CN110113213B

CN110113213B - A collaborative cache deployment method based on cloud radio access network architecture

Info

Publication number: CN110113213B
Application number: CN201910407477.4A
Authority: CN
Inventors: 陈玉婷; 唐斌; 叶保留; 陆桑璐
Original assignee: Nanjing University
Current assignee: Nanjing University
Priority date: 2019-05-15
Filing date: 2019-05-15
Publication date: 2021-07-16
Anticipated expiration: 2039-05-15
Also published as: CN110113213A

Abstract

The invention discloses a cooperative cache deployment method based on cloud wireless access network architecture. It includes the following steps: obtaining the RRH distribution, user groups and their weights, and the distribution of the connection relationship between RRHs and user groups according to the network topology; building an energy consumption model for transferring files on different paths and caching files on a storage device. Model; based on the energy consumption model, the objective function of minimizing system energy consumption is derived, and it is transformed into the transmission energy consumption problem of minimizing the cache scheme under the limit of file storage energy consumption; the transmission energy consumption optimization problem is transformed into a weighted maximum coverage problem, and the solution is The file placement scheme on the BBU and all RRHs is obtained; for each given storage energy consumption, it is combined with the corresponding transmission energy consumption to obtain the cache scheme with the smallest total energy consumption. The present invention provides an efficient method for cooperative cache deployment in a cloud wireless access network, which effectively reduces the total energy consumption in the network.

Description

Cooperative cache deployment method based on cloud wireless access network architecture

Technical Field

The invention belongs to the technical field of networks, and particularly relates to a cooperative cache deployment method based on a cloud wireless access network architecture.

Background

The rapid growth of mobile devices and applications has led to an explosive growth in mobile data traffic, which by 2022 will reach 77.5EB for every month worldwide, corresponding to 119 times 2012. To solve the problem of rapid data traffic increase, content distribution networks have been developed, which deploy data content to the "edge" of the network closest to the user by adding a new layer of network architecture to the existing internet, so that the user can obtain the required data nearby, and thus, backhaul link congestion is solved, and transmission delay is improved. However, due to the rise of services such as ultra-high-definition video, augmented reality, virtual reality games, smart city construction and the like, the user not only can meet the requirement of improving the data transmission rate, but also can put forward the harsh requirement of no perception on time delay. For this reason, operators also make a series of efforts under 4G network architecture, such as deploying more infrastructure, increasing network bandwidth, distributed Multiple Input Multiple Output (MIMO) technology using multiple antennas, etc., however, the number of users is increasing, and the service quality is not effectively improved due to limited bandwidth resources, high deployment cost, heavy control signal burden.

A Cloud Radio Access Network (C-RAN) is a novel Radio Access Network architecture with high capacity, low energy consumption and low cost, which is proposed according to the existing Network conditions and the trend of technical progress. The wireless network communication system is a green framework based on centralized processing, cooperative radio and a real-time cloud computing framework. A typical C-RAN architecture consists of three parts, as shown in fig. 1: (1) a distributed network consisting of remote radio frequency modules (RRHs) and antennas; (2) a centralized baseband pool module (BBU) consisting of a high performance processor and real-time virtual technology; (3) high bandwidth low latency optical transmission networks. The distributed remote radio frequency modules form a high-capacity and wide-coverage wireless network, and the units have small volume and low deployment cost and are easy to install and deploy in a large range. The baseband pool is composed of high-performance processors, and is connected together through a real-time virtual technology to provide powerful computing and storage resources. The baseband pool is connected with the core network through a Backhaul link (Backhaul), and data transmission is performed between the baseband pool and each remote radio frequency module through a high-bandwidth low-delay Fronthaul link (Fronthaul).

The rapid growth of mobile data traffic dominated by video traffic remains a heavy burden on the limited backhaul link bandwidth, which is a bottleneck affecting overall network performance. It is therefore intuitive to think that video data can be cached at the edge of the network closer to the user, which is called "edge caching". The edge caching based on the C-RAN network architecture is widely researched and has a practical significance, on one hand, a large part of popular content (such as popular music and video) in mobile multimedia traffic is repeatedly downloaded, and the data redundancy in the network is high. Research shows that the popularity of video files is often consistent with Zipf distribution, and most of the requests for video are concentrated on a few videos. Thus, actively caching popular video file sets on infrastructure such as BBUs and RRHs and updating the file sets in a time period with a small user request amount (such as night) can greatly improve network performance; on the other hand, the price of the storage device is continuously reduced, and more cache modules can be deployed under the C-RAN architecture to store resources and replace limited bandwidth resources, so that an operator can serve users nearby, and energy is saved.

The key point in designing the caching strategy is to make a balance between the cost and the benefit of the storage cache, but most of the existing energy-oriented caching work considers optimizing the energy consumption of the system on the premise of fixing the caching capacity, and almost no work considers designing the optimal caching capacity and the optimal deployment scheme for each caching facility so as to minimize the global energy consumption. This is not reasonable, on one hand, the user density covered by each RRH is different, and it is very practical to deploy a larger cache capacity and cache more files in RRHs with higher population density. On the other hand, the cost of the storage device is continuously reduced, the deployment of a large-capacity cache device is easy to realize, and the RRH and the BBU can start part or all of the storage capacity to perform caching according to the required decision.

Disclosure of Invention

The purpose of the invention is as follows: based on the defects, the invention provides a reasonable and efficient cache deployment method in a cloud wireless access network, so as to reduce the total energy consumption of a system caused by the daily request process of a user for videos.

The technical scheme is as follows: the invention discloses a cooperative cache deployment method based on a cloud wireless access network architecture, which comprises the following steps:

(1) constructing a network topology based on a cloud wireless access network architecture, and acquiring RRH distribution, user groups and weights thereof, and connection relation distribution between RRHs and the user groups;

(2) establishing an energy consumption model for transmitting files on different paths and an energy consumption model for caching the files on a storage device;

(3) deducing a target function for minimizing the energy consumption of the system based on an energy consumption model, and converting the target function into a transmission energy consumption problem of a minimized cache scheme under the limitation of file storage energy consumption;

(4) converting the transmission energy consumption optimization problem into a weighted maximum coverage problem, and solving to obtain file placement schemes on the BBU and all the RRHs;

(5) and for each given storage energy consumption, combining the storage energy consumption with the corresponding transmission energy consumption, and finally solving to obtain a caching scheme with the minimum total energy consumption.

Preferably, the step (1) comprises:

(1a) the RRH set is recorded as R, and the BBU and RRH sets are recorded as R^*Counting the user distribution condition in the RRH coverage range, and dividing users with the same RRH connection relationship into a user group U belonging to U ═ 1,2, …, | U | } according to the condition that the users are covered by RRH, wherein U represents a user group set;

(1b) for each user group, recording the set of RRHs n (u) they can access, and for each RRH, recording the set of users n (r) they can cover;

(1c) summing all the users in the same user group, dividing the sum by the total number of the users in all the user groups to obtain the weight w of the user group_u。

Preferably, the step (2) includes:

(2a) recording the energy consumption of a file stored on the BBU/RRHs as s;

(2b) the energy consumed by the core network to send a file to the BBU is recorded as sigma₀BBU to RRH set R, R ═ RRH { RRH₁,RRH₂,…,RRH_nWhen sending a file, the energy consumed is recorded as [ sigma ]₁,σ₂,…,σ_n}，RRH_rThe energy consumed by sending a file to a user packet u is denoted σ_ru。

Preferably, the step (3) includes: for one cache scheme Ψ, Ψ ═ C₁,C₂,…,C_F) Wherein

A set of infrastructures representing the cached file f,

i.e. representing the memory consumption of the caching scheme, E_T(Ψ) represents the transmission energy consumption of the scheme, and the MinEC problem table of the transmission energy consumption caused by the minimum caching scheme under the condition that the upper limit of the file storage energy consumption is BShown as follows:

min:E_T(Ψ)

s.t.E_S(Ψ)≤B

preferably, the step (4) includes:

(4a) on the basis of the step (3), the MinEC problem of minimizing energy consumption is converted into the corresponding energy saving problem (MaxES): for the caching scheme Ψ, order D_T(Ψ) represents a transmission energy savings as compared to when both users acquire video files from the core network,

wherein, w_uA weight representing the user group u; n (u) represents the set of RRHs visible to the user group u; f ∈ F ═ {1,2, …, | F | } denotes the popular video file set, p_fRepresenting the popularity of file f; phi (-1, u) ═ min_r′∈N(u){σ₀+σ_r′+σ_r′uRepresenting the energy required by the user group u to download the video file from the core network; phi (r, u) represents the energy required by the user group u to download the file under the caching scheme;

thus, the MaxES problem is formalized as:

max:D_T(Ψ)

s.t.E_S(Ψ)≤B

(4b) the MaxES problem is decomposed into two sub-problems to solve: (i) place k copies of any file, from R^*Which k infrastructure is chosen to minimize transmission power consumption, and (ii) how many copies should be placed for each video file in the set of files to minimize transmission power consumption for the scheme, wherein

For sub-problem (i), it is translated into the maximum coverage problem (MCLP): how at R^*The problem that k file cache copies are set to enable users who can receive services to save energy is maximized is solved, MCLP is solved by means of a greedy selection-based strategy, and the optimal position and delta for placing the kth copy are obtained in the kth iteration process_k,f，Δ_kfRepresenting file f placing the k < th > copy over k-1 copiesMore saved edge energy;

for sub-problem (ii), let n_fRepresenting the number of copies that file f should be placed, the energy savings of this scheme is expressed as

Will be delta_k,fAnd (5) sorting in a descending order, selecting the top B/s values, and combining the sub-problems (i) to obtain the number of copies to be placed in each file and the placement positions.

Preferably, the method for calculating the energy Φ (r, u) required by the user group u to download the file in the caching scheme includes: (i) if the user group u can only download files from the core network in the scheme, phi (r, u) is min_r′∈N(u){σ₀+ σ r '+ σ r' u; (ii) if the user group u downloads the file from the BBU cache in the scheme, phi (r, u) is min_r′∈N(u){σ_r′+σ_r′u}; (iii) if the user group u downloads the file from the RRH cache which is not in the coverage area in the scheme, phi (r, u) is min_r′∈N(u){σ_r+σ_r′+σ_r′u}; (iv) if the user group u downloads the file from the RRH buffer in the coverage area in the scheme, phi (r, u) is min_{r′∈N(u)\r}{σ_ru,σ_r+σ_r′+ σ r 'u, where r' represents the index of the RRH to which the user packet is connected.

Preferably, the popularity p of said file f_fA Zipf distribution was used.

Preferably, the step (5) includes: and (4) obtaining the saved transmission energy consumption of each cache scheme with the storage energy consumption of B through the step (4), and recording the saved transmission energy consumption as D_T(Ψ_B) The problem of minimizing the overall energy consumption of the system is to find an optimal buffering scheme so that the sum of the storage energy consumption and the transmission energy consumption caused by the optimal buffering scheme is minimized, i.e. an optimal storage energy consumption value B is found^*So that B is^*＝argmin_B{D_T(Ψ_B) + B }, by solving for B^*＝argmax_B{D_T(Ψ_B) -B } obtaining a final caching scheme including caching the total number of files and the files on BBU/RRHsAnd caching the file number.

Preferably, the method further comprises: and updating the cached video file set periodically, and sending the files to the BBU and the RRHs in a multicast mode according to a caching scheme during the idle period of the network.

Has the advantages that: the invention provides a cooperative cache deployment method based on a cloud wireless access network architecture, and provides a method for reducing the total energy consumption of a system by utilizing cooperative cache file deployment between BBUs/RRHs, and comprehensively considers the balance between user request weight covered by each RRH, video file flow degree distribution and cache file cost and profit on the basis, so that the data of the video file can be reasonably distributed on the BBUs/RRHs, the possibility of a user obtaining video data nearby is improved, and the energy consumption of the system is reduced.

Drawings

FIG. 1 is a schematic diagram of a cloud wireless access network caching architecture and a user download file path;

FIG. 2 is a schematic diagram of transmission energy consumption on a path of a user downloading a file in a cloud wireless access network;

FIG. 3 is a general flow diagram of the method of the present invention;

fig. 4 is a bipartite graph of a network topology of the present invention and the bipartite graph after conversion to a maximum coverage problem.

Detailed Description

The technical scheme of the invention is further explained by combining the attached drawings.

The invention focuses on the problem of how to perform cache deployment in the existing cloud wireless access network so as to reduce the energy consumption of the system. Research has shown that the popularity distribution of a set of popular video files is relatively stable over a long period of time, and most video requests are concentrated in a small fraction of the video files, so caching these popular video files can improve system performance. However, the more the number of cached files is, the better, a trade-off between cost and benefit of caching needs to be made. On the basis, the invention provides a method for reducing the total energy consumption of the system by utilizing the cooperative deployment of the cache files among the BBUs/RRHs, and comprehensively considers the balance among the user request weight covered by each RRH, the video file stream popularity distribution and the cost and the benefit of the cache files on the basis, so that the data of the video files can be reasonably distributed on the BBUs/RRHs, the possibility of the users for obtaining the video data nearby is improved, and the energy consumption of the system is reduced.

Referring to fig. 3, the cooperative cache deployment method based on the cloud wireless access network architecture provided by the invention comprises the following steps:

step 1, constructing a popular video file stream popularity distribution model.

Describing the popularity distribution of the video file by a Zipf distribution model, wherein the Zipf distribution is in the following specific form:

wherein F is 1,2, …, | F |, which represents the number of the file in the video file set F; p is a radical of_fIndicating the popularity of video file f, i.e. the probability that the file is requested by a user. Can be obtained by the following steps:

11) analyzing the daily request of the user to the video file in the network of the current day, recording the file ID and the requested times, selecting the front | F | video files with the most front watching quantity (selecting the value of | F | according to the actual scene to ensure that the part of the files are requested by enough users), and dividing the request number of each file by the total file number to obtain the requested frequency p of the video file in the current day_f。

12) And taking logarithms of two sides of Zipf at the same time to obtain: logp (Logp)_f- γ logf-C. C represents an independent quantity, the invention being based on f and p_fGamma is calculated for the data taken each day, i.e. the document number f and the probability that the document is requested that day_pfObtaining the gamma value of the current day by using linear regression; for the file and request distribution over a long period of time, the average is taken as the final gamma value.

And 2, constructing a network topology and user grouping, and calculating user grouping weight.

The step is mainly to complete the construction of the network topology concerned by the invention, and the coverage condition of the RRH to the user needs to be counted. Referring to fig. 1, in a cloud wireless access networkUsers with the same RRH connection relationship can be regarded as a whole, and are called user groups. One RRH can cover multiple user groups, and multiple RRHs can be accessed by one user group. When the topological graph is actually constructed, the area covered by the whole network can be divided into small equal blocks, and the users in each equal block are close to each other and are close to the RRH_sHave the same connection relationship and can be regarded as a user group. Dividing the number of users belonging to the same user group by the sum of the total number of users of all the user groups to obtain the weight w of the user group_u。

When a user group u requests a file f, (i) if a directly connected RRH (local RRH) caches the file, the user group will directly acquire the file from the RRH cache; (ii) if the condition (i) is not satisfied, but the file is cached in the BBU, the user group can directly acquire the file from the BBU cache; (iii) if the condition (i) (ii) is not satisfied, but the file is not cached in an RRH (remote RRH) within the connection range, the user group acquires the file according to the path of the remote RRH cache-BBU-local RRH; (iv) and if the conditions are not met, acquiring the file from the core network.

And 3, constructing energy consumption models of the files transmitted on different paths and energy consumption models of the files cached on the storage device.

And counting the energy consumption caused by storing a file on the BBU/RRHs, and recording the energy consumption as s. Meanwhile, the energy consumed by the core network for sending a file to the BBU is counted and recorded as sigma₀(ii) a Statistics BBU to RRH set { RRH₁,RRH₂,…,RRH_nThe energy consumed in sending a file is denoted as { σ }₁,σ₂,…,σ_n}; statistics of RRH_rThe energy consumed by sending a file to a user packet u is denoted as σ_ruRefer to fig. 2.

And 4, deducing an objective function for minimizing the total energy consumption of the system, wherein the objective function is converted into the problem of transmission energy consumption caused by a minimized cache scheme under the limit of storage energy consumption of a given file.

For any one cache scheme Ψ, Ψ ═ (C)₁,C₂,…,C_F) Wherein

Representing the infrastructure set of the cache file f. Let E_S(Ψ) represents the memory consumption of the caching scheme, E_T(Ψ) represents the transmission energy consumption of the scheme, so the total energy consumption resulting from the scheme is E_S(Ψ)+E_T(Ψ). According to the above-mentioned energy model,

and E_T(Ψ) is closely related to the cache file scheme and the number of cache files. Therefore, it is translated into a problem of transmission energy consumption (MinEC) caused by the minimum caching scheme under the condition that the storage energy consumption of a given file is limited to B, and can be expressed as:

min:E_T(Ψ)

s.t.E_S(Ψ)≤B

and 5, in a network system in which a plurality of RRH request files can be connected in a user group, converting the transmission energy consumption minimization problem into a weighted maximum coverage problem aiming at each given storage energy consumption upper limit, and solving to obtain a file placement scheme on the BBU and all the RRHs.

The MinEC problem of minimum energy consumption is converted into the MaxES problem of maximum energy saving: for the caching scheme Ψ it is possible to,

let D_T(Ψ) represents the transmission energy that is expected to be conserved compared to when both users are acquiring video files from the core network:

wherein w_uA weight representing the user group u; n (u) represents the set of RRHs visible to the user group u; f is formed by F

{1,2, …, | F | } denotes the popular video file set, p_fRepresenting the popularity of file f; phi (-1, u) ═ min_r′∈N(u){σ₀+ σ r '+ σ r' u represents the energy required for user packet u to download the video file from the core network; phi (r, u) representsThe energy required by the user group u to download the file under this caching scheme. The MaxES problem can be formalized as:

max:D_T(Ψ)

s.t.E_S(Ψ)≤B

the MaxES problem is decomposed into two sub-problems to solve: (i) place k copies of any file, from R^*Which k infrastructures are chosen to minimize transmission power consumption? (ii) How many copies should be placed for each video file in the file set so that the transmission energy consumption of the scheme is minimal?

51) For the first sub-problem, it is translated into the maximum coverage problem (MCLP): how at R^*The problem of setting up k file cache copies to maximize energy saving for users of acceptable service is solved. Referring to fig. 4, a user grouping and caching infrastructure set R is first constructed^*The weight value on each edge in the graph represents the transmission energy that the placement of the file at that location can save for grouping users, denoted as Ω (r, u). For any user group u, marking the connected BBU and RRHs as r_u1,r_u2,…,r_u(n+1)So that

Ω(r_u1,u)≤Ω(r_u2,u)≤…≤Ω(r_u(n+1),u)

Then, the user group u node is split into n +1 nodes (u)₁,u₂,…,u_n+1) Node u_iThe weight of (d) is Ω (r)_ui,u)-Ω(r_u(i-1),u)(u₁＝Ω(r_u1U)), and only with node r_ui,r_u(i+1),…,r_u(n+1)Are connected. This process is disclosed in fig. 4 (with user packet 1 as an example, followed by RRHs)₁、BBU、RRH₂The weights of the connected edges are 3, 4 and 5 respectively, so that the edges are split into 3 nodes, and the weight values are 3, 1 and 1 respectively).

52) Using a strategy based on greedy selection to solve MCLP, and obtaining the optimal position and delta for placing the kth copy in the kth iteration process_k,fIn which Δ_kfRepresenting the margin energy gain saved by placing the kth copy in the file f more than the kth-1 copy;

53) let n be_fRepresenting the number of copies that file f should be placed, noting that the buffering scheme saves transmission energy equivalent to

Thus will be a_k,fAnd (4) sorting in a descending order, selecting the first B/s values (the storage energy consumption is limited to only buffer B/s files at most), and obtaining the number of copies to be placed in each file and the placement position. Namely, when the upper storage limit is B, file placement schemes on the BBU and all the RRHs are obtained.

And 6, combining each given storage energy consumption with the corresponding transmission energy consumption, and outputting a caching scheme with minimum total energy consumption.

For each storage energy consumption B, the corresponding cache scheme Ψ can be solved through the step (5)_BAnd its saved transmission energy yield D_T(Ψ_B). Thus by solving for B^*＝argmax_B{D_T(Ψ_B) And B, obtaining a final caching scheme which comprises the total cached file number and the cached file number on the BBU/RRHs.

And 7, periodically updating the cached video file set, and sending the file to the BBU and the RRHs in a multicast mode in the idle period of the network according to the cache deployment.

Claims

1. A cooperative cache deployment method based on a cloud wireless access network architecture is characterized by comprising the following steps:

2. The cooperative cache deployment method based on the cloud wireless access network architecture according to claim 1, wherein the step (1) comprises:

(1a) the RRH set is recorded as R, and the BBU and RRH sets are recorded as R^*Counting the user distribution condition in the RRH coverage range, and dividing users with the same RRH connection relationship into a user group U belonging to U ═ 1, 2., | U | } according to the condition that the users are covered by the RRH, wherein U represents a user group set;

3. The cooperative cache deployment method based on the cloud wireless access network architecture according to claim 2, wherein the step (2) comprises:

(2a) recording the energy consumption of a file stored on the BBU/RRHs as s;

(2b) the energy consumed by the core network to send a file to the BBU is recorded as sigma₀BBU to RRH set R, R ═ RRH { RRH₁，RRH₂，...，RRH_nWhen sending a file, the energy consumed is recorded as [ sigma ]₁，σ₂，...，σ_n}，RRH_rThe energy consumed by sending a file to a user packet u is denoted σ_ru。

4. The cooperative cache deployment method based on cloud wireless access network architecture as claimed in claim 3, whereinThe step (3) includes: for one cache scheme Ψ, Ψ ═ C₁，C₂，...，C_F) Wherein

A set of infrastructures representing the cached file f,

i.e. representing the memory consumption of the caching scheme, E_T(Ψ) represents the transmission energy consumption of the scheme, and the minimal transmission energy consumption MinEC problem caused by the minimum caching scheme given that the upper limit of the file storage energy consumption is B is represented as:

min：E_T(Ψ)

s.t.E_S(Ψ)≤B。

5. the cooperative cache deployment method based on the cloud wireless access network architecture as claimed in claim 3, wherein the step (4) comprises:

wherein, w_uA weight representing the user group u; n (u) represents the set of RRHs visible to the user group u; f ∈ F ═ 1, 2., | F | } represents a popular video file set, p_fRepresenting the popularity of file f; phi (-1, u) ═ min_r′∈N(u){σ₀+σ_r′+σ_r′uRepresenting the energy required by the user group u to download the video file from the core network; phi (r, u) representsThe energy required by the user group u to download the file under the caching scheme;

thus, the MaxES problem is formalized as:

max：D_T(Ψ)

s.t.E_S(Ψ)≤B

For sub-problem (i), it is translated into the maximum coverage problem (MCLP): how at R^*The problem that k file cache copies are set to enable users who can receive services to save energy is maximized is solved, MCLP is solved by means of a greedy selection-based strategy, and the optimal position and delta for placing the kth copy are obtained in the kth iteration process_k，f，Δ_k，fRepresenting that file f places the kth copy with more saved edge energy than the k-1 copies;

Will be delta_k，fAnd (5) sorting in a descending order, selecting the top B/s values, and combining the sub-problems (i) to obtain the number of copies to be placed in each file and the placement positions.

6. The cooperative cache deployment method based on the cloud radio access network architecture according to claim 5, wherein the energy Φ (r, u) calculation method required by the user group u to download the file under the cache scheme includes:

(i) if the user group u can only download files from the core network in the scheme, phi (r, u) is min_r′∈N(u){σ₀+σr′+σr′u；

(ii) If the user group u downloads the file from the BBU cache in the scheme, phi (r, u) is min_r′∈N(u){σ_r′+σr′u；

(iii) If the user group u downloads the file from the RRH cache which is not in the coverage area in the scheme, phi (r, u) is min_r′∈N(u){σ_r+σ_r′+σ_r′u}；

(iv) If the user group u downloads the file from the RRH buffer in the coverage area in the scheme, phi (r, u) is min_{r′∈N(u)\r}{σ_ru，σ_r+σ_r′+σ_r′u}，

Where r' represents the number of RRHs to which the user packet is connected.

7. The cooperative cache deployment method based on cloud wireless access network architecture as claimed in claim 5, wherein the popularity p of the file f_fA Zipf distribution was used.

8. The cooperative cache deployment method based on the cloud wireless access network architecture as claimed in claim 5, wherein the step (5) comprises: and (4) obtaining the saved transmission energy consumption of each cache scheme with the storage energy consumption of B through the step (4), and recording the saved transmission energy consumption as D_T(Ψ_B) The problem of minimizing the overall energy consumption of the system is to find an optimal buffering scheme so that the sum of the storage energy consumption and the transmission energy consumption caused by the optimal buffering scheme is minimized, i.e. an optimal storage energy consumption value B is found^*So that B is^*＝argmin_B{D_T(Ψ_B) + B }, by solving for B^*＝argmax_B{D_T(Ψ_B) And B, obtaining a final caching scheme which comprises the total caching file number and the caching file number on the BBU/RRHs.

9. The cooperative cache deployment method based on the cloud wireless access network architecture as claimed in claim 1, wherein the method further comprises: and updating the cached video file set periodically, and sending the files to the BBU and the RRHs in a multicast mode according to a caching scheme during the idle period of the network.