Methods for optimizing server storage space

Abstract

The present invention relates to the technical field of big data resource services, and specifically discloses a method for optimizing server storage space. The method comprises: comparing a data set to be optimized, evaluating a policy feasibility, and determining storage space performance. First, the data set to be optimized of the server storage space is obtained by comparing a data access frequency with a reference frequency, a feasibility evaluation index of a data deletion policy is determined, and adjustment and optimization are performed according to changes in the feasibility evaluation index. The data deletion policy is recorded as a data deletion preset policy, and a data deletion operation is performed on the data set to be optimized of the server storage space, thereby reducing the burden of the storage space. The released storage space can be reallocated to other data, thereby improving the resource utilization of the storage space. The storage performance of the server storage space after the data deletion is determined, and feedback is provided on the optimization of the server storage space.

Description

Technical Field

[0001] The present invention relates to the technical field of big data resource services, and in particular to a method for optimizing server storage space. Background Art

[0002] As servers grow older, a large amount of no longer needed data accumulates on storage devices. This data not only takes up valuable storage space but can also affect server performance and stability. Therefore, an effective method is needed to delete this data and free up the space it occupies, thereby optimizing server storage space.

[0003] For example, the invention patent with announcement number CN110209359B announces a method, device and server for managing system storage space; a recycling node is preset in the system; wherein the method includes: querying whether there are nodes to be emptied in the recycling node according to a preset period; if there are nodes to be emptied, judging whether the nodes to be emptied meet the preset clearing conditions; if they meet the preset clearing conditions, clearing the nodes to be emptied according to the preset clearing method; in the method, the system sets a recycling node, queries the recycling node regularly with a preset period, and clears the nodes to be emptied in the recycling node based on the preset clearing conditions and clearing method.

[0004] For example, the invention patent with announcement number CN111399754B announces a storage space release method, device and distributed system; wherein, a distributed system includes: a management server, which is used to select a target reference time point as a deletion basis from multiple recorded reference time points when it detects that the distributed system meets the preset storage space release conditions; obtain the current time point of the auxiliary clock as the auxiliary time point; send the target reference time point and the auxiliary time point to each storage server in the distributed system; the storage server is used to receive the target reference time point and the auxiliary time point; use the difference value between the current system time of the storage server itself and the auxiliary time point to correct the target reference time point to obtain the corrected target reference time point; from the stored sub-objects, delete the sub-objects whose local storage time point matches the corrected target reference time point.

[0005] Combining the above technical solutions, it is found that most server storage space optimization technical solutions use a relatively simple method when deleting the server's storage data to release its corresponding space, and pay less attention to the server after the storage space is optimized. It may not be able to adapt to the situation of deleting all data, resulting in the server's storage space optimization technical solution having high limitations, and may not be able to effectively release storage space, affecting the use of server storage space. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a method for optimizing server storage space, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for optimizing server storage space, comprising: S1. the server receives a storage space optimization instruction, synchronously obtains the access frequency of each data in the server storage space, and compares it with a predefined data access reference frequency to obtain the data set that needs to be optimized in the server storage space; S2. the server's memory release mechanism detects the predefined data deletion policy to obtain detection information of the data deletion policy, and comprehensively considers the data set that needs to be optimized in the server storage space to determine the feasibility evaluation index of the data deletion policy, and compares it with the predefined feasibility evaluation preset index to obtain a comparison result; S3. based on the comparison result, the data deletion policy is recorded as a data deletion preset policy, whereby the server executes the storage space optimization instruction through the data deletion preset policy, that is, performs a data deletion operation on the data set that needs to be optimized in the server storage space through the data deletion preset policy, and determines the storage performance of the server storage space after the data deletion, and obtains the storage performance evaluation index of the server storage space, thereby providing feedback on the optimization of the server storage space.

[0008] As a further method, the data set that needs to be optimized in the server storage space is obtained, and the specific analysis process is: compare the access frequency of each data in the server storage space with the predefined data access reference frequency. If the access frequency of a certain data in the server storage space is less than or equal to the data access reference frequency, then the data corresponding to the certain data access frequency in the server storage space is recorded as the data that needs to be optimized in the server storage space, and the data set that needs to be optimized in the server storage space is obtained by statistics.

[0009] As a further method, the feasibility evaluation index of the data deletion policy is determined, and the specific determination process is: obtaining the detection information of the data deletion policy, specifically including the data deletion speed of the data deletion policy within the feasibility evaluation period, the time required for data deletion of the data deletion policy within the feasibility evaluation period, the number of parallel data deletions of the data deletion policy, and the number of compatible data deletion categories of the data deletion policy; obtaining the number of data categories of the data set that needs to be optimized in the server storage space, and combining the detection information of the data deletion policy, comprehensively analyzing to obtain the feasibility evaluation index of the data deletion policy.

[0010] As a further method, the feasibility evaluation index of the data deletion strategy is as follows:

[0011] Where KX is the feasibility evaluation index of the data deletion strategy, V1 is the data deletion speed of the data deletion strategy within the feasibility evaluation period, t0 is the start time of the feasibility evaluation period, t1 is the end time of the feasibility evaluation period, T is the time required for data deletion by the data deletion strategy within the feasibility evaluation period, BX is the number of parallel data deletions of the data deletion strategy, SL1 is the number of compatible data deletion categories of the data deletion strategy, SL2 is the number of data categories of the data sets that need to be optimized in the server storage space, α1 is the weight factor corresponding to the total amount of data deletion predefined in the server information management library, α2 is the weight factor corresponding to the time required for data deletion predefined in the server information management library, α3 is the weight factor corresponding to the number of parallel data deletions predefined in the server information management library, and α4 is the impact factor corresponding to the unit value of the difference in the number of compatible data deletion categories predefined in the server information management library.

[0012] As a further method, the data deletion operation is performed on the data set that needs to be optimized in the server storage space through the data deletion preset strategy, specifically: if the comparison result shows that the feasibility evaluation index of the data deletion strategy is greater than the feasibility evaluation preset indicator, the data deletion strategy is directly recorded as the data deletion preset strategy, and the data deletion operation is performed on the data set that needs to be optimized in the server storage space; if the comparison result shows that the feasibility evaluation index of the data deletion strategy is less than or equal to the feasibility evaluation preset indicator, the feasibility evaluation index of the data deletion strategy and the feasibility evaluation preset indicator are differenced to obtain the feasibility evaluation index deviation value of the data deletion strategy, and matched with the policy correction data set corresponding to each feasibility evaluation indicator deviation interval preset in the server information management library, thereby obtaining the policy correction data set of the data deletion strategy, and finally correcting the data deletion strategy through the policy correction data set, and recording the corrected data deletion strategy as the data deletion preset strategy, so as to perform the data deletion operation on the data set that needs to be optimized in the server storage space.

[0013] As a further method, the storage performance evaluation index of the server storage space is specifically processed as follows: the usage of the server storage space during the space optimization period is monitored through a monitoring mechanism, thereby obtaining the space occupancy of the server storage space at the start time point of space optimization, the space occupancy of the server storage space at the end time point of space optimization, and the rated space of the server storage space, and performing data processing to obtain the space optimization value of the server storage space; obtaining the number of data storage categories of the server storage space at the start time point of space optimization and the number of remaining data storage categories of the server storage space at the end time point of space optimization; based on the data reading speed of the server storage space during the performance evaluation period, the data writing speed of the server storage space during the performance evaluation period, the data storage response time of the server storage space during the performance evaluation period, the number of data storage categories of the server storage space at the start time point of space optimization, the number of remaining data storage categories of the server storage space at the end time point of space optimization, the number of data categories of the data sets to be optimized in the server storage space, and the space optimization value of the server storage space, a comprehensive analysis is performed to obtain the storage performance evaluation index of the server storage space.

[0014] As a further method, the optimization of the server storage space is fed back, and the specific feedback process is: the storage performance evaluation index of the server storage space is verified with the predefined storage performance evaluation index preset value to generate a storage space optimization label. If the storage performance evaluation index of the server storage space is greater than or equal to the storage performance evaluation index preset value, the storage space optimization label is defined as a storage space optimization qualified label; if the storage performance evaluation index of the server storage space is less than the storage performance evaluation index preset value, the storage space optimization label is defined as a storage space optimization abnormal label, so as to provide feedback on the optimization of the server storage space.

[0015] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0016] (1) The present invention provides a method for optimizing server storage space. First, by comparing the data access frequency with the reference frequency, the data sets that need to be optimized in the server storage space are obtained. Optimization is performed on these data sets, and the feasibility evaluation index of the data deletion strategy is determined. Adjustment and optimization are performed according to changes in the feasibility evaluation index. The data deletion strategy is recorded as a data deletion preset strategy to ensure that the data deletion strategy always meets the optimization requirements. In this way, data deletion operations are performed on the data sets that need to be optimized in the server storage space, thereby reducing the burden on the storage space. The released storage space can be reallocated to other data, improving the resource utilization of the storage space. The storage performance of the server storage space after data deletion is determined, thereby providing feedback on the optimization of the server storage space.

[0017] (2) The present invention detects predefined data deletion strategies through the memory release mechanism of the server, which helps to identify data sets that occupy a large amount of memory but have a low access rate, thereby formulating data deletion strategies more accurately and improving the accuracy of data deletion strategies. It also comprehensively considers the data sets that need to be optimized in the server storage space, determines the feasibility evaluation indicators of the data deletion strategy, and enhances the effectiveness and flexibility of the data deletion strategy.

[0018] (3) The present invention performs data deletion operations on the data sets that need to be optimized in the server storage space through a data deletion preset strategy, thereby releasing storage space, helping to reduce the waste of storage resources, and determining the storage performance of the server storage space after data deletion to obtain a storage performance evaluation index of the server storage space. After deleting redundant data, the storage space can manage the remaining data more efficiently, which helps to improve the storage read and write speed, data transmission speed and overall performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0020] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the present invention provides a method for optimizing server storage space, including: S1. The server receives a storage space optimization instruction, synchronously obtains the data access frequency of each data in the server storage space, and compares it with a predefined data access reference frequency to obtain the data set that needs to be optimized in the server storage space.

[0023] The storage space optimization instruction mentioned above may specifically be an instruction issued by a user or a central processing unit for optimizing the storage space of a server.

[0024] Specifically, the data set that needs to be optimized in the server storage space is obtained, and the specific analysis process is as follows:

[0025] Compare the data access frequencies of the server storage space with the data access reference frequencies predefined in the server information management library. If the data access frequency of a certain data in the server storage space is less than or equal to the data access reference frequency, then the data corresponding to the data access frequency of the server storage space is recorded as the data that needs to be optimized in the server storage space. In this way, the data set that needs to be optimized in the server storage space is obtained by statistics.

[0026] It should be explained that server storage space is limited, and data access frequencies are often different. Identifying and optimizing data with low access frequencies will help reallocate storage space so that data with high access frequencies can occupy better storage resources, thereby improving the efficiency and performance of the overall storage system.

[0027] The above-mentioned data sets that need to be optimized can specifically include users' old transaction records, order details or purchase history on a platform server, old versions of documents, pictures, videos and other files, and old entries in system logs, application logs or network logs.

[0028] The access frequency of each data in the above server storage space can be extracted from the access log of the server.

[0029] If the access frequency of a certain data in the server storage space is greater than the data access reference frequency, the server storage space still maintains the storage status of the data.

[0030] S2. The server's memory release mechanism detects the predefined data deletion policy to obtain detection information of the data deletion policy, and comprehensively considers the data set that needs to be optimized in the server storage space to determine the feasibility evaluation index of the data deletion policy, and compares it with the predefined feasibility evaluation preset index to obtain a comparison result.

[0031] The server's memory release mechanism is a key feature of the server's memory management system. It monitors and manages memory usage, ensuring efficient and effective use. When memory resources are limited or a specific memory area needs to be freed, the memory release mechanism triggers a data deletion operation to remove the data in need of optimization and reclaim memory.

[0032] The above-mentioned predefined data deletion policy is specifically a data deletion policy predefined in the server information management library, wherein the data deletion policy may be a deletion rule including other conditions such as deletion time, amount of deleted data, access frequency, etc.

[0033] Furthermore, the feasibility evaluation index of the data deletion strategy is determined in the following specific process:

[0034] Obtain detection information of the data deletion policy, including the data deletion speed of the data deletion policy within the feasibility assessment cycle, the time required for data deletion by the data deletion policy within the feasibility assessment cycle, the number of parallel data deletions by the data deletion policy, and the number of compatible data deletion categories of the data deletion policy.

[0035] The aforementioned data deletion speed and duration can be obtained from the server's memory release mechanism test report. The number of parallel data deletions and the number of compatible data deletion categories can be determined through concurrency control technology, using thread pools or process pools to manage concurrent deletion tasks. By monitoring thread pool or process pool usage (such as the number of idle threads and active threads), different numbers of parallel data deletions and compatible data deletion categories can be determined.

[0036] The above-mentioned feasibility assessment cycle specifically refers to the period from the start time point of the feasibility assessment to the end time point of the feasibility assessment. The feasibility assessment cycle is determined by the space optimization management personnel based on a comprehensive analysis of factors such as the specific data deletion strategy, actual needs, and the status of the server storage space.

[0037] The number of data categories of the data set that needs to be optimized in the server storage space is obtained, and combined with the detection information of the data deletion strategy, a comprehensive analysis is performed to obtain the feasibility evaluation index of the data deletion strategy.

[0038] The above-mentioned data set that needs to be optimized, in this embodiment, the data set that needs to be optimized in the server storage space can be the old transaction records of users on the shopping platform retained by the server, recording the user's transaction information on the platform, such as transaction time, transaction quantity, transaction price, and product price. In this embodiment, the number of data categories of the data set that needs to be optimized in the server storage space can be 4.

[0039] The number of data categories of the data sets that need to be optimized in the server storage space can be extracted from a data access frequency comparison report of the server storage space.

[0040] Specifically, the feasibility evaluation index for determining the data deletion strategy is specifically expressed as follows:

[0041]

[0042] Wherein, KX is a feasibility evaluation index of the data deletion policy, which is a quantitative index used in this embodiment to ensure the effective implementation of the data deletion policy, efficient use of resources, and minimization of the impact on the system.

[0043] V1 is the data deletion speed of the data deletion policy within the feasibility assessment cycle. It refers to the rate at which the data deletion policy executes deletion operations within the feasibility assessment cycle. This is usually measured in terms of the amount of data deleted per second (such as MB / s or GB / s). The faster the data deletion speed, the more data the policy can delete in the same amount of time, thereby improving data management efficiency.

[0044] t0 is the starting time point of the feasibility assessment cycle, and t1 is the ending time point of the feasibility assessment cycle.

[0045] The above-mentioned feasibility assessment cycle starting time point refers to the starting time point of the feasibility assessment cycle, and the feasibility assessment cycle ending time point refers to the ending time point of the feasibility assessment cycle.

[0046] The above expression It is expressed as the integration of the data deletion speed from the start time point to the end time point of the feasibility assessment period, and the total amount of data deleted by the data deletion policy within the feasibility assessment period can be obtained.

[0047] T is the duration required for data deletion within the feasibility assessment cycle of the data deletion policy. It refers to the total time required to complete the data deletion task within the feasibility assessment cycle, which includes all the time from policy activation to the complete removal of data from the system. The shorter the data deletion time, the more efficient the policy execution and the smaller the impact on the system.

[0048] BX is the number of parallel data deletions in a data deletion policy, which refers to the number of data deletion tasks that the policy can handle simultaneously at the same time. This reflects the policy's ability in concurrent processing. A higher number of parallel data deletions means that the policy can better cope with large-scale data deletion needs while reducing the occupation of system resources and the impact on server operations.

[0049] SL1 is the number of data deletion category compatibility of the data deletion policy, which refers to the number of data of different types, formats, or sources that the data deletion policy can be compatible with and process. A data deletion policy with strong compatibility can be more widely applied in different data environments, improving the flexibility and practicality of the policy.

[0050] SL2 is the number of data categories of data sets that need to be optimized in the server storage space, which refers to the number of data categories corresponding to the data that need to perform data deletion operations in the server storage space.

[0051] α1 is the weight factor corresponding to the total amount of data deletion predefined in the server information management library.

[0052] In a specific embodiment, the weight factor corresponding to the total amount of data deletion can be directly obtained from the server information management library when in use. The factor represents the numerical value of the degree of influence on the feasibility evaluation index of the data deletion strategy in the process of determining the feasibility of the data deletion strategy. The corresponding relationship can be a pre-set mapping relationship. For example, the total amount of data deletion and the weight factor corresponding to the total amount of data deletion preset in the server information management library form a mapping set, and the real-time total amount of data deletion is brought into the mapping set to obtain the weight factor corresponding to the total amount of data deletion. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0053] α2 is the weight factor corresponding to the time required for data deletion predefined in the server information management library.

[0054] In a specific embodiment, the weight factor corresponding to the time required for data deletion can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the feasibility evaluation index of the data deletion strategy in the process of determining the feasibility of the data deletion strategy. The corresponding relationship can be a pre-set mapping relationship. For example, the time required for data deletion and the weight factor corresponding to the time required for data deletion preset in the server information management library form a mapping set, and the real-time time required for data deletion is brought into the mapping set to obtain the weight factor corresponding to the time required for data deletion. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0055] α3 is the weight factor corresponding to the number of parallel data deletions predefined in the server information management library.

[0056] In a specific embodiment, the weight factor corresponding to the parallel number of data deletions can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the feasibility evaluation index of the data deletion strategy in the process of determining the feasibility of the data deletion strategy. The corresponding relationship can be a pre-set mapping relationship. For example, the parallel number of data deletions and the weight factor corresponding to the parallel number of data deletions preset in the server information management library form a mapping set, and the real-time parallel number of data deletions is brought into the mapping set to obtain the weight factor corresponding to the parallel number of data deletions. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0057] α4 is the impact factor corresponding to the unit value of the data deletion category compatible quantity difference predefined in the server information management library.

[0058] In a specific embodiment, the impact factor corresponding to the unit value of the data deletion category compatible quantity difference can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the feasibility evaluation index of the data deletion policy in the process of determining the feasibility of the data deletion policy. The corresponding relationship can be a pre-set mapping relationship. For example, the data deletion category compatible quantity difference and the impact factor corresponding to the unit value of the data deletion category compatible quantity difference preset in the server information management library form a mapping set, and the real-time data deletion category compatible quantity difference is brought into the mapping set to obtain the impact factor corresponding to the unit value of the data deletion category compatible quantity difference. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0059] In this embodiment, the faster the data deletion speed, the shorter the required data deletion time. This is because, with a fixed amount of data, a faster deletion speed means a shorter deletion time, and a larger number of parallel data deletions can usually increase the data deletion speed, because parallel processing can handle multiple deletion tasks at the same time, thereby speeding up the overall deletion progress, which means that the data deletion strategy can more effectively utilize computing resources and storage resources when deleting data, improving the processing capability of the data deletion strategy, and having significant advantages when the data deletion strategy executes more deletion tasks, thereby increasing the feasibility evaluation index of the data deletion strategy; a higher number of data category compatibility reflects the flexibility of the data deletion strategy in adapting to different deletion requirements. A higher number of data category compatibility can ensure that its strategy can meet more data deletion requirements, thereby improving the feasibility evaluation index of the data deletion strategy.

[0060] Furthermore, the feasibility evaluation index of the data deletion policy is compared with a predefined feasibility evaluation preset index to obtain a comparison result. The specific comparison process is as follows:

[0061] The feasibility evaluation index of the data deletion policy is compared with the feasibility evaluation preset index predefined in the server information management library to obtain comparison results, namely a first comparison result and a second comparison result.

[0062] The first comparison result is that the feasibility evaluation index of the data deletion policy is greater than the preset feasibility evaluation index.

[0063] The second comparison result is that the feasibility evaluation index of the data deletion policy is less than or equal to a preset feasibility evaluation index.

[0064] S3. Based on the comparison result, the data deletion policy is recorded as the data deletion preset policy, and the server executes the storage space optimization instruction through the data deletion preset policy, that is, the data deletion operation is performed on the data set that needs to be optimized in the server storage space through the data deletion preset policy, and the storage performance of the server storage space after the data deletion is judged to obtain the storage performance evaluation index of the server storage space, so as to provide feedback on the optimization of the server storage space.

[0065] Specifically, the data deletion operation is performed on the data set that needs to be optimized in the server storage space by using the data deletion preset policy, specifically:

[0066] If the comparison result shows that the feasibility evaluation index of the data deletion policy is greater than the feasibility evaluation preset index, the data deletion policy is directly recorded as the data deletion preset policy, and the data deletion operation is performed on the data set that needs to be optimized in the server storage space.

[0067] If the comparison result shows that the feasibility evaluation index of the data deletion policy is less than or equal to the feasibility evaluation preset index, the feasibility evaluation index of the data deletion policy is differenced with the feasibility evaluation preset index to obtain the feasibility evaluation index deviation value of the data deletion policy, and matched with the policy correction data set corresponding to each feasibility evaluation index deviation interval preset in the server information management library. The specific matching process is: obtain the mapping set of the feasibility evaluation index deviation value of the data deletion policy and the policy correction data set from the server information management library, first determine the interval to which the feasibility evaluation index deviation value of the data deletion policy belongs, and assign the policy correction data set corresponding to the interval to the data deletion policy corresponding to the feasibility evaluation index deviation value, thereby obtaining the policy correction data set of the data deletion policy, and finally correct the data deletion policy through the policy correction data set, and record the corrected data deletion policy as the data deletion preset policy, so as to perform data deletion operations on the data set that needs to be optimized in the server storage space.

[0068] The above-mentioned data deletion policy is modified by modifying the data set through the policy. Specifically, for the deletion of a large amount of data, the data deletion policy can be avoided from deleting it all at once, and the data can be divided into multiple batches for deletion, thereby improving the data deletion speed; by increasing the server's key-value database to expand the write traffic, thereby supporting more parallel deletion operations and increasing the number of parallel data deletion operations.

[0069] The modified data deletion policy is recorded as a data deletion preset policy, wherein the data deletion preset policy may specifically include deletion time, data deletion speed, amount of deleted data, number of compatible data deletion categories, etc.

[0070] It needs to be explained that when performing data deletion operations on the data sets that need to be optimized in the server storage space through the data deletion preset policy, it is necessary to judge the actual operation process of the data deletion preset policy, so as to reduce the negative impact of the actual operation process of the data deletion preset policy on the judgment of the storage performance of the server storage space after data deletion.

[0071] The actual operation process of the data deletion preset policy is judged above to obtain the actual operation evaluation value of the data deletion preset policy. The specific method is as follows:

[0072] The data related to the actual operation process of the data deletion preset policy is obtained, including the actual data deletion speed of the data deletion preset policy, the actual time required for data deletion, the actual CPU usage and the data loss rate.

[0073] The data related to the actual operation process of the above-mentioned data deletion preset policy can be extracted from the operation report of the data deletion preset policy.

[0074] Obtain the total amount of data in the dataset that needs to be optimized in the server storage space, where the total amount of data can be obtained through the statistical comparison results between the data access frequency of the server storage space and the data access reference frequency, and match it with the data deletion adaptation speed, data deletion adaptation duration, CPU reference occupancy and data loss limit rate corresponding to each data total amount interval predefined in the server information management library to obtain the data deletion adaptation speed, data deletion adaptation duration, CPU reference occupancy and data loss limit rate of the data deletion preset policy.

[0075] The above-mentioned specific matching process takes the data deletion adaptation speed of the data deletion preset policy as an example, extracts the mapping set between the total data amount of the data set to be optimized in the server storage space and the data deletion adaptation speed from the server information management library, determines the total data amount interval to which the total data amount of the data set to be optimized in the server storage space belongs, and assigns the data deletion adaptation speed corresponding to the interval to the data deletion preset policy corresponding to the total data amount, thereby obtaining the data deletion adaptation speed of the data deletion preset policy.

[0076] Based on a comprehensive analysis of the actual data deletion speed of the data deletion preset policy, the actual data deletion time required by the data deletion preset policy, the actual CPU usage of the data deletion preset policy, the data loss rate of the data deletion preset policy, the data deletion adaptation speed of the data deletion preset policy, the data deletion adaptation time of the data deletion preset policy, the reference CPU usage of the data deletion preset policy, and the data loss threshold rate of the data deletion preset policy, the actual operational evaluation value of the data deletion preset policy is obtained. The specific method is as follows:

[0077]

[0078] Wherein, PG is the actual operation evaluation value of the data deletion preset policy. In this embodiment, it is a quantitative indicator obtained after a comprehensive evaluation of the effect, performance, and impact of the data deletion preset policy during actual execution.

[0079] Vs is the actual data deletion speed of the data deletion preset policy, which is related to the description of the data deletion speed of the data deletion policy within the feasibility assessment period. It refers to the data deletion rate during the actual execution of the data deletion preset policy, measured in the amount of data deleted per second.

[0080] Vs′ is the data deletion adaptation speed of the data deletion preset policy, which refers to the data deletion standard speed predefined according to the data volume.

[0081] SJ is the actual duration required for data deletion according to the data deletion preset policy, which is related to the description of the duration required for data deletion according to the above data deletion policy within the feasibility assessment cycle. It refers to the total time required for the data deletion preset policy to complete the data deletion task, including all the time from the actual initiation of the policy to the complete removal of the data from the system when executing the data deletion operation. The closer the duration required for data deletion is to the adaptation duration, the higher the policy execution efficiency is and the smaller the impact on the system is.

[0082] SJ′ is the data deletion adaptation duration of the data deletion preset policy, which refers to the standard data deletion duration predefined according to the data volume.

[0083] ZY is the actual CPU usage of the data deletion preset policy, which refers to the CPU resource usage during the data deletion process executed by the data deletion preset policy. The lower the resource usage and the closer it is to the reference usage, the smaller the impact of the policy on system performance.

[0084] ZY′ is the reference CPU usage of the data deletion preset policy, which refers to the reference value of the CPU usage when executing the data deletion operation, which is predefined based on the data volume.

[0085] Ds is the data loss rate of the data deletion preset policy, which refers to the ratio of the amount of data lost during the execution of data deletion by the data deletion preset policy to the total amount of data that should be deleted. A lower data loss rate indicates that the policy has higher data integrity.

[0086] Ds′ is the data loss limit rate of the data deletion preset policy, which refers to the maximum value of the data loss rate allowed during the data deletion process.

[0087] In this embodiment, when the total amount of data is constant, the faster the actual data deletion speed is and the closer it is to the data deletion adaptation speed, the shorter the actual required time will be and the closer it will be to the data deletion adaptation time. Conversely, the slower the actual data deletion speed is and the greater the difference with the data deletion adaptation speed is, the longer the actual required time will be, and the difference with the data deletion adaptation time will also be affected. The speed of data deletion is often affected by the actual CPU occupancy. When the CPU occupancy rate is high and higher than the CPU reference occupancy rate, the server's data processing capacity decreases, resulting in a slower data deletion speed and an increased required time. At the same time, if the CPU occupancy rate is high and higher than the CPU reference occupancy rate, the data deletion process will be frequently interrupted, which will increase the risk of data loss and have a negative impact on the data loss rate. When the CPU occupancy rate is low and far lower than the CPU reference occupancy rate, it may directly lead to a low overall server load, making it impossible to drive the data deletion preset policy.

[0088] It should be explained that the actual data deletion speed of the data deletion preset policy and the actual time required for data deletion of the data deletion preset policy, as well as the data deletion speed of the data deletion policy during the feasibility assessment period and the time required for data deletion of the data deletion policy during the feasibility assessment period, all represent the data deletion speed and time of their policies. This is because the actual data deletion speed of the data deletion preset policy and the actual time required for data deletion of the data deletion preset policy are the data deletion speed and time of the data deletion policy during actual implementation; while the data deletion speed of the data deletion policy during the feasibility assessment period and the time required for data deletion of the data deletion policy during the feasibility assessment period are the data deletion policy and time tested during the feasibility test of the data deletion policy. Therefore, there are numerical differences between the above corresponding parameters. In the process of feasibility assessment of data deletion policy, data deletion speed and time are expressed as whether the predefined data deletion policy meets the current factors such as the total amount of deletion and data deletion category. In the process of actual operation evaluation of data deletion preset policy, data deletion speed and time are expressed as the performance and quality of the current data deletion policy during actual implementation.

[0089] Furthermore, the storage performance of the server storage space after data deletion is determined, specifically obtaining storage performance information of the server storage space, including the data reading speed of the server storage space during the performance evaluation period, the data writing speed of the server storage space during the performance evaluation period, and the data storage response time of the server storage space during the performance evaluation period.

[0090] The storage performance information of the server storage space can be obtained by extracting the operation report of the server storage space.

[0091] The above-mentioned performance evaluation cycle is specifically a period of time from the start time point of the performance evaluation to the end time point of the performance evaluation. The performance evaluation cycle is determined by the space optimization management personnel based on a comprehensive analysis of factors such as the server storage status, actual needs, and the amount of stored data.

[0092] Specifically, the storage performance evaluation index of the server storage space is calculated as follows:

[0093] The usage of the server storage space during the space optimization cycle is monitored through a monitoring mechanism, thereby obtaining the space occupancy of the server storage space at the start time point of space optimization, the space occupancy of the server storage space at the end time point of space optimization, and the rated space amount of the server storage space. The space occupancy at the start time point of space optimization, the space occupancy at the end time point of space optimization, and the rated space amount can be extracted from the monitoring report of the monitoring mechanism, and data processing is performed to obtain the space optimization value of the server storage space.

[0094] The above monitoring mechanism specifically tracks the usage of storage space and data deletion in real time to ensure the stable operation of server storage space and efficient management of data.

[0095] The above-mentioned space optimization cycle is specifically a period of time from the start time point of space optimization to the end time point of space optimization. The space optimization cycle is determined by the space optimization management personnel based on a comprehensive analysis of factors such as actual needs, optimization status, and server performance status.

[0096] Get the number of data storage categories at the start time of space optimization of the server storage space and the number of remaining data storage categories at the end time of space optimization of the server storage space.

[0097] The above data storage category quantity and the remaining data storage category quantity can be obtained from the server storage space operation report.

[0098] The storage performance evaluation index of the server storage space is obtained through comprehensive analysis based on the data read speed of the server storage space during the performance evaluation period, the data write speed of the server storage space during the performance evaluation period, the data storage response time of the server storage space during the performance evaluation period, the number of data storage categories of the server storage space at the start time of space optimization, the number of remaining data storage categories of the server storage space at the end time of space optimization, the number of data categories of the data sets to be optimized in the server storage space, and the space optimization value of the server storage space. The specific expression is:

[0099]

[0100] Where XN is the storage performance evaluation index of the server storage space. In this embodiment, it is a key indicator for measuring and evaluating the performance of the server storage system. These indicators can reflect the speed, efficiency, and ability of the storage system when processing data, and are important for ensuring that the server can meet application requirements and perform performance optimization.

[0101] PG is the actual operational evaluation value of the data deletion preset policy, which indicates that the actual speed of data deletion of the data deletion preset policy, the actual time required for data deletion of the data deletion preset policy, the actual CPU occupancy of the data deletion preset policy, the data loss rate of the data deletion preset policy, the data deletion adaptation speed of the data deletion preset policy, the data deletion adaptation time of the data deletion preset policy, the reference CPU occupancy of the data deletion preset policy and the data loss limit rate of the data deletion preset policy are comprehensively analyzed to obtain the actual operational evaluation value of the data deletion preset policy.

[0102] V2 is the data read speed of the server storage space during the performance evaluation cycle, which refers to the rate at which the server storage space reads data from the server during the performance evaluation cycle. This is usually measured in the amount of data that can be read per second, such as MB / s (megabytes per second) or GB / s (gigabytes per second). A high read speed means that the server storage space can respond to data requests faster, thereby improving overall system performance.

[0103] V3 is the data write speed of the server storage space during the performance evaluation period, which refers to the rate at which the server storage space can write data from the server to its storage space during the performance evaluation period.

[0104] The above expression (V2+V3) represents the data throughput of the server storage space during the performance evaluation period.

[0105] S is the data storage response time of the server storage space during the performance evaluation period. It refers to the time interval from the server storage space receiving a data storage request to the start of the data storage operation (or the start of data being written to the storage medium). It reflects the response speed and efficiency of the storage system in processing data storage requests.

[0106] SL is the number of data storage categories in the server storage space at the start time of space optimization, which refers to the number of data storage categories corresponding to the server storage space at the beginning of space optimization.

[0107] SL2 is the number of data categories of data sets that need to be optimized in the server storage space.

[0108] SL3 is the number of data storage categories remaining in the server storage space at the end of space optimization, which refers to the number of data storage categories actually remaining in the server storage space at the end of space optimization.

[0109] The above expression (SL-SL2) refers to the expected value of the number of remaining data storage categories in the server storage space during the space optimization period, and [(SL-SL2)-SL3] is expressed as the difference between the expected value and the actual value of the number of remaining data storage categories in the server storage space during the space optimization period.

[0110] In this embodiment, the difference between the expected value and the actual value of the number of remaining categories of data storage is shown in Table 1 below:

[0111] Table 1 Changes in the numerical relationship between expected values ​​and actual values

[0112]

[0113]

[0114] In this embodiment, according to the table of the difference between the expected value and the actual value of the number of remaining categories of data storage, it can be seen that when the number of data categories to be deleted is large, the data deletion policy may have execution obstacles, such as insufficient data deletion authority, concerns about the risk of accidental data deletion, etc., so that the difference between the expected value and the actual value is relatively large. Therefore, before executing the data deletion operation, the feasibility of the data deletion policy needs to be analyzed to ensure that the application effect of the policy is in the best state.

[0115] Y is the space optimization value of the server storage space, which refers to the difference in storage space occupancy before and after the data deletion operation is performed on the server storage space.

[0116] Y′ is a predefined space optimization adaptation value in the server information management library, which refers to a pre-set space optimization expected value.

[0117] h1 is the weight factor corresponding to the data throughput predefined in the server information management library.

[0118] In a specific embodiment, the weight factor corresponding to the data throughput can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the storage performance evaluation index of the server storage space in the process of analyzing the storage performance of the server storage space. The corresponding relationship can be a pre-set mapping relationship. For example, the data throughput and the weight factor corresponding to the data throughput preset in the server information management library form a mapping set, and the real-time data throughput is brought into the mapping set to obtain the weight factor corresponding to the data throughput. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0119] h2 is the weight factor corresponding to the data storage response time predefined in the server information management library.

[0120] In a specific embodiment, the weight factor corresponding to the data storage response time can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the storage performance evaluation index of the server storage space in the process of analyzing the storage performance of the server storage space. The corresponding relationship can be a pre-set mapping relationship. For example, the data storage response time and the weight factor corresponding to the data storage response time preset in the server information management library form a mapping set, and the real-time data storage response time is brought into the mapping set to obtain the weight factor corresponding to the data storage response time. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0121] h3 is the impact factor corresponding to the unit value of the difference in the number of remaining categories of data storage predefined in the server information management library, and e is a natural constant.

[0122] In a specific embodiment, the impact factor corresponding to the unit value of the data storage remaining category quantity difference can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the storage performance evaluation index of the server storage space in the process of analyzing the storage performance of the server storage space. The corresponding relationship can be a pre-set mapping relationship. For example, the data storage remaining category quantity difference and the impact factor corresponding to the unit value of the data storage remaining category quantity difference preset in the server information management library form a mapping set, and the real-time data storage remaining category quantity difference is brought into the mapping set to obtain the impact factor corresponding to the unit value of the data storage remaining category quantity difference. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0123] g1 is the impact factor corresponding to the actual operation evaluation value unit value predefined in the server information management library.

[0124] In a specific embodiment, the impact factor corresponding to the actual operation evaluation value unit value can be directly obtained from the server information management library during use. The factor represents the numerical value of the degree of influence on the storage performance evaluation index of the server storage space in the process of analyzing the storage performance of the server storage space. The corresponding relationship can be a pre-set mapping relationship. For example, the actual operation evaluation value and the impact factor corresponding to the actual operation evaluation value unit value preset in the server information management library form a mapping set, and the real-time actual operation evaluation value is brought into the mapping set to obtain the impact factor corresponding to the actual operation evaluation value unit value. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this embodiment, its value range is (0, 1).

[0125] In this embodiment, data throughput measures the server's ability to process data per unit time. Given a certain concurrent access capability, a shorter response time generally means higher throughput because the server can process more requests in a shorter time. The difference between the expected and actual number of remaining data storage categories reflects the effectiveness of the data deletion policy. If the actual value is lower than the expected value, it may indicate that the data deletion policy is not effective in actual application or there are implementation obstacles, such as insufficient data deletion permissions or concerns about the risk of accidental data deletion. The difference in space occupancy before and after space optimization reflects the effectiveness of the space optimization measure. The smaller the difference between the space optimization value and the preset space optimization reference value, the more effective the optimization measure is, and the storage space occupancy can be significantly reduced. Conversely, more in-depth analysis and optimization may be required. The smaller the difference between the space optimization value and the preset space optimization reference value, the more effective the data deletion policy is in actual application, so that the difference between the expected and actual number of remaining data storage categories is reduced. In addition, the optimized storage space can improve the server's data processing capability because more storage space means more data can be cached or quickly accessed, which may increase throughput and reduce response time.

[0126] In this embodiment, the actual operation evaluation value reflects the execution efficiency and effect of the data deletion preset policy in actual application. If the deletion efficiency is high, it means that the server storage space can be released more effectively and the space utilization of the server storage space can be improved, thereby increasing the difference in space occupancy before and after space optimization, thereby enhancing the storage performance of the server storage space; in addition, if the actual occupancy of the central processing unit is large during the execution of the data deletion preset policy, while reducing the actual operation evaluation value of the data deletion preset policy, it may affect the read and write speed of the server storage system. For example, when a large amount of data is deleted, it will trigger the server system to call more central processing unit resources, resulting in a temporary decline in read and write performance.

[0127] Furthermore, the space optimization value of the server storage space is analyzed in the following specific process:

[0128] The space occupancy of the server storage space at the start time of space optimization is ratioed to the rated space of the server storage space, and recorded as the initial space occupancy of the server storage space.

[0129] The space occupancy rate of the server storage space refers to the ratio of the used storage space to the total storage space in the server.

[0130] The space occupied by the server storage space at the end of the space optimization is compared with the rated space of the server storage space, and the ratio is recorded as the final space occupancy rate of the server storage space.

[0131] The initial space occupancy rate of the server storage space is processed with the final space occupancy rate of the server storage space to obtain the space optimization value of the server storage space.

[0132] Specifically, the feedback on the optimization of the server storage space is provided in the following steps:

[0133] The storage performance evaluation index of the server storage space is verified with the storage performance evaluation index preset value predefined in the server information management library to generate a storage space optimization label. If the storage performance evaluation index of the server storage space is greater than or equal to the storage performance evaluation index preset value, the storage space optimization label is defined as a storage space optimization qualified label.

[0134] If the storage performance evaluation index of the server storage space is less than the preset storage performance evaluation index value, the storage space optimization tag is defined as a storage space optimization abnormality tag, thereby providing feedback on the optimization of the server storage space.

[0135] The specific process of the above feedback is: if the above storage space optimization process generates a storage space optimization exception label, the optimization exception status of the storage space is displayed through the visualization platform to which the server belongs, etc., so as to remind the management personnel of the storage space optimization exception, and generate an exception identification report so that the management personnel can take necessary measures to resolve the exception.

[0136] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. A method for optimizing server storage space, characterized in that: include: S1. The server receives a storage space optimization instruction, synchronously obtains the data access frequency of the server storage space, and compares it with the predefined data access reference frequency to obtain the server storage space to be optimized data set; S2. The server's memory release mechanism detects the predefined data deletion policy, obtains detection information about the data deletion policy, and, based on the dataset requiring optimization of the server's storage space, determines a feasibility evaluation indicator for the data deletion policy. The feasibility evaluation indicator is then compared with the predefined feasibility evaluation preset indicators to obtain a comparison result. S3. Based on the comparison results, the data deletion policy is recorded as the data deletion preset policy. The server then executes the storage space optimization instruction based on the data deletion preset policy. Specifically, the server deletes the data set that needs to be optimized in the server storage space based on the data deletion preset policy, and determines the storage performance of the server storage space after the data deletion. The storage performance evaluation index of the server storage space is obtained, and feedback is provided on the optimization of the server storage space. The feasibility evaluation index of the data deletion strategy is determined as follows: Obtaining data deletion policy detection information, including the data deletion speed of the data deletion policy within the feasibility assessment period, the data deletion time required by the data deletion policy within the feasibility assessment period, the number of concurrent data deletions by the data deletion policy, and the number of compatible data deletion categories of the data deletion policy; Obtain the number of data categories in the data set that needs to be optimized in the server storage space, and combine it with the detection information of the data deletion policy to obtain the feasibility evaluation index of the data deletion policy through comprehensive analysis; The feasibility evaluation index of the data deletion strategy is as follows: Where, is the feasibility evaluation index of the data deletion strategy, The data deletion speed of the data deletion policy within the feasibility assessment period. The feasibility assessment cycle starts at The feasibility assessment cycle ends at The time required for data deletion within the feasibility assessment cycle of the data deletion policy. The number of parallel data deletions for the data deletion policy. The number of data deletion categories compatible with the data deletion policy. The number of data categories in the dataset that needs to be optimized for server storage space, The weight factor corresponding to the total amount of data deleted predefined in the server information management library, The weight factor corresponding to the time required for data deletion predefined in the server information management library. The weight factor corresponding to the number of parallel data deletions predefined in the server information management library. The impact factor corresponding to the unit value of the compatible quantity difference of the data deletion category predefined in the server information management library.

2. The method for optimizing server storage space according to claim 1, characterized in that: The specific analysis process of obtaining the data set that needs to be optimized for the server storage space is as follows: Compare the data access frequencies of the server storage space with the predefined data access reference frequency. If the data access frequency of the server storage space is less than or equal to the data access reference frequency, the data corresponding to the data access frequency of the server storage space is recorded as the data that needs to be optimized in the server storage space. In this way, the data set that needs to be optimized in the server storage space is obtained by statistics.

3. The method for optimizing server storage space according to claim 1, characterized in that: The feasibility evaluation index of the data deletion policy is determined and compared with the predefined feasibility evaluation preset index to obtain a comparison result. The specific comparison process is as follows: Comparing the feasibility evaluation index of the data deletion policy with the predefined feasibility evaluation preset index to obtain comparison results, namely a first comparison result and a second comparison result; The first comparison result is that the feasibility evaluation index of the data deletion policy is greater than a preset feasibility evaluation index; The second comparison result is that the feasibility evaluation index of the data deletion policy is less than or equal to a preset feasibility evaluation index.

4. The method for optimizing server storage space according to claim 1, characterized in that: The data deletion operation is performed on the data set that needs to be optimized in the server storage space by using the data deletion preset policy, specifically: If the comparison result shows that the feasibility evaluation index of the data deletion policy is greater than the preset feasibility evaluation index, the data deletion policy is directly recorded as the preset data deletion policy, and the data deletion operation is performed on the data set that needs to be optimized in the server storage space; If the comparison result shows that the feasibility evaluation index of the data deletion policy is less than or equal to the feasibility evaluation preset index, the feasibility evaluation index of the data deletion policy is differenced with the feasibility evaluation preset index to obtain the feasibility evaluation index deviation value of the data deletion policy, and matched with the policy correction data set corresponding to each feasibility evaluation index deviation interval preset in the server information management library, thereby obtaining the policy correction data set of the data deletion policy, and finally correcting the data deletion policy through the policy correction data set, and recording the corrected data deletion policy as the data deletion preset policy, so as to perform data deletion operations on the data set that needs to be optimized in the server storage space.

5. The method for optimizing server storage space according to claim 1, characterized in that: The storage performance determination of the server storage space after data deletion specifically involves obtaining storage performance information of the server storage space, including the data reading speed of the server storage space during the performance evaluation period, the data writing speed of the server storage space during the performance evaluation period, and the data storage response time of the server storage space during the performance evaluation period.

6. The method for optimizing server storage space according to claim 1, characterized in that: The storage performance evaluation index of the server storage space is evaluated as follows: The usage of the server storage space during the space optimization cycle is monitored through a monitoring mechanism, thereby obtaining the space occupancy of the server storage space at the start time of the space optimization, the space occupancy of the server storage space at the end time of the space optimization, and the rated space of the server storage space, and performing data processing to obtain the space optimization value of the server storage space; Obtain the number of data storage categories of the server storage space at the start time of space optimization and the number of remaining data storage categories of the server storage space at the end time of space optimization; Based on the data reading speed of the server storage space during the performance evaluation period, the data writing speed of the server storage space during the performance evaluation period, the data storage response time of the server storage space during the performance evaluation period, the number of data storage categories of the server storage space at the start time of space optimization, the number of remaining data storage categories of the server storage space at the end time of space optimization, the number of data categories of the data sets that need to be optimized in the server storage space, and the space optimization value of the server storage space, a comprehensive analysis is conducted to obtain the storage performance evaluation index of the server storage space.

7. The method for optimizing server storage space according to claim 6, characterized in that: The specific analysis process of the space optimization value of the server storage space is as follows: The space occupied by the server storage space at the start time of space optimization is compared with the rated space of the server storage space, and the ratio is recorded as the initial space occupancy rate of the server storage space; The space occupied by the server storage space at the end of space optimization is compared with the rated space of the server storage space, and the ratio is recorded as the final space occupancy rate of the server storage space. The initial space occupancy rate of the server storage space is processed with the final space occupancy rate of the server storage space to obtain the space optimization value of the server storage space.

8. The method for optimizing server storage space according to claim 1, characterized in that: The feedback on the optimization of the server storage space is specifically conducted as follows: The storage performance evaluation index of the server storage space is verified with a predefined storage performance evaluation index preset value to generate a storage space optimization label. If the storage performance evaluation index of the server storage space is greater than or equal to the storage performance evaluation index preset value, the storage space optimization label is defined as a storage space optimization qualified label. If the storage performance evaluation index of the server storage space is less than the preset storage performance evaluation index value, the storage space optimization tag is defined as a storage space optimization abnormality tag, thereby providing feedback on the optimization of the server storage space.

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