elias et al. [35] | ✓ | ✓ | ✓ | ✓ | |||||
Pham et al. [36] | ✓ | ✓ | ✓ | ✓ | |||||
Wei et al. [37] | ✓ | ✓ | ✓ | ✓ | |||||
Soualah et al. [38] | ✓ | ✓ | ✓ | ✓ | |||||
Chowdhary et al. [39] | ✓ | ✓ | ✓ | ✓ |
P2 Cooperates | P2 Defects | |
---|---|---|
P1 cooperates | , | , |
P1 defects | , | , |
(a) GT solutions comparison in terms of players | |
Players | Approaches |
Virtual Networks | Wang et al. [17] |
Wang et al. [20] | |
Yuan et al. [25] | |
Service Providers | Zhou et al. [18] |
Seddiki et al. [19] | |
Fangwen et al. [21] | |
Wei et al. [28] | |
Khan et al. [31] | |
Virtual operators | Wei et al. [23] |
Fan et al. [24] | |
Teng et al. [26] | |
Liu et al. [29] | |
Elias et al. [35] | |
Virtual machine | Guo et al. [22] |
Ye et al. [34] | |
Task | Guo et al. [22] |
Subrata et al. [30] | |
Wei et al. [28] | |
Iyer et al. [27] | |
Others | Ardagna et al. [32] |
Niyato et al. [33] | |
Pham et al. [36] | |
Soualah et al. [38] | |
Chowdhary et al. [39] | |
Salvatore D’Oro et al. [57] | |
(b) GT solutions comparison in terms of goals | |
Goals | Approaches |
Maximize the bandwidth allocation | Wang et al. [17] |
Zhou et al. [18] | |
Seddiki et al. [19] | |
Yuan et al. [25] | |
Cui-rong Wang et al. [20] | |
Maximize the power allocation | Fangwen et al. [21] |
Wei et al. [23] | |
Fan et al. [24] | |
Fairly allocate the resources | Teng et al. [26] |
Iyer et al. [27] | |
Guo et al. [22] | |
Wei et al. [28] | |
Ardagna et al. [32] | |
Niyato et al. [33] | |
Liu et al. [29] | |
Wei et al. [37] | |
Ensure the load balancing | Subrata et al. [30] |
Khan et al. [31] | |
Ye et al. [34] | |
Reduce the congestion | Elias et al. [35] |
Salvatore D’Oro et al. [57] | |
Pham et al. [36] | |
Soualah et al. [38] | |
Enhance the fault tolerance | Ardagna et al. [32] |
Ensure security | Chowdhary et al. [39] |
Benefits | Limitations |
---|---|
Low complexity | Abrupt failure |
Efficient resource management | Security issues |
Economical | Bottlenecks problem and latency |
Easy control and Quick updates | Single point of failure |
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Bennaceur, J.; Ahmadi, H.; Souhi, S. Game-Theoretical Approaches for Service Provisioning in Network Virtualization: Survey, Taxonomies and Open Challenges. Telecom 2021, 2, 232-254. https://doi.org/10.3390/telecom2030016
Bennaceur J, Ahmadi H, Souhi S. Game-Theoretical Approaches for Service Provisioning in Network Virtualization: Survey, Taxonomies and Open Challenges. Telecom. 2021; 2(3):232-254. https://doi.org/10.3390/telecom2030016
Chicago/Turabian StyleBennaceur, Jihen, Hanen Ahmadi, and Sami Souhi. 2021. "Game-Theoretical Approaches for Service Provisioning in Network Virtualization: Survey, Taxonomies and Open Challenges" Telecom 2, no. 3: 232-254. https://doi.org/10.3390/telecom2030016