Cloud infrastructure asset management system based on cloud computing architecture

Abstract

The present invention discloses a cloud infrastructure asset management system based on a cloud computing architecture. The present invention relates to the technical field of cloud computing architecture, and includes a cloud infrastructure asset storage end: responsible for sharding cloud infrastructure assets to obtain a number of data blocks; then calculating the hash value of each data block, and storing and backing up the data blocks in combination with the position number of the data block in the asset and the load score of each server; a cloud infrastructure asset monitoring and analysis end: continuously monitoring cloud infrastructure assets stored in the cloud computing architecture storage end, obtaining their storage status, and identifying risky assets; a cloud infrastructure asset optimization processing end: providing optimization measures and security protection processing based on the risky assets identified by the monitoring and analysis end; at the same time, updating and optimizing the data blocks to ensure the security and integrity of the data.

Description

Technical Field

[0001] The present invention belongs to the technical field of cloud computing architecture, and specifically relates to a cloud infrastructure asset management system based on cloud computing architecture. Background Art

[0002] Cloud computing architecture technology is an architectural model that provides computing resources or management resources based on Internet technology and actual needs. Its central idea is to dynamically allocate and adjust the user's storage resource needs or computing resource needs by combining the total resources of the actual server or system itself.

[0003] Most existing technologies for managing cloud infrastructure assets find it difficult to achieve refined resource allocation based on actual user needs and the performance of the system itself, resulting in resource waste or shortage. Existing resource scheduling algorithms find it difficult to effectively balance the load of each server, causing some server resources to be idle while other servers face resource shortages. In addition, most existing systems lack automated monitoring, early warning, and optimization functions, and are unable to detect and resolve potential problems in a timely manner. As the amount of data and user requests increase, the performance of the system may be limited. In order to solve the above problems, the present invention proposes a cloud infrastructure asset management system based on cloud computing architecture. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a cloud infrastructure asset management system based on cloud computing architecture, which solves the problems of imbalance in cloud infrastructure asset management in the existing technology, such as idle server resources, excessive server load, and inability to automatically monitor and optimize system resource allocation.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A cloud infrastructure asset management system based on cloud computing architecture, which includes:

[0007] The cloud infrastructure asset storage end shards the cloud infrastructure assets into several data blocks, calculates the hash value of each data block, combines the location number of the data block with the storage location number of the next data block to form a numbered hash value, and then calculates the server load score and the inverse ratio of the load score based on the performance parameters of each server. The data blocks are then stored and backed up based on the inverse ratio of the server load score.

[0008] The cloud infrastructure asset monitoring and analysis terminal continuously monitors stored cloud infrastructure assets, obtains the storage status of cloud infrastructure assets, and identifies risky assets.

[0009] The cloud infrastructure asset optimization processing end provides optimization measures and missing data processing based on the identified risk assets; and updates and optimizes data blocks;

[0010] Cloud infrastructure asset terminals coordinate data transmission between terminals, send operation commands, and ensure the normal operation of the system.

[0011] As a further solution of the present invention, the cloud infrastructure asset storage terminal calculates the load score of the server based on the performance parameters of each server, and the server performance parameters include CPU usage, memory usage, network latency, and disk usage.

[0012] As a further solution of the present invention, the cloud infrastructure asset storage terminal stores and backs up data blocks according to the load score of each server in the following specific manner:

[0013] Get the cloud infrastructure asset Q to be stored in the cloud infrastructure asset storage terminal, split Q into data slices with the preset data size q, and obtain the data block sequence Q1, Q2, ..., Q n , where the value of q is determined by the system operator, n is a positive integer counting index, indicating the total number of data blocks;

[0014] Then calculate the hash value corresponding to each data block, and together with the position number of the data block in the data block sequence and the storage position number of the next data block, use them as the unique identifier of the data block and record them as the numbered hash value;

[0015] Sort the numbered hash values ​​in the order of the data block sequence to get the numbered hash value sequence H1, H2, ..., H n ;

[0016] Calculate the load score of each server in the current system and perform storage and backup operations on data blocks.

[0017] As a further solution of the present invention, the cloud computing architecture storage terminal calculates the load score of each server in the current system and performs storage and backup operations on data blocks in the following specific manner:

[0018] Based on the servers W1, W2, ..., W in the current system m , get any server W i CPU usage of A i Memory usage B i 、Network delay C i and disk usage D i , and perform normalization, and recalculate the normalized CPU usage, memory usage, network latency, and disk usage to A i 、Bi 、C i 、D i The original value of is overwritten, where m is a positive integer counting index, indicating the total number of servers, i is a positive integer counting index, 0 <i≤m、W i Represents any server;

[0019] According to the load rating calculation formula:

[0020] SN i =α·A i +β·B i +γ·C i +δ·D i

[0021] Get server W i Load rating SN i , α, β, γ, δ are all weight coefficients, and their values ​​are determined by the system operator, α+β+γ+δ=1;

[0022] Calculate the load scores of all servers in this system and sort them from small to large to get the load score sequence SN′1, SN′2, ..., SN′ m ;

[0023] Then use the load score sequence to sort the servers and get the server sequence W′1, W′2, ..., W′ m ;

[0024] Based on the determined load scoring sequence SN′1, SN′2, ..., SN′ m , use the normalization algorithm to normalize the m load scores to the range of 0-1, and obtain m normalized load scores. Use the value 1 to subtract the m normalized load scores, and then normalize them again. Record them as the inverse of the load scores of the m servers, and sort them according to the order of the server sequence to obtain the load score inverse sequence E1, E2, ..., E m ;

[0025] The data blocks are stored with the inverse ratio of the load score as the storage weight.

[0026] As a further solution of the present invention, the specific manner in which the cloud computing architecture storage end stores data blocks with the inverse ratio of the load score as the storage weight is as follows:

[0027] Based on the determined load score inverse sequence E1, E2, ..., E m , for the data block sequence Q1,Q2,...,Q n And the corresponding numbered hash value sequence H1,H2,...,H n , perform segmentation;

[0028] For server W′1, take E1 as the storage weight, from Q1, Q2, ..., Q n In the sequence, obtain the data blocks of E1 ratio and the data blocks from H1, H2, ..., H n Sequentially obtain the numbered hash values ​​of the E1 ratios and store them in W′1;

[0029] This process continues in this way until all m servers are assigned data blocks and numbered hash values ​​in inverse proportion to their load scores.

[0030] As a further solution of the present invention, the specific method of storing data blocks in the cloud computing architecture storage end using the inverse ratio of the load score as the storage weight also includes:

[0031] For server W′1, take E1 as storage weight, from Q1, Q2, ..., Q n In the reverse order, obtain the data blocks of E1 ratio and the data blocks from H1, H2, ..., H n Obtain the numbered hash values ​​of the E1 ratio in reverse order and store them in W′1 as backup data;

[0032] This process continues in this way until all m servers are assigned data blocks and numbered hash values ​​in inverse proportion to their load scores.

[0033] As a further solution of the present invention, the cloud infrastructure asset monitoring and analysis terminal obtains the storage status of cloud infrastructure assets and identifies risky assets in the following specific manners:

[0034] Monitor access requests in real time and obtain access data block Q j Failed Server W i and the hash value H j , determine server W i The storage status of the current cloud infrastructure asset Q is abnormal, where j is a positive integer count index starting from 1 and the maximum value is n, Q j Represents any data block.

[0035] As a further solution of the present invention, the cloud infrastructure asset monitoring and analysis terminal further includes:

[0036] Obtain all data blocks of the abnormal cloud infrastructure asset Q and the corresponding numbered hash values ​​of the data blocks, extract the position number in the numbered hash value, and calculate the position number according to the server sequence W′1, W′2, ..., W′ m Arrange the position numbers in the order of the position numbers, determine whether the position numbers are continuous, and determine whether the total number of data blocks matches the total number of data blocks n that are divided. If there is a missing position number, it is regarded as the data block Q corresponding to the current position number. jIf it is missing, the cloud infrastructure asset Q is considered a risky asset.

[0037] As a further solution of the present invention, the cloud infrastructure asset optimization processing terminal provides optimization measures and missing data processing based on the identified risk assets in the following specific manner:

[0038] For access requests that fail and missing data blocks Q j When the missing data block Q is obtained j Server W i , extract the previous adjacent successfully accessed data block Q j-1 The hash value of the number, intercept Q j-1 The position number of the hash value is extended one bit backward to obtain the data block Q j The position number in the hash value is denoted as P j ;

[0039] The location number corresponding to the cloud infrastructure asset Q in the extracted backup data is P j Backup data block Q′ j , provided to the access request, and the data block Q is restored in the first time j Normal access;

[0040] Then use the backup data block Q′ j For the currently missing data block Q j Fill in, using the backup data block Q′ j The hash value of the missing data block Q j The hash value of the number is filled.

[0041] As a further solution of the present invention, the cloud infrastructure asset optimization processing terminal performs the following specific operations on updating and optimizing data blocks:

[0042] At a preset interval T, the load score of each server is obtained, and the inverse load score ratio is recalculated based on the load score. The inverse load score ratio is used as the storage ratio to redistribute the cloud infrastructure assets Q to each server for storage and backup.

[0043] Get the data block Q of the user's change operation in real time j , according to data block Q j The number of the hash value is used to find the corresponding backup data block Q' j ;

[0044] Using data block Q j Backup data block Q′ j To overwrite, use data block Q j The numbered hash value of the backup data block Q′ jThe number hash value is overwritten and the update operation is automatically triggered every preset time T. For any cloud infrastructure asset Q, the position number in the number hash value of any data block is first obtained, and the corresponding backup data block is found according to the position number;

[0045] Determine whether the hash value portion of the numbered hash value of the data block is the same as the hash value of the numbered hash value of the corresponding backup data block;

[0046] If they are the same, no processing will be done;

[0047] If they are not the same, the data block is overwritten with the backup data block, and the number hash value of the data block is overwritten with the number hash value of the backup data block.

[0048] Beneficial effects of the present invention:

[0049] (1) The present invention provides a cloud infrastructure asset management system based on a cloud computing architecture. By dividing a single cloud infrastructure asset into fixed data sizes and assigning corresponding numbered hash values, multiple data blocks are stored in multiple servers in the system. Through data sharding and hash value calculation, the integrity and recoverability of data can be ensured, and the efficiency of data management can be improved. In addition, data sharding and hash value calculation also improve data security, which can effectively reduce the losses caused by data tampering and data loss due to server attacks.

[0050] (2) The present invention provides a data redundancy mechanism that can effectively reduce the losses caused by data loss by backing up data. Secondly, by combining the hash values ​​of the numbers of the previous and next adjacent non-missing data blocks of the missing data block, the number of the missing data block can be calculated, and the backup server storing the lost data block can be quickly retrieved based on the number, thereby realizing rapid data recovery. This mechanism significantly improves the integrity of the data and the fault tolerance of the system, ensuring that users will not be seriously affected by data loss when accessing data;

[0051] (3) The present invention provides an algorithm for quantifying server performance, namely, a load scoring algorithm. By monitoring server performance in real time, dynamic data distribution adjustment and server load balancing are achieved. This is particularly important for the dynamic allocation and expansion of resources in a cloud computing environment, and improves storage efficiency and overall system utilization. By storing larger amounts of data on servers with lower loads and smaller amounts of data on servers with higher loads, data sharding is achieved to ensure data security while reducing the risk of service interruption due to server overload, thereby improving data availability and system fault tolerance, and ensuring the continuity and stability of user access. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The present invention will be further described below with reference to the accompanying drawings.

[0053] Figure 1 It is a schematic diagram of the structure of the cloud infrastructure asset management system based on cloud computing architecture according to the present invention;

[0054] Figure 2 Schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0055] 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 any creative efforts shall fall within the scope of protection of the present invention.

[0056] Example 1

[0057] Cloud infrastructure asset management system based on cloud computing architecture, such as Figure 1 As shown, this system includes:

[0058] The cloud infrastructure asset storage terminal is one of the core modules in this system. It is interconnected with multiple servers in this system, and each server contains a database that can store data blocks. This terminal is mainly responsible for data sharding, data block hash calculation, location number assignment, storage and backup of cloud infrastructure assets with a fixed data size. Specifically, this terminal divides cloud infrastructure assets into several independent but equal data blocks according to the preset data size. Each data block will be calculated with an independent hash value, which is combined with the position number of the data block in the data block sequence of the original cloud infrastructure asset and the storage location number of the next adjacent data block to form a numbered hash value.

[0059] The position numbers are continuous and unique;

[0060] The storage location number records the storage location of the next adjacent data block, which is empty before the storage operation and is supplemented after the data block is allocated to the corresponding server;

[0061] The numbered hash value is not only used to verify data integrity, but also for the rapid location and recovery of data blocks. By combining the numbered hash value of the data block and the load score of each server, it intelligently determines which server to store the data block on and performs redundant backup.

[0062] The calculation of the load score involves the performance parameters of the server, including CPU usage, memory usage, network latency, and disk usage. It should be explained here that the network latency is the average network latency within a preset time period T, and the CPU usage is the average CPU usage within a preset time period T. The preset time T is set by the system operator based on actual needs.

[0063] The cloud infrastructure asset monitoring and analysis end monitors the stored cloud infrastructure assets (data blocks in different servers) and analyzes the storage status of the data blocks. If the storage status of a data block is identified as abnormal, the cloud infrastructure asset will be marked as a risky asset and processed through the cloud infrastructure asset optimization processing end.

[0064] The cloud infrastructure asset optimization processing end provides optimization measures and security protection processing based on the risk assets identified by the cloud infrastructure asset monitoring and analysis end; for damaged data blocks, the location number of the missing data block is calculated through the data redundancy mechanism and the hash value of the number of the upper and lower data blocks of the missing data block, and the backup data block of the missing data block is found and restored.

[0065] The cloud infrastructure asset terminal is the coordination center of the entire system, responsible for coordinating data transmission between each end, sending operation commands, and ensuring the normal operation of the system; it should be able to meet the communication needs between each end. When a user accesses a data block, the cloud infrastructure asset terminal will locate the server storing the data block and the server of the next data block based on the location number of the data block and the storage location number of the next data block, and coordinate each end to complete data transmission and processing.

[0066] Example 2

[0067] This embodiment discloses a method for sharding cloud infrastructure assets and implementing data storage and data backup operations in combination with the performance of each server in the system. Figure 2 As shown, the specific steps include:

[0068] First, when a user wants to store a cloud infrastructure asset Q, the system operator first slices the cloud infrastructure asset Q into pieces according to the data size q preset by the system operator based on the number of servers equipped in the system. The resulting data block sequence is recorded as Q1, Q2, ..., Q n , where n is a positive integer count index, indicating the total number of data blocks after the data sharding operation.

[0069] For the data blocks determined after the data sharding operation, calculate the hash value corresponding to each data block; the position number of the data block in the cloud infrastructure asset Q; and the storage position number of the next data block in the data block sequence. The three results of the above calculations are combined into a string, recorded as the numbered hash value, which serves as the unique identifier of the data block.

[0070] The location number of each data block divided by each cloud infrastructure asset is different. The location number of any two data blocks divided by any two cloud infrastructure assets is different. The next data block storage location number includes the server location where the next data block is stored.

[0071] According to the data block sequence Q1,Q2,...,Q n Sort the hash value of each data block in the order of sorting, and get the hash value sequence, recorded as H1, H2, ..., H n , and by sorting the data blocks according to their corresponding position numbers in the hash value sequence, the n data blocks can be restored to the cloud infrastructure asset Q.

[0072] Get the servers W1, W2, ..., W in the current system m , the number of servers m, and calculate the current load score of each server. The calculation method is as follows:

[0073] First, from all servers in this system: W1, W2, ..., W m Get any server W i CPU usage of A i Memory usage B i 、Network delay C i and disk usage D i , and perform normalization, and recalculate the normalized CPU usage, memory usage, network latency, and disk usage to A i 、B i 、C i 、D i The original value of is overwritten, where m is a positive integer counting index, indicating the total number of servers equipped in this system, W i Where i is a positive integer counting index, i is greater than 0 and less than or equal to m, W i Indicates any server equipped in this system;

[0074] Build the server-associated load score calculation formula:

[0075] SN i =α·A i +β·B i+γ·C i +δ·D i

[0076] The server W is obtained by calculation i Load rating SN i , where α is the CPU usage A i The weight coefficient, β is the memory usage B i The weight coefficient, γ is the network delay C i The weight coefficient, δ is the disk usage D i The weight coefficients of α, β, γ, and δ are determined by the system operator based on actual needs, and α+β+γ+δ=1. The higher the load score, the worse the current server performance, and the lower the load score, the better the current server performance.

[0077] Repeat the above calculation for server W i Load rating SN i The load scores of all servers in the system are calculated by the steps of m , sort SN1, SN2, ..., SN according to the load rating value from small to large. m Sorting is performed to obtain the sorted load score sequence SN′1, SN′2, ..., SN′ m ;

[0078] Then sort all the servers in the system according to the sorting order of the load score sequence, and obtain the sorted server sequence W′1, W′2, ..., W′ m .

[0079] The determined load scoring sequence SN′1, SN′2, ..., SN′ m , using the normalization algorithm, normalize the load score of each server to the range of 0-1, and obtain the normalized load score between each server. Then, use the value 1 to subtract the normalized load score of each server. The final result of subtracting the normalized load score of each server from the value 1 is normalized again to obtain the inverse ratio of the load score of each server. And according to the server sequence W′1,W′2,...,W′ m The order of load score is recorded as the inverse sequence E1, E2, ..., E m .

[0080] According to the load score of each server, the inverse sequence E1, E2, ..., E m , for the data block sequence Q1,Q2,...,Q n And the numbered hash value sequence H1, H2, ..., H corresponding to the data blockn , for segmentation.

[0081] Taking server W′1 as an example, the inverse ratio E1 of the load score of server W′1 is used as the storage weight, and the data block sequence Q1, Q2, ..., Q n In the ascending order of the count index value, the data blocks of E1 ratio are obtained and the numbered hash value sequence H1, H2, ..., H n Obtain the E1 ratio number hash value corresponding to the E1 ratio data block in ascending order of the count index value;

[0082] The obtained E1-ratio data blocks and the E1-ratio serial number hash values ​​corresponding to the E1-ratio data blocks are stored in the server W′1 whose load score is inversely proportional to E1;

[0083] Repeat the above steps for the m servers in this system until the m servers are allocated data blocks in inverse proportion to their load scores and the numbered hash values ​​corresponding to the data blocks, and all n data blocks are allocated.

[0084] Then use E1 as the storage weight, from the data block sequence Q1, Q2, ..., Q n In the order of the count index value from large to small, the data blocks of E1 ratio and the numbered hash value sequence H1, H2, ..., H n Obtain the numbered hash values ​​of the E1 ratio corresponding to the data blocks of the E1 ratio in descending order of the count index values, store them in the server W′1 whose load score is inversely proportional to E1, and use the data blocks and the corresponding numbered hash values ​​stored this time as backup data. Repeat the above steps for the m servers in this system until all m servers are allocated data blocks of the corresponding load score inverse ratio and the numbered hash values ​​corresponding to the data blocks, and all n data blocks are allocated.

[0085] For example, there is a system including three servers numbered W1, W2, and W3 that need to store a cloud infrastructure asset. The cloud infrastructure asset is divided into 100 data blocks, each of which is 10 GB in size and denoted as Q1, Q2, ..., Q100 respectively.

[0086] For each data block in Q1, Q2, ..., Q100, determine the hash value, position number, and the next data block storage position number:

[0087] The hash value of data block Q1 is H1, the location number is P1, and the next data block storage location number is N1;

[0088] The hash value of data block Q2 is H2, the location number is P2, and the next data block storage location number is N2; ......

[0090] The hash value of data block Q100 is H100, the location number is P100, and the next data block storage location number is empty;

[0091] Combine the hash value, position number, and the next data block storage position number into a string as the unique identifier of the data block. The hash value of data block Q1 is H1P1N1, the hash value of data block Q2 is H2P2N2, ..., and the hash value of data block Q100 is H100P100.

[0092] All data blocks are sorted according to their numbered hash values ​​to obtain a sorted numbered hash value sequence: [H1P1N1, H2P2N2, ..., H100P100]. This sequence can be used to restore cloud infrastructure assets.

[0093] The system operator sets the weight coefficients: CPU usage weight is 0.4, memory usage weight is 0.3, network latency weight is 0.2, and disk usage weight is 0.1;

[0094] For each server:

[0095] Server W1: CPU usage 80%, memory usage 60%, network latency 50ms, disk usage 70%;

[0096] Server W2: CPU usage 70%, memory usage 50%, network latency 40ms, disk usage 60%;

[0097] Server W3: CPU usage 60%, memory usage 40%, network latency 30ms, disk usage 50%;

[0098] Calculate the load score for each server:

[0099] The load score of server W1 is 67, the load score of server W2 is 57, and the load score of server W3 is 47;

[0100] Sort the load scores from smallest to largest: server W3 (47), server W2 (57), server W1 (67), and then normalize the load scores to the range of 0-1 to obtain server W3 (0.27), server W2 (0.33), server W1 (0.4). Then use the value 1 to subtract each normalized load score to obtain server W3 (0.73), server W2 (0.67), server W1 (0.6). Perform normalization again to obtain the inverse sequence of load scores: server W3 (0.36), server W2 (0.34), server W1 (0.3);

[0101] According to the inverse proportion of the load score, the storage data blocks are allocated, and Q1 to Q36 and the corresponding numbered hash values ​​H1P1N1 to H36P36N36 are stored in the server W3, Q37 to Q70 and the corresponding numbered hash values ​​H37P37N37 to H70P70N70 are stored in the server W2, and Q71 to Q100 and the corresponding numbered hash values ​​H71P1N1 to H100P100 are stored in the server W1 as storage data;

[0102] Backup data blocks are allocated according to the inverse proportion of the load scores, Q65 to Q100 and the corresponding numbered hash values ​​H65P65N65 to H100P100 are stored in server W3, Q31 to Q64 and the corresponding numbered hash values ​​H31P31N31 to H64P64N64 are stored in server W2, and Q30 to Q1 and the corresponding numbered hash values ​​H30P30N30 to H1P1N1 are stored in server W1 as backup data.

[0103] Example 3

[0104] This embodiment discloses a method for obtaining the storage status of cloud infrastructure assets, identifying risky assets, providing optimization measures and security protection for risky assets, and updating cloud infrastructure assets. The method specifically includes the following steps:

[0105] Based on the cloud infrastructure asset monitoring and analysis terminal, all data blocks of any cloud infrastructure asset in each server of this system are monitored in real time, and the corresponding data block Q is obtained when the user fails to access the corresponding data block. j 、Number hash value H j And data block Q j Server W i , and determine the server W i The storage status of the current cloud infrastructure asset Q is abnormal;

[0106] The cloud infrastructure asset monitoring and analysis terminal then obtains the numbered hash values ​​corresponding to all data blocks of the cloud infrastructure asset Q, extracts the position numbers therein, and calculates the numbered hash values ​​in the order H1, H2, ..., H n Arrange the numbered hash values ​​of all data blocks in the sorting order;

[0107] And according to the order of the position numbers, determine whether the number hash values ​​are continuous and whether the number of position numbers matches the total number n of data blocks divided. The number hash value is bound to the data block. If the number hash value is missing, it is considered that the current data block is missing;

[0108] If there is a missing position number P j , then it is considered as the position number P j The corresponding number hash value H j If missing, the hash value is H j The corresponding data block Q j If missing, the cloud infrastructure asset Q is considered a risky asset, where j is a positive integer counting index starting from 1 and the maximum value is n. j Represents any data block, H j Represents data block Q j The corresponding number hash value, P j Represents data block Q j The position number in the corresponding hash value;

[0109] If the user accesses the corresponding data block Q j Failed and the data block Q j A missing data block Q is obtained. j Server W i , according to the previous adjacent data block Q that was successfully obtained j-1 The position number in the hash value is extended backward to deduce the data block Q that failed to be accessed. j The position number in the hash value of ;

[0110] Based on the determined missing data block Q j The position number and the proportion of the position number in the data block sequence of the cloud infrastructure asset Q are used to extract the missing data block Q. j Backup data blocks Q stored in other servers j ';

[0111] The data block Q j ′ is provided to the user to restore the data block Q j Normal access.

[0112] If multiple consecutive data blocks are lost, the position numbers of the consecutive missing data blocks can still be calculated by the position number in the hash value corresponding to the previous adjacent data block that has not been lost;

[0113] Then use Q′ j For the currently missing data block Q j Fill in, using the backup data block Q′ j The hash value of the missing data block Q j Fill in the numbered hash value to restore the missing data block Q j .

[0114] At every preset time T, the cloud infrastructure asset optimization processing terminal obtains the load scores of all servers in the system, recalculates the inverse ratio of the load scores between the servers, and uses the inverse ratio of the load scores as the storage ratio to redistribute the cloud infrastructure assets Q to the various servers in the system for storage and backup.

[0115] Based on the cloud infrastructure asset monitoring and analysis terminal, the data block of any cloud infrastructure asset in each server of this system is monitored in real time, and the data block Q of the change operation made by the user is obtained. j , and according to the data block Q j The numbered portion of the hash value is used to find the data block Q in the backup data. j Corresponding backup data block Q′ j , and use data block Q j Backup data block Q′ j To overwrite, use data block Q j The numbered hash value of the backup data block Q′ j Overwrite the number hash value;

[0116] In addition to the above operations, the cloud infrastructure asset optimization processing end also includes regular update operations, which automatically trigger update operations every preset time T. For any cloud infrastructure asset Q, it is determined whether the hash value in the number hash value of the data block and the hash value in the number hash value of the backup data block correspond to each other. If the data block and the backup data block fail to correspond, they are then checked in pairs according to the position numbers. Based on the successfully checked data block and the backup data block, the data block is overwritten to the backup data block, and the number hash value of the data block is overwritten to the number hash value of the backup data block.

[0117] Some of the data in the formulas described above are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0118] The above contents are merely examples and explanations of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in similar ways. As long as they do not deviate from the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

[0119] It is important to note that all user data collected in this application is collected with the user's consent and authorization. Furthermore, the use of user data is legal and compliant, and the use and processing of user data complies with the relevant laws, regulations, and standards of the relevant regions.

Claims

1. A cloud infrastructure asset management system based on cloud computing architecture, characterized by: The management system includes: The cloud infrastructure asset storage end shards the cloud infrastructure assets into several data blocks, calculates the hash value of each data block, combines the location number of the data block and the storage location number of the next data block to form a numbered hash value, and then calculates the server load score and the inverse ratio of the load score based on the performance parameters of each server. The data blocks are then stored and backed up based on the inverse ratio of the server load score. Calculate the load scores of all servers in this system and sort them from small to large to get the load score sequence SN′1, SN′2, ..., SN′ m ; Then use the load score sequence to sort the servers and get the server sequence W′1, W′2, ..., W′ m ; Based on the determined load scoring sequence SN′1, SN′2, ..., SN′ m , use the normalization algorithm to normalize the m load scores to the range of 0-1, and obtain m normalized load scores. Use the value 1 to subtract the m normalized load scores, and then normalize them again. Record them as the inverse of the load scores of the m servers, and sort them according to the order of the server sequence to obtain the load score inverse sequence E1, E2, ..., E m ; The data blocks are stored with the inverse ratio of the load score as the storage weight; The cloud infrastructure asset monitoring and analysis terminal continuously monitors stored cloud infrastructure assets, obtains the storage status of cloud infrastructure assets, and identifies risky assets. The cloud infrastructure asset optimization processing end provides optimization measures and missing data processing based on the identified risk assets; and updates and optimizes data blocks; Cloud infrastructure asset terminals coordinate data transmission between terminals, send operation commands, and ensure the normal operation of the system.

2. The cloud infrastructure asset management system based on cloud computing architecture according to claim 1, characterized in that: The cloud infrastructure asset storage terminal calculates the server load score based on each server performance parameter, where the server performance parameters include CPU usage, memory usage, network latency, and disk usage.

3. The cloud infrastructure asset management system based on cloud computing architecture according to claim 2, characterized in that: The cloud infrastructure asset storage terminal stores and backs up data blocks according to the load score of each server in the following manner: Get the cloud infrastructure asset Q to be stored in the cloud infrastructure asset storage terminal, split Q into data slices with the preset data size q, and obtain the data block sequence Q1, Q2, ..., Q n , where the value of q is determined by the system operator, n is a positive integer counting index, indicating the total number of data blocks; Then calculate the hash value corresponding to each data block, and together with the position number of the data block in the data block sequence and the storage position number of the next data block, use them as the unique identifier of the data block and record them as the numbered hash value; Sort the numbered hash values ​​in the order of the data block sequence to get the numbered hash value sequence H1, H2, ..., H n ; Calculate the load score of each server in the current system and perform storage and backup operations on data blocks.

4. The cloud infrastructure asset management system based on cloud computing architecture according to claim 3, characterized in that: The specific method of calculating the load score of the server at the storage end of the cloud computing architecture is as follows: Based on the servers W1, W2, ..., W in the current system m , get any server W i CPU usage of A i Memory usage B i 、Network delay C i and disk usage D i , and perform normalization, and recalculate the normalized CPU usage, memory usage, network latency, and disk usage to A i 、B i 、C i 、D i The original value of is overwritten, where m is a positive integer counting index, indicating the total number of servers, i is a positive integer counting index, 0 <i≤m、W i Represents any server; According to the load rating calculation formula: SN i =α·A i +β·B i +γ·C i +δ·D i Get server W i Load rating SN i , α, β, γ, δ are all weight coefficients, and their values ​​are determined by the system operator, α+β+γ+δ=1.

5. The cloud infrastructure asset management system based on cloud computing architecture according to claim 4, characterized in that: The specific method of storing data blocks in the cloud computing architecture storage end using the inverse ratio of the load score as the storage weight is as follows: Based on the determined load score inverse sequence E1, E2, ..., E m , for the data block sequence Q1,Q2,...,Q n And the corresponding numbered hash value sequence H1,H2,...,H n , perform segmentation; For server W ′ 1. Use E1 as the storage weight, from Q1, Q2, ..., Q n In the sequence, obtain the data blocks of E1 ratio and the data blocks from H1, H2, ..., H n Obtain the hash value of the E1 ratio in sequence and store it in W ′ 1 in; This process continues in this way until all m servers are assigned data blocks and numbered hash values ​​in inverse proportion to their load scores.

6. The cloud infrastructure asset management system based on cloud computing architecture according to claim 5, characterized in that: The specific method of storing data blocks in the cloud computing architecture storage end using the inverse ratio of the load score as the storage weight also includes: For server W ′ 1, using E1 as the storage weight, from Q1, Q2, ..., Q n In the reverse order, obtain the data blocks of E1 ratio and the data blocks from H1, H2, ..., H n Get the hash value of E1 ratio in reverse order and store it in W ′ 1, as backup data; This process continues in this way until all m servers are assigned data blocks and numbered hash values ​​in inverse proportion to their load scores.

7. The cloud infrastructure asset management system based on cloud computing architecture according to claim 6, characterized in that: The cloud infrastructure asset monitoring and analysis terminal obtains the storage status of cloud infrastructure assets and identifies risky assets in the following manner: Monitor access requests in real time and obtain access data block Q j Failed Server W i and the hash value H j , determine server W i The storage status of the current cloud infrastructure asset Q is abnormal, where j is a positive integer count index starting from 1 and the maximum value is n, Q j Represents any data block.

8. The cloud infrastructure asset management system based on cloud computing architecture according to claim 7, characterized in that: The cloud infrastructure asset monitoring and analysis terminal may further include: Obtain all data blocks of the abnormal cloud infrastructure asset Q and the corresponding numbered hash values ​​of the data blocks, extract the position number in the numbered hash value, and calculate the position number according to the server sequence W′1, W′2, ..., W′ m Arrange the position numbers in the order of the position numbers, determine whether the position numbers are continuous, and determine whether the total number of data blocks matches the total number of data blocks n that are divided. If there is a missing position number, it is regarded as the data block Q corresponding to the current position number. j If it is missing, the cloud infrastructure asset Q is considered a risky asset.

9. The cloud infrastructure asset management system based on cloud computing architecture according to claim 8, characterized in that: The cloud infrastructure asset optimization processing terminal provides optimization measures and missing data processing based on the identified risk assets in the following specific manner: For access requests that fail and missing data blocks Q j When the missing data block Q is obtained j Server W i , extract the previous adjacent successfully accessed data block Q j-1 The hash value of the number, intercept Q j-1 The position number of the hash value is extended one bit backward to obtain the data block Q j The position number in the hash value is denoted as P j ; The location number corresponding to the cloud infrastructure asset Q in the extracted backup data is P j Backup data block Q′ j , provided to the access request, and the data block Q is restored in the first time j Normal access; Then use the backup data block Q′ j For the currently missing data block Q j Fill in, using the backup data block Q′ j The hash value of the missing data block Q j The hash value of the number is filled.

10. The cloud infrastructure asset management system based on cloud computing architecture according to claim 9, characterized in that: The specific method by which the cloud infrastructure asset optimization processing end updates and optimizes data blocks is as follows: At a preset interval T, the load score of each server is obtained, and the inverse load score ratio is recalculated based on the load score. The inverse load score ratio is used as the storage ratio to redistribute the cloud infrastructure assets Q to each server for storage and backup. Get the data block Q of the user's change operation in real time j , according to data block Q j The number of the hash value is used to find the corresponding backup data block Q' j ; Using data block Q j Backup data block Q′ j To overwrite, use data block Q j The numbered hash value of the backup data block Q′ j The number hash value is overwritten and the update operation is automatically triggered every preset time T. For any cloud infrastructure asset Q, the position number in the number hash value of any data block is first obtained, and the corresponding backup data block is found according to the position number; Determine whether the hash value portion of the numbered hash value of the data block is the same as the hash value of the numbered hash value of the corresponding backup data block; If they are the same, no processing will be done; If they are not the same, the data block is overwritten with the backup data block, and the number hash value of the data block is overwritten with the number hash value of the backup data block.

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