Satellite-ground collaborative content distribution architecture service caching method for low-orbit satellite internet

Abstract

The invention discloses a satellite-ground collaborative content distribution architecture service caching method for a low-orbit satellite internet, and relates to the field of wireless communication. Comprising the following steps: aiming at a satellite end, collecting content request information of a user, unloading partial content to a ground server according to the request information, responding to a request of the partial content by the ground server, and directly distributing the rest request content by a satellite. For the ground server, in order to minimize the time delay in the distribution process, the cache state of the ground server needs to be optimized, so that the cache hit rate of the ground server is maximized. The maximization of the cache hit rate is taken as an optimization objective, and an optimization problem is converted into a '01 'knapsack problem. The invention provides an improved binary particle swarm optimization (EBPSO) algorithm aiming at the optimization problem, and outputs an optimal cache decision of the ground server. According to the invention, the time delay of the architecture distribution process can be reduced.

Description

Technical Field

[0001] The present invention mainly relates to storage resource allocation in the field of wireless communication with limited resources in a satellite-ground collaborative network, and specifically studies a service caching method for a content distribution architecture of a low-orbit satellite Internet satellite-ground collaborative network. Background Art

[0002] In the future 6G era, the vision of space-ground integration will be realized. With the continuous development of information networks and satellite communication technologies, satellite networks and ground networks have gradually merged, resulting in a new satellite-ground collaborative network that has a wider coverage and better anti-interference capabilities than traditional single satellite or ground networks.

[0003] In the content distribution architecture based on the satellite-ground collaborative network, since the direct distribution latency of the ground server is lower than that of the satellite, and the satellite will unload part of the content to the ground server during the distribution process, if the ground server caches the requested content, it will be directly distributed, and other requests will be distributed by the satellite. At this time, the latency of the entire distribution process is determined by the cache hit rate of the ground server. When the hit rate is higher, it means that the number of contents distributed by the ground server is more, so the latency of the entire distribution process is smaller. Therefore, it is very necessary to design an effective ground server caching method to maximize its cache hit rate. Summary of the invention

[0004] The purpose of the present invention is to provide a low-orbit satellite Internet satellite-ground collaborative content distribution architecture service caching method. For the satellite end, by collecting user request information, the number and request times of each requested content are counted. According to the request information, part of the content is unloaded to the ground server, and the ground server responds to the request for the part of the content, and the remaining requested content is directly distributed by the satellite. In order to minimize the delay in the distribution process, it is necessary to optimize the cache state of the ground server to maximize its cache hit rate.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A low-orbit satellite Internet satellite-ground collaborative content distribution architecture service caching method comprises the following steps:

[0007] S1: Establish a content distribution architecture model based on a satellite-ground collaborative network, including low-orbit satellites and ground servers; both low-orbit satellites and ground servers have caching and distribution functions, and ground servers have computing functions and can perform content version conversion;

[0008] S2: The satellite collects content request information from each user in the network and makes statistics;

[0009] S3: Establish a cache optimization model, that is, minimize the delay of the distribution process, convert the goal of minimizing the delay into maximizing the cache hit rate. The cache hit rate refers to the proportion of the content cached in the ground server to the content requested by the user, and establish the cache optimization model as a "01" knapsack problem;

[0010] S4: Use binary particle swarm optimization algorithm to solve the "01" knapsack problem and obtain the optimal cache decision for the ground server;

[0011] S5: The satellite offloads part of the statistical information to the ground server cache based on the best cache decision. The content request information cached by the ground server is distributed by the ground server, and other requests are distributed directly by the satellite.

[0012] Furthermore, the satellite caches all content request information in the network, the ground server only caches the basic version of the content request information, and the content request information is only transcoded from the basic version to other versions, and the other versions are not converted to each other.

[0013] Furthermore, the content request information collected by the satellite includes the content numbers requested by all users, and counts the number of requests for each number.

[0014] Furthermore, the S3 specifically includes the following steps:

[0015] S31: Establish a cache optimization model as the "01" backpack model. The cache capacity of the ground server is regarded as the backpack capacity, and the size of the content and the number of requests are regarded as the weight and value of the item. The goal of maximizing the cache hit rate needs to be achieved within the limited capacity.

[0016] S32: Divide the users into multiple multicast groups G={g 1,1 ,…,g V,E}; Obtain the number of users in each multicast group by counting content request information The formula for calculating the total number of users requesting is: Among them, g i,j A multicast group consisting of users who requested version j of content i; For multicast group g i,j The number of users in the network, V is the number of content request information in the network, and E is the number of versions of the content request information;

[0017] S33: Add the number of requests for all versions of content i, and the calculation formula is: Where val i is the value of content i, and the value of each content is obtained: val=(val1,…,val V );

[0018] S34: Calculate the cache status of the ground server, C = (c1, ..., c V ), where c i ∈{0,1}, the value is 0, which means that the content i is not cached, and the value is 1, which means that i is cached; let the total value of the content val sum Maximum, val sum =C T ·val.

[0019] Furthermore, the S4 specifically includes the following steps:

[0020] S41: In the EBPSO algorithm mechanism, a group of particles form a population, where each particle represents a cache state; initialize the position x of particle i i and speed v i ; The total value of the content val calculated using the cache state represented by each particle sum As the fitness of each particle, the position P of the particle with the current best fitness value and the historical best position of the i-th particle are recorded.

[0021] S42: For particles that violate the storage capacity constraint of the ground server, the fitness value is recorded as 0. If the fitness of any particle in the particle swarm is greater than the current optimal fitness value, P is replaced by the fitness of the corresponding particle;

[0022] S43: traverse each particle in the particle swarm and reinitialize the position of the particle that has reached the local optimal fitness;

[0023] S44: Introducing the inertia weight factor w d , α1 and α2, each particle is based on P and speed v i Adjust the position as shown in the following formula:

[0024]

[0025] in, and are the speeds of the ith particle at the tth and t-1th iterations, respectively. is the t-1th local optimum of the i-th particle, P t-1 is the t-1th global optimal value, is the position of the ith particle at the t-1th iteration, rand1 and rand2 are random numbers uniformly distributed between 0 and 1, and w d is the fine-tuning weight index, α1 and α2 are the weight ratios;

[0026] S45: Determine whether the termination condition is met. If so, end the iteration and output the cache state C represented by the particle with the best fitness value. Otherwise, update the speed and position parameters of the particle and return to S41.

[0027] Furthermore, the S5 specifically includes the following process:

[0028] Satellite will cache the base version vid corresponding to content i according to the best cache decision i,1 The content is unloaded to the ground server, which then obtains multiple versions through computational transcoding. The ground server and satellite respectively send the cached content to the users who request the corresponding content.

[0029] The beneficial effects of the present invention are:

[0030] Using the improved binary particle swarm optimization content caching algorithm, a low-orbit satellite Internet satellite-ground collaborative content distribution architecture service caching method is proposed. This method takes into account the cache capacity of the ground server, the size of the content and the number of requests, and dynamically adjusts the cache state of the ground server to ensure that the cache hit rate of the ground server is maximized during the distribution process, thereby minimizing the delay of the entire distribution process. At the same time, the EBPSO algorithm has the advantages of no relaxation, fast convergence and avoiding falling into local optimality, which helps to obtain the optimal caching decision. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 Schematic diagram of the content distribution architecture model based on the satellite-ground collaborative network.

[0032] Figure 2 It is a schematic diagram of the overall process of the present invention.

[0033] Figure 3 Schematic diagram of the overall process of the improved binary particle swarm optimization (EBPSO) algorithm. DETAILED DESCRIPTION

[0034] The technical solution will be described clearly and completely below in conjunction with the embodiments of the present invention. It should be noted that the described embodiments are only a part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without creative work belong to the protection scope of the present invention.

[0035] like Figure 1The figure shows a content distribution architecture model based on a satellite-ground collaborative network, including satellites, ground servers and users. Satellites and ground servers have caching and content distribution functions, and ground servers also have computing transcoding functions. Among them, the satellite caches all content request information in the network, the ground server only caches the basic version of the content request information, and the content request information is only transcoded from the basic version to other versions, and the other versions are not converted to each other. The content request information collected by the satellite includes the content numbers requested by all users, and counts the number of requests for each number.

[0036] Figure 2 The overall process diagram of the present invention is given. The satellite collects user request information, models the cache decision of the ground server as a "01" knapsack problem, uses the improved binary particle swarm optimization (EBPSO) algorithm to find the optimal cache decision, and unloads the content to the ground server according to the decision. The two work together to complete the content distribution. Specifically, it includes the following steps:

[0037] S1: Establish a content distribution architecture model based on a satellite-ground collaborative network, including low-orbit satellites and ground servers; among them, both low-orbit satellites and ground servers have caching and distribution functions, and ground servers have computing functions and can perform content version conversion.

[0038] S2: The satellite collects content request information from each user in the network and makes statistics.

[0039] S3: Establish a cache optimization model, that is, minimize the delay of the distribution process, convert the goal of minimizing the delay into maximizing the cache hit rate. The cache hit rate refers to the proportion of the content cached in the ground server to the content requested by the user, and establish the cache optimization model as a "01" knapsack problem; specifically, the following steps are included:

[0040] S31: Establish a cache optimization model as the "01" backpack model. The cache capacity of the ground server is regarded as the backpack capacity, and the size of the content and the number of requests are regarded as the weight and value of the item. The goal of maximizing the cache hit rate needs to be achieved within the limited capacity.

[0041] S32: Divide the users into multiple multicast groups G={g 1,1 ,…,g V,E}; Obtain the number of users in each multicast group by counting content request information The formula for calculating the total number of users requesting is: Among them, g i,j A multicast group consisting of users who requested version j of content i; For multicast group g i,jThe number of users in the network, V is the number of content request information in the network, and E is the number of versions of the content request information;

[0042] S33: Add the number of requests for all versions of content i, and the calculation formula is: Where val i is the value of content i, and the value of each content is obtained: val=(val1,…,val V );

[0043] S34: Calculate the cache status of the ground server, C = (c1, ..., c V ), where c i ∈{0,1}, the value is 0, which means that the content i is not cached, and the value is 1, which means that i is cached; let the total value of the content val sum Maximum, val sum =C T ·val.

[0044] S4: Use the binary particle swarm optimization algorithm to solve the “01” knapsack problem and obtain the optimal caching decision for the ground server.

[0045] Figure 3 The improved binary particle swarm optimization (EBPSO) algorithm flow chart is given. First, the algorithm parameters are set, then the position and speed of the swarm particles are initialized, and the individual fitness values ​​of the particles in the swarm are calculated. Then, the position of the particle that has reached the local optimal fitness value is reinitialized, and the particle speed and position parameters are continuously updated iteratively before the termination condition is reached. Finally, the iteration is terminated after the condition is reached, and the optimal cache decision of the ground server is output. S4 specifically includes the following steps:

[0046] S41: In the EBPSO algorithm mechanism, a group of particles form a population, where each particle represents a cache state; initialize the position x of particle i i and speed v i ; The total value of the content val calculated using the cache state represented by each particle sum As the fitness of each particle, the position P of the particle with the current best fitness value and the historical best position of the i-th particle are recorded.

[0047] S42: For particles that violate the storage capacity constraint of the ground server, the fitness value is recorded as 0. If the fitness of any particle in the particle swarm is greater than the current optimal fitness value, P is replaced by the fitness of the corresponding particle;

[0048] S43: traverse each particle in the particle swarm and reinitialize the position of the particle that has reached the local optimal fitness;

[0049] S44: Introducing the inertia weight factor w d , α1 and α2, each particle is based on P and speed v i Adjust the position as shown in the following formula:

[0050]

[0051] in, and are the speeds of the ith particle at the tth and t-1th iterations, respectively. is the t-1th local optimum of the i-th particle, P t-1 is the t-1th global optimal value, is the position of the ith particle at the t-1th iteration, rand1 and rand2 are random numbers uniformly distributed between 0 and 1, and w d is the fine-tuning weight index, α1 and α2 are the weight ratios;

[0052] S45: Determine whether the termination condition is met. If so, end the iteration and output the cache state C represented by the particle with the best fitness value. Otherwise, update the speed and position parameters of the particle and return to S41.

[0053] S5: The satellite caches the base version vid corresponding to content i according to the best cache decision i,1 The content is unloaded to the ground server, which then obtains multiple versions through computational transcoding. The ground server and satellite respectively send the cached content to the users who request the corresponding content.

[0054] The embodiments of the present invention are described above, but the present invention is not limited to the above-mentioned specific implementation modes. The above-mentioned specific implementation modes are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A low-orbit satellite Internet satellite-ground collaborative content distribution architecture service caching method, characterized in that: The steps include: S1: Establish a content distribution architecture model based on a satellite-ground collaborative network, including low-orbit satellites and ground servers; both low-orbit satellites and ground servers have caching and distribution functions, and ground servers have computing functions and can perform content version conversion; S2: The satellite collects content request information from each user in the network and makes statistics; S3: Establish a cache optimization model, that is, minimize the delay of the distribution process, convert the goal of minimizing the delay into maximizing the cache hit rate. The cache hit rate refers to the proportion of the content cached in the ground server to the content requested by the user, and establish the cache optimization model as a "01" knapsack problem; S4: Use binary particle swarm optimization algorithm to solve the "01" knapsack problem and obtain the optimal cache decision for the ground server; S5: The satellite offloads part of the statistical information to the ground server cache based on the best cache decision. The content request information cached by the ground server is distributed by the ground server, and other requests are distributed directly by the satellite.

2. The method for caching content distribution architecture service in low-orbit satellite Internet space-ground collaboration according to claim 1 is characterized in that: The satellite caches all content request information in the network, and the ground server only caches the basic version of the content request information. The content request information is only transcoded from the basic version into other versions, and the other versions are not converted to each other.

3. The method for caching low-orbit satellite internet satellite-ground coordinated content distribution architecture services according to claim 1, characterized in that: The content request information collected by the satellite includes the content numbers requested by all users, and counts the number of requests for each number.

4. The method for caching content distribution architecture service in low-orbit satellite Internet space-ground collaboration according to claim 1 is characterized in that: The S3 specifically includes the following steps: S31: Establish a cache optimization model as the "01" backpack model. The cache capacity of the ground server is regarded as the backpack capacity, and the size of the content and the number of requests are regarded as the weight and value of the item. The goal of maximizing the cache hit rate needs to be achieved within the limited capacity. S32: Divide the users into multiple multicast groups G={g 1,1 ,…,g V,E }; Obtain the number of users in each multicast group by counting content request information The formula for calculating the total number of users requesting is: Among them, g i,j A multicast group consisting of users who requested version j of content i; For multicast group g i,j The number of users in the network, V is the number of content request information in the network, and E is the number of versions of the content request information; S33: Add the number of requests for all versions of content i, and the calculation formula is: Where val i is the value of content i, and the value of each content is obtained: val=(val1,…,val V ); S34: Calculate the cache status of the ground server, C = (c1, ..., c V ), where c i ∈{0,1}, the value is 0, which means that the content i is not cached, and the value is 1, which means that i is cached; let the total value of the content val sum Maximum, val sum =C T ·val.

5. The low-orbit satellite Internet satellite-ground coordinated content distribution architecture service caching method according to claim 4 is characterized in that: The S4 specifically includes the following steps: S41: In the EBPSO algorithm mechanism, a group of particles form a population, where each particle represents a cache state; initialize the position x of particle i i and speed v i ; The total value of the content val calculated using the cache state represented by each particle sum As the fitness of each particle, the position P of the particle with the current best fitness value and the historical best position of the i-th particle are recorded. S42: For particles that violate the storage capacity constraint of the ground server, the fitness value is recorded as 0. If the fitness of any particle in the particle swarm is greater than the current optimal fitness value, P is replaced by the fitness of the corresponding particle; S43: traverse each particle in the particle swarm and reinitialize the position of the particle that has reached the local optimal fitness; S44: Introducing the inertia weight factor w d , α1 and α2, each particle is based on P and speed v i Adjust the position as shown in the following formula: in, and are the speeds of the ith particle at the tth and t-1th iterations, respectively. is the t-1th local optimum of the i-th particle, P t-1 is the t-1th global optimal value, is the position of the ith particle at the t-1th iteration, rand1 and rand2 are random numbers uniformly distributed between 0 and 1, and w d is the fine-tuning weight index, α1 and α2 are the weight ratios; S45: Determine whether the termination condition is met. If so, end the iteration and output the cache state C represented by the particle with the best fitness value. Otherwise, update the speed and position parameters of the particle and return to S41.

6. The low-orbit satellite Internet satellite-ground coordinated content distribution architecture service caching method according to claim 1, characterized in that: The S5 specifically includes the following process: Satellite will use the best cache decision to send the base version vid corresponding to content i i,1 The content is unloaded to the ground server, which then obtains multiple versions through computational transcoding. The ground server and satellite respectively send the cached content to the users who request the corresponding content.

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