 Research
 Open Access
Dynamic CRAN resource sharing scheme based on a hierarchical game approach
 Sungwook Kim^{1}Email author
https://doi.org/10.1186/s136380150507y
© Kim. 2015
 Received: 16 July 2015
 Accepted: 20 December 2015
 Published: 4 January 2016
Abstract
Over the past decade, wireless applications have experienced tremendous growth, and this growth is likely to multiply in the near future. To cope with expected drastic data traffic growth, a new cloud computingbased radio access network (CRAN) has been proposed for nextgeneration cellular networks. It is considered as a costefficient way of meeting high resource demand of future wireless access networks. In this paper, we propose a novel resource sharing scheme for future CRAN systems. Based on the Indian buffet game, we formulate the CRAN resource allocation problem as a twolevel game model and find an effective solution according to the coopetition approach. Our proposed scheme constantly monitors the current CRAN system conditions and adaptively responds in a distributed manner. The experimental results validate the effectiveness and efficiency of our proposed scheme under dynamic CRAN situations.
Keywords
 Cloud radio access network
 Indian buffet game
 Resource allocation
 Twolevel game model
 Asymptotic shapley value
 Coopetition approach
1 Introduction
In recent years, the radio access network (RAN) is commonly used to support the exponential growth of mobile communications. Conceptually, RAN resides among network devices such as a mobile phone, a computer, or any remotely controlled machine and provides connections with core networks. However, traditional RAN architecture has been faced with a number of challenges. First, a highly loaded base station (BS) cannot share processing power with other idle or less loaded BSs; it results in a poor resource utilization. Second, a BS equipment serves only radio frequency channels in each physical cell, where BS’s resources cannot be shared with other BSs in different cells. Finally, BSs built on proprietary hardware cannot have a flexibility to upgrade radio networks [1, 2].
To overcome these problems, cloud computingbased radio access network (CRAN) is widely considered as a promising paradigm, which can bridge the gap between the wireless communication demands of end users and the capacity of radio access networks. To meet users’ high resource demands, the CRAN consolidates BSs to a central cloud and takes a benefit from the cloud computing elasticity, which allows dynamic provisioning of cloud BS resources [1, 3]. In CRAN, the baseband processing unit (BPU) of traditional BSs is pooled and moved into a centralized location. By virtualization, the computing resources in the BPU pool can be dynamically shared among all the cells in the network while allowing a significant improvement in computing resource utilization and power efficiency. Currently, there have been a number of researches on the computing resource allocation for the virtualized BPUs. However, they are not realizable in a practical system because of the computational complexity [4].
Game theory is the formal study of conflict and cooperation and can be used to model a multiplayer decisionmaking process and to analyze the manner in which players interact with each other during this process. Therefore, the concepts of game theory provide a language in which to formulate, structure, analyze, and understand strategic scenarios. This concept drew great attentions in both areas of economics and computer science [5]. In 2013, C. Jiang introduced the fundamental notion of an Indian buffet game to study how game players make multiple concurrent selections under uncertain system states [6]. Specifically, the Indian buffet game model can reveal how players learn the uncertainty through social learning and make optimal decisions to maximize their own expected utilities by considering negative network externality [7]. This game model is well suited for the CRAN resource sharing problem.
Motivated by the above discussion, we design a new CRAN resource sharing scheme based on the Indian game model. The key feature of our scheme is to develop a decentralized mechanism according to the twolevel coopetition approach. The term “coopetition” is a neologism coined to describe cooperative competition. Therefore, coopetition is defined as the phenomenon that differs from competition or cooperation and stresses two faces, i.e., cooperation and competition, of one relationship in the same situation [8]. In this study, our proposed game model consists of two levels: the upper and lowerlevel Indian buffet games. At the upperlevel game, cloud resources are shared in a cooperative manner. At the lowerlevel game, allocated resources are distributed in a noncooperative manner. Based on the hierarchical interconnection of the two game models, control decisions can cause cascade interactions to reach a mutually satisfactory solution.
Usually, different CRAN agents may pursue different interests and act individually to maximize their own profits. This selforganizing feature can add autonomics into CRAN systems and help to ease the heavy burden of complex centralized control algorithms. Based on the recursive bestresponse algorithm, we draw on the concept of a learning perspective and investigate some of the reasons and probable lines for justifying each system agent’s behavior. The dynamics of the interactive feedback learning mechanism can allow control decisions to be dynamically adjustable. In addition, by employing the coopetition approach, control decisions are mutually dependent on each other to resolve conflicting performance criteria.
1.1 Related work
Over the years, a lot of stateoftheart research work on the CRAN resource sharing problem has been conducted [4, 9, 10]. The baseband processing units virtualization (BPUV) scheme [4] was proposed for the baseband processing unit virtualization. It was formulated as a bin packing problem, where each baseband processing unit was treated as a bin with finite computing resources, expressed in million operations per timeslot (MOPTS). In addition, the dynamics of the cell traffic load was treated as an item that needed to be packed into the bins with the size equal to the computing resources in MOPTS, required to support the traffic load. To solve the optimization problem and simultaneously improve the standard solver for the bin packing problem, the BPUV scheme was designed based on a heuristic simulated annealing approach [4].
The joint cloud computing and network (JCCN) scheme [9] was proposed to jointly study dynamic cloud and wireless network operations so as to improve endtoend performance in the mobile cloud computing environment. This scheme considered not only the spectrum efficiency in wireless networks but also the pricing information in the cloud, based on which power allocation and interference management in wireless networks were performed. The JCCN scheme formulated the problems of cloud media service price decision, resource allocation, and the interference management in the mobile cloud computing environment as a threelevel Stackelberg game [9].
The cloud provider’s resource sharing (CPRS) scheme [10] was developed to study the cooperative behavior of multiple cloud providers. In the CPRS scheme, a hierarchical cooperative game model was designed; it was composed of two interrelated cooperative games to analyze the decisions of cloud providers to support internal users and to offer service to public cloud users. In the lowerlevel, the CPRS scheme implemented a stochastic linear programming game model to study the resource and revenue sharing for a given coalition of cloud providers. In the upperlevel, the CPRS scheme formulated the coalitional game for which the cloud providers can form the groups of cooperation to share resource and revenue. Finally, the analytical model based on a Markov chain was used to obtain stable coalitional structure [10]. All the earlier work has attracted a lot of attention and introduced unique challenges to efficiently solve the resource sharing problem in CRAN systems. Compared to these schemes [4, 9] and [10], our proposed scheme attains better system performance.
The remainder of this paper is structured as follows. In Section 2, we outline the CRAN architecture in detail. Section 3 describes the Indian buffet game model for CRAN systems. And then, the proposed algorithm is explained step by step in Section 4. In Section 5, we show the simulation results. Through simulation, we show the ability of the proposed scheme to achieve high accuracy and promptness in dynamic CRAN environments. Finally, we draw conclusions in Section 6.
2 Cloud radio access network architecture
The CRAN is a novel mobile network architecture, which has a potential to optimize cost and energy consumption in the field of mobile networks. In CRAN systems, there are multiple cloud providers (CPs), which can generate more revenue from the sharing of available resources. CPs have their system resources, such as a CPU core, memory, storage, and network bandwidth. To ensure the optimal usage of cloud resources, baseband processing is centralized in a virtualized baseband units pool (VBP). The VBP can be shared by different CPs and multiple BSs. Therefore, the VBP is in a unique position as a cloud brokering between the BSs and the CPs for cloud services while increasing resource efficiency and system throughput [11].
In this study, we consider a CRAN architecture with one VBP, 10 SBSs, and 100 MUs, and system resources are the computing capacities of CPU, memory, storage, and bandwidth. These resources can be used by the MUs through the VBP to gain more revenue. For the rest of this paper, we refer the organization that CPs cooperate to form a logical pool of computing resources to support MUs’ applications. Each MU application service has its own application type and requires different resource requirements.
3 Indian buffet game model for CRAN systems
Let us consider an Indian buffet restaurant which provides m dishes denoted by d _{1}, d _{2},…, d _{ m }. Each dish can be shared among multiple guests. Each guest can select sequentially multiple dishes to get different meals. The utility of each dish can be interpreted as the deliciousness and quantity. All guests are rational in the sense that they will select dishes which can maximize their own satisfactions. In such a case, the multiple dishselection problem can be formulated to be a noncooperative game, called the Indian buffet game. In the traditional Indian buffet game, the main goal is to study how guests in a buffet restaurant learn the uncertain dishes’ states and make multiple concurrent decisions by not only considering the current utility but also taking into account the influence of subsequent players’ decisions [5, 7].
During the CRAN system operations, system agents should make decisions individually. In this situation, a main issue for each agent is how to perform well by considering the mutual interaction relationship and dynamically adjust their decisions to maximize their own profits. In this study, we develop a new CRAN system resource sharing scheme based on the Indian buffet game model. In our proposed scheme, the dynamic operation of VBP, SBSs, and MUs is formulated as a twolevel Indian buffet game. At the first stage, the VBP and SBSs play the upperlevel Indian buffet game; the VBP distribute the available resources to each SBS by using a cooperative manner. At the second stage, multiple MUs decide to purchase the resource from their corresponding SBS by employing a noncooperative manner. Based on this hierarchical coopetition approach, we assume that all game players (VBP, SBSs, and MUs) are rational and independent of gaining the profit as much as possible. Therefore, for the implementation practicality, our proposed scheme is designed in an entirely distributed and selforganizing interactive fashion.

ℕ is the finite set of players \( \mathrm{\mathbb{N}}=\left\{\mathbf{\mathcal{C}},\mathbf{\mathcal{B}}\right\} \) where \( \mathbf{\mathcal{C}} \) = {VBP} represents one VBP and ℬ = {b _{1}, …, b _{ n }} is a set of multiple SBSs, which are assumed as guests in the upperlevel Indian restaurant.

\( \mathbb{D} \) is the finite set of resources \( \mathbb{D}=\Big\{{d}_1 \), d _{2},…, d _{ l }} in the VBP. Elements in \( \mathbb{D} \) metaphorically represent different dishes on the buffet table in the upperlevel Indian restaurant.

S _{ i } is the set of strategies with the player i. If the player i is the VBP, i.e., \( i\in \mathbf{\mathcal{C}} \), a strategy set can be defined as S _{ i } = {\( {\delta}_i^1 \), \( {\delta}_i^2 \),…, \( {\delta}_i^l \)} where \( {\delta}_i^k \) is the distribution status of kth resource, i.e., 1 ≤ k ≤ l. If the player i is a SBS, i.e., i ∈ ℬ, the player i can request multiple resources. Therefore, the strategy set can be defined as a combination of requested resources S _{ i } = {∅, {\( {d}_i^1\left({\mathrm{\mathcal{I}}}_i^1\right) \)}, {\( {d}_i^1\left({\mathrm{\mathcal{I}}}_i^1\right) \), \( {d}_i^2 \)(\( {\mathrm{\mathcal{I}}}_i^2 \))},…, {\( {d}_i^1\left({\mathrm{\mathcal{I}}}_i^1\right) \), \( {d}_i^2\left({\mathrm{\mathcal{I}}}_i^2\right) \),…, \( {d}_i^l\left({\mathrm{\mathcal{I}}}_i^l\right) \)}} where \( {\mathrm{\mathcal{I}}}_i^k \) is the player i’s requested amount for the kth resource; each player’s strategy set is finite with 2^{ l } elements.

The U _{ i } is the payoff received by the player i. If the player i is the VBP, i.e., \( i\in \mathbf{\mathcal{C}} \), it is the total profit obtained from the resource distribution for SBSs. If the player i is a SBS, i.e., i ∈ ℬ, the payoff is determined as the outcomes of the distributed resources minus the cost of corresponding resources.

The T is a time period. The \( {\mathbb{G}}^U \) is repeated t ∈ T < ∞ time periods with imperfect information.

ℙ is the finite set of players \( \mathrm{\mathbb{P}}=\left\{\mathbf{\mathcal{B}},\mathbf{\mathcal{X}}\right\} \) where ℬ = {b _{1}, …, b _{ n }} is a set of multiple SBSs and \( \mathbf{\mathcal{X}} \) = {x _{1}, …, x _{ m }} is a set of multiple MUs, which are assumed guests in the lowerlevel Indian restaurant.

\( {\mathbf{\mathcal{L}}}_i=\Big\{{\mathrm{\mathcal{R}}}_i^1 \), \( {\mathrm{\mathcal{R}}}_i^2 \),…, \( {\mathrm{\mathcal{R}}}_i^l\Big\} \) is the finite set of the player i’s resources, i.e., i ∈ ℬ. Elements in ℒ _{ i } metaphorically represent different dishes on the buffet table in the ith lowerlevel Indian restaurant; there are total n lowerlevel Indian restaurants.

\( {\mathbf{\mathcal{T}}}_i \) is the set of strategies with the player i. If the player i is a SBS, i.e., i ∈ ℬ, the strategy set can be defined as \( {\mathbf{\mathcal{T}}}_i \) = {\( {\lambda}_i^1 \), \( {\lambda}_i^2 \),…, \( {\lambda}_i^l \)} where \( {\lambda}_i^k \) is the price of the kth resource in the ith SBS. If the player i is a MU, i.e., \( i\in \mathbf{\mathcal{X}} \), the player i can request multiple resources. Therefore, the strategy set can be defined as a combination of requested resources \( {\mathbf{\mathcal{T}}}_i \) = {∅, {\( {\mathrm{\mathcal{R}}}_i^1\left({\xi}_i^1\right) \)}, {\( {\mathrm{\mathcal{R}}}_i^1\left({\xi}_i^1\right) \),\( {\mathrm{\mathcal{R}}}_i^2 \)(\( {\xi}_i^2 \))},…, {\( {\mathrm{\mathcal{R}}}_i^1\left({\xi}_i^1\right) \), \( {\mathrm{\mathcal{R}}}_i^2\left({\xi}_i^2\right) \),…, \( {\mathrm{\mathcal{R}}}_i^l\left({\xi}_i^l\right) \)}} where \( {\xi}_i^k \) is the MU i’s request amount for the kth resource.

The U _{ i } is the payoff received by the player i. If the player i is a SBS, i.e., i ∈ ℬ, it is the total profit obtained from the resource allocation for MUs. If the player i is a MU, i.e., \( i\in \mathbf{\mathcal{X}} \), the payoff is determined as the outcomes of the allocated resources minus the cost of corresponding resources.

The T is a time period. The \( {\mathbb{G}}^L \) is repeated t ∈ T < ∞ time periods with imperfect information.
4 Proposed resource sharing algorithm in CRAN systems
In this section, we present our resource sharing algorithm, which employs a hierarchical twolevel approach. And then, the proposed scheme is described strategically in a ninestep procedure through the coopetition concept.
4.1 CRAN resource sharing in the upper Indian buffet game
In this subsection, we consider the upperlevel Indian buffet game. In CRAN systems, there are multiple resource types, and multiple SBSs request different resources to the VBP. In this study, we mainly consider four resource types: CPU, memory, storage, and network bandwidth. Let \( \mathbb{D} \) denote a set of resources in the VBP; \( \mathbb{D}=\Big\{{d}_1 \) = CPU; d _{2} = memory; d _{3} = storage; d _{4} = bandwidth } where each d represents the available amount of corresponding resource. Virtualization technology is used to collect these resources from CPs, and they are dynamically shared among SBSs. In our upperlevel Indian buffet game, there are one VBP and n SBSs. The VBP is responsible for the cloud resource control and distributes resources over multiple SBSs. Each SBS is deployed for each microcell and covers relatively a small area. In general, SBSs are situated around high traffic density hot spots to support QoSensured applications. To get an effective solution for the upperlevel Indian game, we focused on the basic concept of the shapley value (SV). It is a wellknown solution idea for ensuring an equitable division, i.e., the fairest allocation, of collectively gained profits among the several collaborative players [5].
When the requested amount of kth resource (\( {\partial}_i^k \), 1 ≤ k ≤ 4) of the ith SBS (SBS_{ i }) is less than the distributed resource (\( {\mathcal{A}}_i^k \)), i.e., \( {\partial}_i^k<{\mathcal{A}}_i^k \); the SBS_{ i } can waste this excess resource, and the property loss is estimated based on the resource unit price (\( U\_{\mathcal{P}}_i^k \)). \( U\_{\mathcal{P}}_i^k \) value is adaptively adjusted in the lowerlevel Indian buffet game; it is discussed in Section 4.3. In this case, the value function (v(SBS_{ i })) of the SBS_{ i } becomes \( v\left({\mathrm{SBS}}_i\right)=U\_{\mathcal{P}}_i^k\times \left({\mathcal{A}}_i^k{\partial}_i^k\right) \). Conversely, if \( {\partial}_i^k>{\mathcal{A}}_i^k \), the deficient resource amount \( \left({\partial}_i^k{\mathcal{A}}_i^k\right) \) is needed in the SBS_{ i }. Therefore, the value function becomes \( v\left({\mathrm{SBS}}_i\right)=U\_{\mathcal{P}}_i^k\times \left({\partial}_i^k{\mathcal{A}}_i^k\right) \). We assume that ℕ = {\( \mathbf{\mathcal{C}} \) = {VBP}∪ ℬ = {b _{1}, …, b _{ n }}} is a set of upper game players and v(·) is a real valued function defined on all subsets of ℬ satisfying v(∅) = 0. Therefore, in our game model, a nonempty subset (c) of ℬ is called a coalition. A set of games with a finite number of players is denoted by Γ. Given a game (ℬ, v(·)) ∈ Γ, let ℂ^{ k } be a coalition structure of ℬ for the kth resource. In particular, \( {\mathrm{\mathbb{C}}}^k=\left\{{\boldsymbol{c}}_1^k, \dots,\ {\boldsymbol{c}}_j^k\right\} \) is a partition of ℬ, that is, \( {\boldsymbol{c}}_f^k\kern0.5em {\displaystyle {\displaystyle \cap \kern0.5em {\boldsymbol{c}}_h^k=\varnothing }} \) for f ≠ h and \( {\displaystyle {\cup}_{t=1}^j{\boldsymbol{c}}_t^k=\mathbf{\mathcal{B}}} \).
4.2 CRAN resource sharing in a lowerlevel Indian buffet game
In the lowerlevel Indian game model, multiple MUs request different resources to their corresponding SBS. Let \( {\mathrm{MU}}_i^j \) be the MU j in the area of SBS_{ i } and ℒ _{ i } denote a set of resources in the ith SBS; ℒ _{ i } = {\( {\mathrm{\mathcal{R}}}_i^1 \) = CPU, \( {\mathrm{\mathcal{R}}}_i^2 \) = memory, \( {\mathrm{\mathcal{R}}}_i^3 \) = storage, \( {\mathrm{\mathcal{R}}}_i^4 \) = bandwidth}. Each \( {\mathrm{\mathcal{R}}}_i^k \) represents the available amount of kth resource in the SBS_{ i }; these resources are obtained from the VBP through the upperlevel Indian game. Individual MU attempts to actually purchase multiple resources based on their unit prices \( U\_{\mathcal{P}}_i^k \), where 1 ≤ k ≤ 4 and i ∈ ℬ.
s.t., \( {b}_j\left({\xi}_j^k(i)\right)={\omega}_j^k\times \log \left({\xi}_j^k(i)\right) \) and mp ^{ k } ≤ \( U\_{\mathcal{P}}_i^k \) ≤ Mp ^{ k }
s.t., \( \Delta U\_{\mathcal{P}}_i^k(t)=\frac{\left({\varPsi}_i^k(t){\varPsi}_i^k\left(t1\right)\right)}{\varPsi_i^{t1}\left(t1\right)} \), \( \Omega =\frac{\left({\lambda}_i^k(t){\lambda}_i^k\left(t1\right)\right)}{\Delta U\_{\mathcal{P}}_i^k(t)} \), and \( \boldsymbol{\varLambda} \left[\mathcal{K}\right]=\left\{\begin{array}{c}\hfill \boldsymbol{\varLambda} \left[\mathcal{K}\right]=m{p}^k,\ if\kern0.5em K<m{p}^k\kern2.5em \hfill \\ {}\hfill \boldsymbol{\varLambda} \left[\mathcal{K}\right]=K,\ if\kern0.5em m{p}^k\le K\le M{p}^k\hfill \\ {}\hfill \kern1em \boldsymbol{\varLambda} \left[\mathcal{K}\right]=M{p}^k,\ if\kern0.5em K>M{p}^{k\kern0.5em }\kern2.25em \hfill \end{array}\right. \)
4.3 The main steps of the proposed algorithm

Step 1: At the initial time, all SBSs have same price strategies (\( \mathbf{\mathcal{T}} \)). At the beginning of the game, this starting guess is a reasonable assumption.

Step 2: At each game period, the VBP collects available resources from CPs using the virtualization technology and distributes these resources to each SBS according to Eqs. (1)–(4).

Step 3: Individual MU in each cell attempts to actually purchase multiple resources from corresponding SBS. Based on this information, each SBS dynamically decides the price strategy (\( \mathbf{\mathcal{T}} \)) using Eqs. (6) and (7).

Step 4: At each game period, the VBP redistributes periodically the CP resources based on the currently calculating ϕ values; it is the upperlevel Indian game.

Step 5: Based on the current price (\( \mathbf{\mathcal{T}} \)), each MU dynamically decides the amount of purchasing resources according to Eq. (6).

Step 6: Strategy decisions for each game player are made in an entirely distributed manner.

Step 7: Under widely diverse CRAN environments, the VBP, SBSs, and MUs are selfmonitoring constantly based on the iterative feedback mechanism.

Step 8: If the change of prices in all SBSs is within a predefined bound (ε), this change is negligible; proceed to step 9. Otherwise, proceed to step 2 for the next iteration.

Step 9: Game is temporarily over. Ultimately, the proposed scheme reaches an effective resource sharing solution. When the CRAN system status is changed, it can retrigger another gamebased resource sharing procedure.
5 Performance evaluation
In this section, the effectiveness of our proposed scheme is validated through simulation. Using a simulation model, the performance of our proposed scheme is compared with three existing CRAN resource sharing schemes—the BPUV scheme [4], JCCN scheme [9], and CPRS scheme [10]. All schemes are implemented with a polynomial time computational complexity.
5.1 Simulation model, parameters, and scenario

The simulated model was assumed as a CRAN system with one VBP, 10 SBSs, and 100 MUs.

The process for new application service requests was Poisson with rate σ (applications/MU/s), and the range of offered load was varied from 0 to 3.0.

The total capacity of resources were CPU (d _{1} = 3.6 GHz), memory (d _{2} = 240 Mbyte), storage (d _{3} = 480 Gbyte), and bandwidth (d _{1} = 30 Mbps).

System performance measures obtained on the basis of 50 simulation runs were plotted as a function of the offered load.

Each application service had its own application type and requires different resource requirements. They were generated with equal probability.

The durations of services were exponentially distributed.

SBSs had the predefined minimum and maximum resource unit prices.

For simplicity, we assumed the absence of noise or physical obstacles in our experiments.
Application and system parameters used in the simulation experiment
Application type  Applications  Resource type  Minimum resource requirement  Maximum resource requirement 

I  Voice telephony  CPU  30 MHz  60 MHz 
Memory  12 Mbyte  24 Mbyte  
Storage  4 Gbyte  8 Gbyte  
Bandwidth  128 K  512 K  
II  Video phone  CPU  60 MHz  120 MHz 
Memory  24 Mbyte  48 Mbyte  
Storage  8 Gbyte  16 Gbyte  
Bandwidth  256 K  640 K  
III  Remote login  CPU  15 MHz  40 MHz 
Memory  6 Mbyte  12 Mbyte  
Storage  2 Gbyte  4 Gbyte  
Bandwidth  64 K  384 K  
IV  Teleconference  CPU  60 MHz  150 MHz 
Memory  24 Mbyte  96 Mbyte  
Storage  8 Gbyte  32 Gbyte  
Bandwidth  256 K  896 K  
Parameter  Value  Description  
n  10  The number of SBSs  
m  10  The number of MUs in each SBS  
l  4  The number of resources  
mp , Mp  0.5 , 2  The predefined minimum and maximum price boundaries  
ω ^{1},ω ^{2},ω ^{3},ω ^{4}  1.5, 1.2, 1, 1.5  Willingness to pay the price for each resource  
ε  0.3  The predefined bound for strategy stability  
Parameter  Initial  Description  Values  
\( U\_\mathcal{P} \)  1  The unit price for each resource  Dynamically adjustable 
5.2 Simulation results
As mentioned earlier, the BPUV scheme [4], JCCN scheme [9], and CPRS scheme [10] have been recently published and introduced unique challenges to efficiently solve the resource sharing problem in CRAN systems. However, they are successful only in certain circumstances. Compared to these schemes, we can confirm the superiority of our proposed hierarchical game approach.
5.3 Analysis and discussion
In summary, the simulation analysis obtained from Figs. 2, 3, 4, and 5 shows the performance trends of all the schemes. They are very similar. This is because the main design goals of all the schemes are the same. However, based on the twolevel Indian buffet game approach, the proposed scheme adaptively responds to the current CRAN system conditions in a distributed manner. Therefore, we can say that the proposed scheme is much more flexible, adaptable, and able to sense the current CRAN environment. Therefore, as expected, we achieve a better CRAN system performance than the BPUV scheme [4], JCCN scheme [9], and CPRS scheme [10].
6 Conclusions
Efficient and finegrained resource sharing becomes an increasingly important and attractive control issue for newgeneration CRAN systems. In this work, we propose a novel multiresource sharing scheme, which is framed as a twolevel Indian buffet game model: the upperlevel Indian game is played among VBPSBSs, and the lowerlevel Indian game is played among SBSsMUs. Based on the hierarchical interaction mechanism, the VBP, SBSs, and MUs are intertwined and make decisions during the stepbystep interactive feedback process. The novelty of our work lies in the fact that we develop a new resource sharing paradigm and apply this paradigm to control the CRAN environment while comparing its performance to other existing schemes. From the simulation results, we can claim that our proposed approach effectively works to improve the system efficiency and utilization of resource usage in dynamically changeable CRAN environments.
In this study, only a specific implementation case of the Indian buffet game is addressed as a restricted version. However, there are insights that can be applied to open questions in the field of various resource sharing research areas. Therefore, our work opens a door to some interesting extensions. For the future work, revenue sharing algorithms with cooperative game models can be implemented. Another issue for further study is how the quality of experience (QoE) could be resolved with the original QoS in CRAN systems.
Declarations
Acknowledgements
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the ITRC (Information Technology Research Center) support program (IITP2015H8501151018) supervised by the IITP (Institute for Information & communications Technology Promotion) and was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (NRF2015R1D1A1A01060835)
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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