A distributed collaborative caching method for unreliable edge environments in new power systems

Abstract

The present invention belongs to the field of power wireless communication technology, and specifically relates to a distributed collaborative caching method for an unreliable edge environment in a new power system. The method comprises: constructing a distributed collaborative edge caching system model; constructing a cache hit rate maximization problem based on the distributed collaborative edge caching system model; solving the cache hit rate maximization problem to obtain the number of file blocks cached on different types of edge servers; and obtaining an optimal caching solution for each file based on the number of file blocks cached by the edge server. The present invention can improve the utilization rate of limited edge resources while providing a cost-effective edge caching service.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric power wireless communication, and in particular relates to a distributed collaborative caching method for an unreliable edge environment in a new type of electric power system. Background Art

[0002] As the construction of new power systems continues to advance, the number of digital and intelligent devices within them is rapidly increasing, posing severe challenges to the accessibility, flexibility, and reliability of power communication networks. Currently, to address the congestion in power communication networks caused by the massive influx of heterogeneous terminals connecting to the power system, caching popular files at the network edge has become a common approach to addressing network congestion, reducing the number of requests to content providers during peak hours, and lowering service latency to support real-time applications. Power system operations generate a large amount of structured, semi-structured, and unstructured smart distribution data, which is received by the grid data management center and stored in a central cloud. Users access this distribution data from the central cloud through various smart terminal devices and edge clouds. Excessive user access can place significant pressure on the central cloud. Therefore, the central cloud can pre-store various smart distribution data on edge servers for easier access. However, such reliable edge resources are highly limited and expensive.

[0003] Therefore, popular files can be cached on unreliable edge resources to improve cost efficiency. Two types of unreliable resources are considered here. The first is idle resources, or resources that are reserved for other applications but are not fully utilized. This is unreliable because the edge server may erase the cached content to serve higher priority applications. Cached files on unused resources of the edge server can be erased when needed to minimize side effects. In addition, caching on resources that are not used by the edge server is much cheaper than caching on reserved cache resources. The second type of unreliable resources are edge servers owned by various organizations, which may fail or leave the system for various reasons. The cost of using this type of edge servers is also lower. Using unreliable edge servers to cache popular files in the power grid can not only improve the utilization of limited edge resources, but also provide cost-effective edge caching services. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention proposes a distributed collaborative caching method for unreliable edge environments in new power systems, which includes:

[0005] S1: Build a distributed collaborative edge caching system model;

[0006] S2: Constructing the problem of maximizing cache hit rate based on the distributed collaborative edge cache system model;

[0007] S3: Solve the problem of maximizing cache hit rate and obtain the number of file blocks cached on different types of edge servers;

[0008] S4: Obtain the optimal caching solution for each file block based on the number of file blocks cached by the edge server.

[0009] Preferably, the distributed collaborative edge cache system model includes: multiple N available edge servers of different types, with servers of the same type having the same capacity and reliability parameters; users requesting content at peak times with an average request rate h per time slot. i The file i is encoded into no less than K file blocks, and the file i can be reconstructed from any K file blocks; the server of type v can cache at most σ v file blocks, the reliability parameter of the server of type v is q v , the number of file blocks cached by file i on server type v is y i,v ∈{0,1,2....N v}, N v Indicates the number of servers of type v.

[0010] Preferably, the process of constructing the problem of maximizing cache hit rate includes:

[0011] Get the server's cache availability probability f n,t and the set of edge servers φ that cache the file blocks of file i i (x i ),use represents a set consisting of a subset of edge servers whose number of cached file blocks of file i is not less than K. For each Calculate the probability P that the cache on M is available and the caches of other edge servers are unavailable i,M,t ;

[0012] According to P i,M,t Calculate the probability that file i is reconstructed;

[0013] Calculate the time-accumulated availability of file i according to the probability that file i is reconstructed;

[0014] The problem of maximizing cache hit rate is constructed based on the time-accumulated availability of file i and the average request rate of users in each time slot during peak hours.

[0015] Furthermore, we calculate the probability P i,M,t The formula is:

[0016]

[0017] Among them, f n,trepresents the cache availability probability of edge server n at time slot t, φ i (x i ) represents the set of edge servers that cache the file blocks of file i.

[0018] Furthermore, the formula for calculating the probability of file i being reconstructed is:

[0019]

[0020] Among them, a i,t represents the probability that file i is reconstructed at time slot t.

[0021] Furthermore, the formula for calculating the time-accumulated availability of file i is:

[0022]

[0023] Among them, a i represents the time-cumulative availability of file i, a i,t represents the probability that file i is reconstructed at time slot t, and T represents the total number of time slots.

[0024] Furthermore, the problem of maximizing cache hit rate is expressed as:

[0025]

[0026] Among them, h i represents the average request rate of users requesting file i during peak hours, a i represents the cumulative time availability of file i, I represents the file set, x i,n Indicates whether file i is cached on server n, s n represents the capacity of server n, and N represents the number of edge servers.

[0027] Preferably, the process of obtaining the optimal caching solution for each file block according to the mapping of the number of file blocks cached by the edge server includes:

[0028] definition:

[0029]

[0030] If S i,v <E i,v , then for Set x i,n = 1, the x of other servers of type v i,n =0;

[0031] If S i,v >E i,v , then for Set xi,n = 1, the x of other servers of type v i,n =0;

[0032] If S i,v =E i,v , then for all servers of type v, set x i,n =1;

[0033] If S i,v =E i,v And y i,v = 0, then for all servers of type v, set x i,n =0;

[0034] Among them, S i,v y represents the remainder of the number of edge servers of type v that have cached the first i-1 files divided by the total number of edge servers of type v. j,v N represents the number of file blocks of file j cached on edge servers of type v. v Indicates the number of edge servers of type v, E i,v represents the remainder of the number of edge servers of type v that have cached the first i files divided by the total number of edge servers of type v. Indicates the sth type of v i,v +1 edge server, Indicates the Eth of type v i,v edge servers, x i,n Indicates whether file i is cached on edge server n.

[0035] The present invention utilizes unreliable edge server resources for edge caching, improving overall resource utilization. By implementing distributed collaborative caching during off-peak hours, it can address network congestion issues during peak periods in smart grids and reduce overall service latency. This invention improves the utilization of limited edge resources while providing cost-effective edge caching services. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flow chart of the distributed collaborative caching method for unreliable edge environments in new power systems in the present invention;

[0037] Figure 2 This is an example diagram of the DEC system cache file in the present invention. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] This paper proposes a distributed collaborative caching method for unreliable edge environments in new power systems. Figure 1 As shown, the method includes:

[0040] S1: Build a distributed collaborative edge caching system model.

[0041] In a distributed collaborative edge caching (DEC) system with unreliable cache resources, there are N available edge servers of different types, all of which have the same capacity and reliability parameters. Popular files are downloaded and encoded into multiple packets, each of which is referred to as a file chunk. Each chunk of each file is cached on edge servers during off-peak hours to reduce file retrieval latency, core network congestion, and the load on remote servers during peak hours. Assuming that the peak hours of a day can be divided into T time slots of equal length, T∈[T], {1,2,3...T}, since edge servers are located near users, the cost of downloading data from edge servers in this system is much lower in terms of latency and throughput than downloading data from remote content providers via the core network.

[0042] The set of popular files is represented by I∈[I], {1, 2, 3...I}. Users who request content from the DEC system will request content at an average request rate h per time slot during peak hours. i For each request for file i, assume that all files in I are of the same size, say Q bits. Each file is divided into K packets of equal length and encoded into K or more blocks using an MDS code (e.g., RS code) (some bits of data in the packet are deleted or replaced, and the replaced bits are re-encoded into blocks), each of which has a length of Q / K. Using the MDS code, the original file i can be regenerated from any of its K blocks, where K is a pre-specified and known parameter. K is fixed in order to facilitate the adjustment of the code rate for different files, i.e., the number of file blocks. The blocks of the file are stored on different edge servers.

[0043] The set of edge servers is represented by N = {1, 2..., N}. Assume that N>K and the size of server n is S n M / K position, equivalent to S n That is, server n can cache at most S n file blocks.

[0044] Considering that there are many types of edge servers with unreliable resources, we assume that there are V types of edge servers, and the same type of server has the same capacity s n and reliability parameter p n , where V is much smaller than N. Assume N v represents the number of servers of type v∈V. A server of type v can cache at most σ v file blocks, the reliability parameter of the server of type v is q v , the number of file blocks cached by file i on server type v is y i,v ∈{0,1,2....N v}, N v Indicates the number of servers of type v. Use Y i To represent the set {y i,v} v∈[V] ,and

[0045] S2: Construct the problem of maximizing cache hit rate based on the distributed collaborative edge cache system model.

[0046] If server n wants to contribute its cache capacity to DEC for applications with higher priority, or fails for some reason, it will directly erase the cached blocks or leave the system. If, for various reasons, the user cannot download the data block from the edge server within a time below a given threshold, it is also considered an edge server failure. For server n∈N, get the server's cache availability probability f n,t , that is, for each block cached on server n, the probability of being available in time slot t is f n,t In particular, for any server n∈N, the availability of its cache is modeled as a geometric distribution, where f n,t =(p n ) t ,p n is the reliability parameter of server n, which is equal to the probability that the cache on server n is available at time slot t.

[0047] For example, Figure 2As shown, there are five files of the same length, marked with different colors. Each data packet is divided into K = 3 data packets, encoded into no fewer than 3 blocks, and cached on edge servers 1, 2, 3, and 4. A file can be regenerated from any three of its encoded blocks. Server 1 fails at time slot t1. Users can regenerate any of the five files by downloading the encoded blocks from other edge servers before the failure of server 1. When time slot t2 > t1, users can regenerate the red, yellow, gold, and orange files by encoding blocks on available edge servers 2, 3, and 4. The purple file cannot be recovered by encoding blocks on available edge servers and can only be obtained from the content source.

[0048] Get the set of edge servers φ that cache the file blocks of file i i (x i ),use represents the set consisting of the subset of edge servers whose number of cached file blocks of file i is not less than K, that is, For each Calculate the probability P that the cache on M is available and the caches of other edge servers are unavailable i,M,t :

[0049]

[0050] Among them, f n,t represents the cache availability probability of edge server n at time slot t, φ i (x i ) represents the set of edge servers that cache the file blocks of file i.

[0051] According to P i,M,t Calculate the probability that file i is reconstructed, that is, the availability of file i in the time slot t∈T i,t :

[0052]

[0053] Calculate the time-accumulated availability a of file i according to the probability that file i is reconstructed i :

[0054]

[0055] The problem of maximizing cache hit rate is constructed based on the time-accumulated availability of file i and the average request rate of users in each time slot during peak hours. Specifically:

[0056] If the cached encoding blocks on the edge server are less than k, the user must download the data through the congested core network, which is very time-consuming. Therefore, the goal of the system is to maximize the cache hit rate, that is, the expected size of the file downloaded from the edge server during peak hours is as follows:

[0057]

[0058] Constraint: The number of file blocks cached on server n is not greater than the size of the server, that is,

[0059] Among them, x i,,n ∈{0,1} is a binary variable indicating whether a file block of file i is placed on edge server n. represents a set of variables related to file i, represents the set of all decision variables. x contains information about the number and location of blocks for each file. In this invention, the blocks in each server are independent, that is, some blocks in a server may fail (be erased), while other blocks in the same server may be available.

[0060] Therefore, the problem of maximizing cache hit rate is expressed as:

[0061]

[0062] S3: Solve the problem of maximizing the cache hit rate and obtain the number of file blocks cached on different types of edge servers.

[0063] For a server of type v, there is p n =q v , f n,t =(p n ) t =(q v ) t ; For a given y i , the expected availability of file i at time slot t is expressed as a i,t =A t (y i ), if y i <K,a i,t = 0. If y i >K, then Among them, f v,t represents the cache availability probability of type v server in time slot t, when y i,v <j v When it represents the total number of cache blocks of type v server, j=(j1,j2......j V ), j v Indicates the total number of cached file blocks of server type v.

[0064] Therefore, the objective function of maximizing the cache hit rate problem can be written as:

[0065]

[0066] Then the problem of maximizing cache hit rate can be rewritten as:

[0067]

[0068] Solve the above problem:

[0069] When there are only the first j files, and the total capacity of the edge server of type v∈[V] is z v In the case of file blocks, w(z1,z2...z n ,j) represents the current optimal target value. In addition, r(z1,z2...z n ,j) The total capacity of the edge server with only the first j files and type v is z v For the convenience of representation, the present invention uses w(z,j) and r(z,j) as w(z1,z2...z n ,j) and r(z1,z2...z n ,j) abbreviation, definition

[0070] Next, design the initialization and dynamicization of w(z,j) and r(z,j):

[0071] First, when j=1,

[0072]

[0073] For j∈[2,3...I], z v ={0,1...σ v N v}, v∈V, w(z,j) and r(z,j) are calculated as follows:

[0074]

[0075] First, initialize w(z,1) and r(z,1) through the formula;

[0076] Then, w(z,j) and r(z,j) are iteratively updated using the above dynamic update formula to obtain the local optimal choice.

[0077] Finally, by backtracking the local optimal decision recorded in r(z,j), the optimal placement solution Y is obtained. i = r(z,i), which is the number of file blocks cached on different types of edge servers.

[0078] S4: Obtain the optimal caching solution for each file block based on the number of file blocks cached by the edge server.

[0079] A mapping scheme is designed here to map the optimal solution of the placement scheme to the optimal solution of the original problem, that is, to obtain the optimal caching solution for each file. Specifically:

[0080] For i∈[I],v∈V, define:

[0081]

[0082] If S i,v <E i,v , then for Set x i,n = 1, the x of other servers of type v i,n =0;

[0083] If S i,v >E i,v , then for Set x i,n = 1, the x of other servers of type v i,n =0;

[0084] If S i,v =E i,v , then for all servers of type v, set x i,n =1;

[0085] If S i,v =E i,v And y i,v = 0, then for all servers of type v, set x i,n =0;

[0086] Among them, S i,v y represents the remainder of the number of edge servers of type v that have cached the first i-1 files divided by the total number of edge servers of type v. j,v N represents the number of file blocks of file j cached on edge servers of type v. v Indicates the number of edge servers of type v, E i,v represents the remainder of the number of edge servers of type v that have cached the first i files divided by the total number of edge servers of type v. Indicates the sth type of v i,v +1 edge server, Indicates the Eth of type v i,v edge servers, x i,n Indicates whether file i is cached on edge server n.

[0087] After obtaining the optimal caching solution for each file, the system executes the optimal caching solution, which can not only improve the utilization of limited edge resources but also provide cost-effective edge caching services.

[0088] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation plans of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made to the present invention within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed collaborative caching method for unreliable edge environments in new power systems, characterized by: include: S1: Build a distributed collaborative edge caching system model; S2: Constructing the problem of maximizing cache hit rate based on the distributed collaborative edge cache system model; The process of formulating the problem of maximizing cache hit ratio involves: Get the server's cache availability probability f n,t and the set of edge servers φ that cache the file blocks of file i i (x i ),use represents a set consisting of a subset of edge servers whose number of cached file blocks of file i is not less than K. For each Calculate the probability P that the cache on M is available and the caches of other edge servers are unavailable i,M,t ; Calculate the probability P i,M,t The formula is: Among them, f n,t represents the cache availability probability of edge server n at time slot t, φ i (x i ) represents the set of edge servers that cache the file blocks of file i; According to P i,M,t Calculate the probability that file i is reconstructed; the formula for calculating the probability that file i is reconstructed is: Among them, a i,t represents the probability that file i is reconstructed at time slot t; The time-accumulated availability of file i is calculated based on the probability that file i is reconstructed. The formula for calculating the time-accumulated availability of file i is: Among them, a i represents the time-cumulative availability of file i, a i,t represents the probability that file i is reconstructed at time slot t, and T represents the total number of time slots; The problem of maximizing cache hit rate is constructed based on the time-accumulated availability of file i and the average request rate of users in each time slot during peak hours. The problem of maximizing cache hit rate is expressed as: Among them, h i represents the average request rate of users requesting file i during peak hours, A(Y i )=a i represents the time-cumulative availability of file i, Y i represents the set of file blocks of file i cached on different servers, I represents the file set, y i,v Indicates the number of file blocks of file i cached on type v server, z v Indicates the total capacity of v-type servers, where V represents the total number of server types; S3: Solve the problem of maximizing cache hit rate and obtain the number of file blocks cached on different types of edge servers; S4: Obtain the optimal caching solution for each file based on the number of cached file blocks on the edge server.

2. A distributed collaborative caching method for unreliable edge environments in new power systems according to claim 1, characterized in that: The distributed collaborative edge cache system model includes: multiple N available edge servers of different types, with the same capacity and reliability parameters for servers of the same type; users requesting content at peak times with an average request rate h per time slot. i The file i is encoded into no less than K file blocks, and the file i can be reconstructed from any K file blocks; the server of type v can cache at most σ v file blocks, the reliability parameter of the server of type v is q v , the number of file blocks cached by file i on server type v is y i,v ∈{0,1,2....N v }, N v Indicates the number of servers of type v.

3. The distributed collaborative caching method for unreliable edge environments in new power systems according to claim 1, characterized in that: The process of mapping the number of file blocks cached by the edge server to obtain the optimal caching solution for each file block includes: definition: If S i,v <E i,v , then for Set x i,n = 1, the x of other servers of type v i,n =0; If S i,v >E i,v , then for Set x i,n = 1, the x of other servers of type v i,n =0; If S i,v =E i,v , then for all servers of type v, set x i,n =1; If S i,v =E i,v And y i,v = 0, then for all servers of type v, set x i,n =0; Among them, S i,v y represents the remainder of the number of edge servers of type v that have cached the first i-1 files divided by the total number of edge servers of type v. j,v N represents the number of file blocks of file j cached on edge servers of type v. v Indicates the number of edge servers of type v, E i,v The remainder of the number of edge servers of type v that have cached the first i files divided by the total number of edge servers of type v. Indicates the Sth type of v i,v +1 edge server, Indicates the Eth of type v i,v edge servers, x i,n Indicates whether file i is cached on edge server n.

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