Method and device for predicting storage capacity of large mainframe after commissioning

Abstract

The present invention provides a method and device for predicting storage capacity after a large-scale mainframe is put into production, which relates to the financial field. The method comprises: determining the storage information statistical time point before and after the large-scale mainframe is put into production according to the large-scale mainframe production drill time; collecting the storage data information before and after the large-scale mainframe is put into production according to the determined storage information statistical time point before and after the large-scale mainframe is put into production; generating the storage capacity prediction data after the large-scale mainframe is put into production according to the collected storage data information before and after the large-scale mainframe is put into production and the large-scale mainframe environment parameters after production. By analyzing and comparing the capacity situation before and after the test environment production drill, the performance capacity risk that may occur in the production after production is predicted, and various storage growth requirements that appear after production are prompted, and sufficient space is reserved for production.

Description

Technical Field

[0001] The present invention relates to computer technology, and in particular to a method and device for predicting storage capacity of a large mainframe after it is put into production. Background Art

[0002] In the financial technology field dominated by banks, the security and reliability of services are crucial to providing customers with guaranteed financial services. IBM mainframes are widely used to build the basic service architecture of the banking industry due to their unparalleled stability and security, and powerful parallel data processing capabilities. At present, most banks use IBM mainframes to build their core transaction information and customer information processing systems. Compared with common personal computers, minicomputers or cloud servers, mainframes require a lot of proprietary knowledge in development and daily operation and maintenance due to their integrated packaging of hardware and proprietary software, and are highly professional. Its operating system is z / OS. This operating system is not equipped with an intuitive graphical processing interface similar to Windows, and the modules running on it to provide various functions and resource configurations (such as middleware, database software, etc.) are also unique.

[0003] In the daily operation and maintenance of IBM mainframes, capacity changes are an important indicator to pay attention to. The trend analysis of capacity changes is crucial for predicting possible performance risks in advance. Currently, existing performance capacity prediction methods all use historical data as training sets, build mathematical models through statistical methods, and predict future capacity change trends in the same environment. The estimation of capacity changes relies on mathematical models and is insufficient to cope with real-time changes. Summary of the invention

[0004] In view of the defects in the prior art in predicting the performance capacity of a large mainframe, the present invention provides a method for predicting the storage capacity of a large mainframe after it is put into production, comprising:

[0005] Determine the storage information statistics time point before and after the mainframe production drill according to the mainframe production drill time;

[0006] Collect storage data information before and after the production drill of the mainframe according to the determined storage information statistics time point before and after the production drill of the mainframe;

[0007] The storage capacity prediction data after the mainframe is put into production is generated based on the collected storage data information before and after the mainframe production drill and the environmental parameters of the mainframe after production.

[0008] In the embodiment of the present invention, the storage information statistical time points before and after the commissioning drill include: a time point before the commissioning drill and a time point after the commissioning drill; wherein,

[0009] The time point before the production drill includes: the time point when the last batch of operations of the large mainframe database ends before the production drill;

[0010] The time point after the production drill includes: the time point when the first batch of operations of the large mainframe database ends after the production drill.

[0011] In the embodiment of the present invention, the stored data information includes: table space parameter statistics information, database file information, batch file information, migration file information and shared file information; wherein,

[0012] The table space parameter statistics include: database name, table space name, number of partitions, number of active pages, number of pages where active rows are located;

[0013] The database file information includes: data collection date, data collection time, environmental information, table name, database name and the number of allocated tracks;

[0014] The batch file information includes: data collection date, data collection time, batch file name, number of allocated tracks and data collection type.

[0015] In an embodiment of the present invention, generating storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the mainframe environment parameters after the production includes:

[0016] Determine storage data analysis data based on storage data information before and after the commissioning drill of the mainframe; wherein the storage data analysis data includes: table space information, database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data;

[0017] The storage capacity forecast data after the mainframe is put into production is determined based on the determined storage data analysis data and the mainframe environmental parameters collected after production.

[0018] In the embodiment of the present invention, the determination of storage data analysis data based on storage data information before and after the commissioning drill of the mainframe includes:

[0019] Determine the table space usage rate according to the table space parameter statistical information, and obtain the table space information that meets the preset table space usage rate threshold condition;

[0020] According to the database file information and batch file information before and after the production drill, the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data are determined respectively.

[0021] In an embodiment of the present invention, the method of determining the storage capacity prediction data after the mainframe is put into production based on the determined storage data analysis data and the mainframe environment parameters after the mainframe is put into production includes:

[0022] Determine, based on the table space information, a table space that meets a preset table space usage rate threshold condition after the mainframe is put into production;

[0023] The database file size, batch file size, migration file size and shared file size data of the mainframe after it is put into production are determined based on the mainframe environment parameters and the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data; the mainframe environment parameters include: the original table file size data of the mainframe after it is put into production, and the original batch file size data of the mainframe after it is put into production.

[0024] At the same time, the present invention also provides a storage capacity prediction device for a large mainframe after it is put into production, comprising:

[0025] A time node determination module is used to determine the storage information statistics time points before and after the mainframe production drill according to the mainframe production drill time;

[0026] A data collection module is used to collect storage data information before and after the commissioning drill of the mainframe according to the determined storage information statistics time point before and after the commissioning drill of the mainframe;

[0027] The data analysis module is used to generate storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the mainframe environment parameters after the production.

[0028] In the embodiment of the present invention, the data analysis module includes:

[0029] An analysis data determination unit is used to determine storage data analysis data according to storage data information before and after the commissioning drill of the mainframe; wherein the storage data analysis data includes: table space information, database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data;

[0030] The prediction unit is used to determine the storage capacity prediction data after the mainframe is put into production according to the determined storage data analysis data and the mainframe environmental parameters collected after the production.

[0031] At the same time, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the above method is implemented when the processor executes the computer program.

[0032] At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program for executing the above method.

[0033] The present invention provides a method and device for predicting the storage capacity of a large-scale mainframe after it is put into production. Storage data information before and after the production drill of the large-scale mainframe is collected according to the determined storage information statistical time point before and after the production drill of the large-scale mainframe, and storage capacity prediction data after the production of the large-scale mainframe is generated according to the collected storage data information before and after the production drill of the large-scale mainframe and the environmental parameters of the large-scale mainframe after production. Aiming at the capacity estimation demand of the large-scale mainframe, the present invention proposes a method and device for predicting the storage of the mainframe based on the association of the test environment and the production environment. By analyzing and comparing the capacity situation before and after the production drill of the test environment, the performance capacity risk that may occur in the production after production is predicted. The present invention mainly organizes various storage information during the production drill of the test environment according to the characteristics that the test environment is ready for the new version in advance of the production environment, analyzes its growth trend, and then prompts various storage growth requirements after the production is put into production based on the existing data volume of the production, prompting the production to reserve sufficient space. The present invention can effectively deal with changes caused by real-time factors such as program iteration and user information change, and avoids the risk of excessive load rate of the application system due to the exhaustion of storage capacity of the system, and finally causing system failure by predicting the capacity change trend.

[0034] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A flow chart of a method for predicting storage capacity of a large mainframe after it is put into production provided by the present invention;

[0037] Figure 2 is a block diagram in an embodiment of the present invention;

[0038] Figure 3 is a block diagram in an embodiment of the present invention;

[0039] Figure 4 is a block diagram in an embodiment of the present invention;

[0040] Figure 5 It is a schematic diagram of a flow chart in an embodiment of the present invention;

[0041] Figure 6 A block diagram of a device for predicting storage capacity of a large mainframe after it is put into production provided by the present invention;

[0042] Figure 7 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0044] In response to the capacity estimation requirements of large hosts, the present invention proposes a host storage estimation method based on associating test environment and production environment. By analyzing and comparing the capacity of the test environment before and after the production drill, the performance capacity risk that may occur in production after production is predicted.

[0045] like Figure 1 As shown, the present invention provides a method for predicting storage capacity of a large mainframe after it is put into production, which includes:

[0046] Step S101, determining the storage information statistics time points before and after the mainframe production drill according to the mainframe production drill time;

[0047] Step S102, collecting storage data information before and after the commissioning drill of the mainframe according to the determined storage information statistics time point before and after the commissioning drill of the mainframe;

[0048] Step S103, generating storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the mainframe environment parameters after the production.

[0049] The present invention determines the storage information statistical time point before and after the production drill through the production drill time, collects the storage data information before and after the production drill of the large mainframe, and generates the storage capacity prediction data after the large mainframe is put into production according to the collected storage data information before and after the production drill and the large mainframe environmental parameters after production. According to the characteristics that the test environment is ready for the new version earlier than the production environment, the various storage information during the production drill of the test environment is sorted out, and its growth trend is analyzed, and then based on the existing production data volume, various storage growth demands that appear after the production is put into production are prompted, and sufficient space is reserved for production. The present invention can effectively deal with changes caused by real-time factors such as program iterations and user information changes. By predicting the capacity change trend, it avoids the risk of excessive application system load rate and ultimately system failure due to exhaustion of storage capacity in the system.

[0050] Specifically, in the embodiment of the present invention, the storage information statistical time points before and after the commissioning drill include: a time point before the commissioning drill and a time point after the commissioning drill.

[0051] In the embodiment of the present invention, the time point before the production drill includes: the time point when the last batch of operations of the large mainframe database ends before the production drill;

[0052] The time point after the production drill includes: the time point when the first batch of operations of the large mainframe database ends after the production drill.

[0053] The time point before the production drill is the start time point of version capacity statistics after the data recovery is completed and before the version is installed. In one embodiment of the present invention, the mainframe database includes: a domestic core database and an overseas database.

[0054] The specific method for selecting the time point before the production drill for domestic core databases and overseas databases is as follows:

[0055] Domestic core database:

[0056] Statistics start time: after the last batch of operations before production is completed (ENDWTOR);

[0057] Overseas database:

[0058] Statistics start time: after the last batch of work before production is completed (ENDWTOR); in multiple production drills, this time is the end time of the data delivery batch of the previous production drill;

[0059] The time point after the rehearsal is the end time point of the first batch of operations of the large host database after the production drill. The end time point of the version capacity statistics is the end time point after the first batch of the version is installed. The selection method is as follows:

[0060] Domestic core database:

[0061] Statistical end time point: after the first batch of operations after production is completed (ENDWTOR);

[0062] Overseas database:

[0063] Statistical end time point: after the first batch of operations after production is completed (ENDWTOR);

[0064] When the present invention is implemented, special time points such as the end of the month, the end of the year, and the quarterly interest settlement should be avoided as much as possible when determining the time point for production. If there are special days, the estimates before and after should be kept consistent as much as possible.

[0065] In the embodiment of the present invention, the stored data information includes: table space parameter statistics information, database file information, batch file information, migration file information and shared file information; wherein,

[0066] The table space parameter statistics include: the database name of the table space, the table space name, the number of partitions, the number of active pages, and the number of pages where active rows are located;

[0067] The database file information includes: the date and time of collection of the database, environmental information, table name, database name and the number of allocated tracks;

[0068] The batch file information includes: the batch file collection date, collection time, batch file name, allocated track number and collection type.

[0069] In an embodiment of the present invention, generating storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the mainframe environment parameters after the production includes:

[0070] Determine storage data analysis data based on storage data information before and after the commissioning drill of the mainframe; wherein the storage data analysis data includes: table space usage, database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data;

[0071] The storage capacity forecast data after the mainframe is put into production is determined based on the determined storage data analysis data and the mainframe environmental parameters after it is put into production.

[0072] In the embodiment of the present invention, the table space usage rate is determined according to the collected table space parameter statistical information;

[0073] Specifically, in the embodiment of the present invention, the calculation formula of the table space usage rate is:

[0074] PCT_NACTIVE=DEC(PGSIZTES*SINZAECTIVE,22,8)

[0075] Among them, TSSIZE refers to the tablespace size, PGSIZE refers to the page size (usually 4, 8, 16 or 32KB); NACTIVE refers to the number of active pages, and DEC refers to the precision requirement, which can be customized according to actual conditions. In the embodiment of the present invention, the tablespace size, page size and active page number can be determined according to the database name DBNAME, tablespace name, number of partitions, number of active pages NACTIVE, and number of pages where active rows are located NPAGES in the collected tablespace parameter statistics, which will not be repeated here.

[0076] In an embodiment of the present invention, determining the incremental data of the database file after the production drill based on the collected database file information specifically includes:

[0077] According to the collected database file information, the database file size increment before and after the performance is determined. In this embodiment, the database file size increment before and after the performance includes: the total database file size increment and the single database file with a larger increment. Specifically,

[0078] Determine the total size increment of the database file before and after the show:

[0079]

[0080] Wherein, n1 and n2 refer to the total number of database files at the time points before and after the casting, respectively. The total number of database files n1 and n2 at the time points before and after the casting are determined according to the aforementioned collected database file information;

[0081] TRKS_ALLOC DB Refers to the number of tracks allocated for the database file.

[0082] Determine the database files with large increments before and after the show:

[0083] In this embodiment, the database files with larger increments mainly refer to the statistics of the storage growth of all database files before and after the show (calculated in GB), focusing on analyzing the top ten database files with storage increments in each environment (domestic personal, corporate and overseas partitions, etc.).

[0084] Similarly, in the embodiment of the present invention, determining the incremental data of batch files after the production drill based on the collected batch file information specifically includes:

[0085] Determine the growth of batch file size before and after the show, including: the total size increment of batch files and the single batch file with a large increment. The specific statistical information is as follows:

[0086] Determine the total size increment of batch files before and after the show:

[0087]

[0088] Wherein, n1 and n2 refer to the total number of batch files at the time points before and after the casting, respectively. In this embodiment, the total number of database files n1 and n2 at the time points before and after the casting are determined according to the aforementioned collected batch file information;

[0089] TRKS_ALLOC PL Refers to the number of tracks allocated for batch files.

[0090] Identify batch files with large increments before and after the show.

[0091] Batch files with large increments mainly refer to statistics on the storage growth of all batch files before and after the show (calculated in GB), with a focus on analyzing the top ten batch files with storage increments in each environment (domestic personal, corporate and overseas partitions, etc.). If the total size increment of batch files is too large compared with historical situations, you can sort by increment, query more files, do statistical screening by application, locate specific application information, and notify production of the growth percentage of batch files for this application.

[0092] Similarly, the incremental data of batch files and the incremental data of shared files after the production drill are determined according to the formula (1) similar to the above.

[0093] Specifically, the acquired batch file information is queried according to the specific type of migration files and the specific type of shared files to determine the migration file information and the shared file information, thereby determining the number of files and the number of allocated tracks of the migration files and the shared files before and after the production drill based on the determined migration file information and the shared file information, thereby determining the incremental data of the migration files and the shared files based on the number of files and the number of allocated tracks of the migration files and the shared files before and after the production drill.

[0094] The database file size, batch file size, migration file size and shared file size data after the mainframe is put into production are determined based on the mainframe environment parameters and the test environment database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data; the mainframe environment parameters include: the original table file size data before the mainframe is put into production, and the original batch file size data before the mainframe is put into production.

[0095] The present invention provides a method for predicting the storage capacity of a large host after it is put into production, and proposes a method for estimating host storage based on associating a test environment with a production environment. By analyzing and comparing the capacity conditions before and after the test environment is put into production, the performance capacity risk that may occur in production after it is put into production is predicted. The prediction method mainly organizes various storage information during the test environment production drill according to the characteristics that the test environment is ready for a new version earlier than the production environment, analyzes its growth trend, and then based on the existing production data volume, prompts various storage growth requirements that appear after production is put into production, and prompts production to reserve sufficient space. The problem that the existing method cannot introduce information elements such as version program changes and user information iterations due to the use of only historical data in the prior art, and the estimation of capacity changes relies on mathematical models, and the ability to cope with real-time changes is insufficient. The present invention can effectively cope with changes caused by real-time factors such as program iterations and user information changes, and avoids the risk of excessive application system load rate and ultimately system failure due to system storage capacity exhaustion by predicting the capacity change trend.

[0096] The embodiment of the present invention also provides a storage estimation system based on an IBM mainframe, which estimates storage conditions in different production environments by performing capacity collection and storage analysis in the z / OS operating system of the IBM mainframe.

[0097] like Figure 2 As shown, the storage system provided in this embodiment mainly includes: a time node unit 001, a data collection unit 002, and a data analysis unit 003.

[0098] Among them, the time node unit 001 is used to determine the key time points for analyzing data; the data collection unit 002 collects real-time statistical information and file information of the database by running host jobs, and saves the results in a dedicated database; the data analysis unit 003 organizes and analyzes the information collected by unit 002 to generate storage estimation data.

[0099] The detailed description of each unit of this embodiment is as follows:

[0100] 1. Time node unit 001:

[0101] This unit is a key time point determined for collecting and comparing storage information, and in this embodiment, it mainly includes three nodes: a production rehearsal time point, a pre-production time point, and a post-production time point.

[0102] 1. Time of the drill:

[0103] That is, determine the time point for the test environment to conduct production drills;

[0104] 2. Time before the performance:

[0105] The time point before production is the end time point of the last batch of operations of the large mainframe database before the production drill; in this embodiment, the start time point of version capacity statistics is the time before the version is installed after the data recovery is completed;

[0106] The specific selection method is as follows:

[0107] A. Domestic core database:

[0108] Statistics start time point: the time after the ENDWTOR operation of the last batch before production is completed;

[0109] B. Overseas database:

[0110] Statistics start time point: the time after the ENDWTOR operation of the last batch before production is completed;

[0111] In multiple production drills, this time point is the end time point of the batch delivery of the previous production drill;

[0112] 3. Time point after the performance:

[0113] The time point after the production drill includes: the time point when the first batch of jobs of the large mainframe database ends after the production drill.

[0114] The end time of version capacity statistics is the time when the first batch after version installation is completed;

[0115] The specific selection method is as follows:

[0116] A. Domestic core database:

[0117] Statistical end time point: the time after the ENDWTOR operation of the first batch after production is completed;

[0118] B. Overseas database:

[0119] Statistical end time point: the time after the ENDWTOR operation of the first batch after production is completed.

[0120] When determining the time of production, special time points such as the end of the month, the end of the year, and quarterly interest settlement should be avoided as much as possible. If there are special days, the estimates before and after should be kept as consistent as possible.

[0121] 2. Data Collection Unit 002:

[0122] This unit is used to collect storage data information at key time points. In z / OS, data exists in the form of files, called Data Sets, and the naming method used by Data Sets is XXX.XXX.XXX. The present invention uses the Data Set naming conventions of different data, lists the Data Sets covered by various types of information, writes host data collection operations, classifies and collects relevant storage information, and outputs various types of information into custom tables after collection, and stores them in a dedicated collection information database.

[0123] like Figure 3 As shown, in the embodiment of the present invention, the data collection unit 002 mainly includes: table space usage collection device 201, database file collection device 202, batch file collection device 203, migration file collection device 204, and shared file collection device 205. The detailed description of each device is as follows:

[0124] 1. Table space usage collection device 201:

[0125] The SYSTABLESPACESTATS table of the mainframe contains real-time statistics of the table space. A data collection job is written to extract statistical data from the table at time point t1 before the show and time point t2 after the show, including information such as database name DBNAME, table space name TSNAME, number of partitions PARTITION, number of active pages NACTIVE, number of pages where active rows are located NPAGES, etc.

[0126] In the embodiment of the present invention, the database on which the statistical table space usage rate and the database file are based is the same, including the bank's personal, corporate, overseas and other databases. Each database requires both statistical table space usage rate and statistical database file (also called table file) size.

[0127] 2. Database file collection device 202:

[0128] Organize all naming formats of the test environment database files, write database file collection jobs, run the jobs at time t1 before the test and time t2 after the test, and output relevant information to the database. The specific information collected includes: collection date REC_DATE, collection time REC_TIME, environment information SSID, table name OBNAME, database name DBNAME, number of allocated tracks TRKS_ALLOC DB And other information.

[0129] 3. Batch file collection device 203:

[0130] Organize all naming formats of batch files in the test environment, write batch file collection jobs, run the jobs at time t1 before the show and time t2 after the show, and output relevant information to the database. The specific collected information includes: batch file collection date REC_DATE, collection time REC_TIME, batch file name DSNAME, number of allocated tracks TRKS_ALLOC PL , collection type REC_TYPE and other information.

[0131] 4. Transition file collection device 204:

[0132] The file naming format of the test environment migration line is sorted out, and the collection type REC_TYPE is uniformly defined as AYX. The collection operation in the batch file collection device 203 is used to filter all file information with REC_TYPE AYX.

[0133] 5. Shared file collection device 205:

[0134] By utilizing the collection operation of the batch file collection device 203, the batch file name DSNAME and the corresponding collection type REC_TYPE are screened according to the unique naming format of the shared file.

[0135] 3. Data Analysis Unit 003:

[0136] The data analysis unit 003 uses the timestamps before and after the production determined by the unit 001 to write a statistical SQL. The SQL mainly extracts data from the database output by the data collection unit 002. The specific extraction method is: using the data collection date REC_DATE, the data collection time REC_TIME and the environmental information SSID as screening conditions, querying the relevant tables output by the data collection unit 002, and outputting the various query results to different tables, so as to sort out the estimated project data list. Then compare the storage information collected before and after the production drill, and then analyze whether the information with large increments is caused by the production according to the MD5 comparison program, combing the version transformation content, querying the relevant tables and file change records, querying the number of records added, deleted, modified and checked in the relevant tables, and judging whether the data growth is related to the stress test.

[0137] like Figure 4 As shown, in the embodiment of the present invention, the data analysis unit 003 mainly includes: a table space usage analysis device 301, a database file analysis device 302, a batch file analysis device 303, a migration file analysis device 304, and a shared file analysis device 305. The detailed description of each device is as follows:

[0138] 1. Table space usage analysis device 301:

[0139] The device mainly analyzes the situation where the table space usage rate exceeds 70% after the casting and the situation where the table space usage rate increases by more than 10% at the time points before and after the casting. The specific analysis method is as follows:

[0140] The calculation formula for the table space usage of a large host is:

[0141]

[0142] Among them, TSSIZE refers to the tablespace size, PGSIZE refers to the page size (usually 4, 8, 16 or 32KB), NACTIVE refers to the number of active pages, and DEC refers to the precision requirement, which can be customized according to actual conditions.

[0143] According to this formula, all tablespace information for the following two situations is counted:

[0144] 1) PCT_NACTIVE t2 >70%

[0145] 2) PCT_NACTIVE t2 -PCT_NACTIVE t1 >10%

[0146] Based on the statistical information, after excluding influencing factors such as test environment stress testing, the table space usage in the production environment can be directly predicted based on the collected table space usage.

[0147] Determine the table space usage rate in the production drill. According to the preset table space usage rate threshold, determine the table space whose table space usage rate is greater than the preset threshold during the production drill, record the table space information, and thus determine the table space whose table space usage rate is greater than the preset threshold after production.

[0148] 2. Database file analysis device 302:

[0149] The device mainly analyzes the growth of database file size before and after the show, including the total size increment of database table files and the single database file with a large increment. Since the storage format of the database file of the large host on the disk is calculated in units of tracks, it is first necessary to convert it into the commonly used storage unit GB. The specific statistical information is as follows:

[0150] 1) Total size increment of table files before and after the show (unit: GB):

[0151]

[0152] Among them, n1 and n2 refer to the total number of database files before and after the projection, and TRKS_ALLOC refers to the number of tracks allocated to the database files.

[0153] The growth percentage is calculated based on this increment, and the original table file size of the production environment is calculated according to this percentage to predict the table file size after the production environment is put into production.

[0154] 2) Database files with large increments before and after the show:

[0155] Database files with large increments mainly refer to statistics on the storage growth of all database files before and after the show (calculated in GB), with a focus on analyzing the top ten database files with the largest storage increments in each environment (domestic personal, corporate and overseas partitions, etc.).

[0156] Calculate the growth percentage of these database files, and analyze whether the growth percentage is reasonable by combining the table change records with the number of additions, deletions, modifications, and queries during the casting period. If the growth percentage is large, calculate the file size of the table in the production environment according to this percentage to predict the file size of the table after the production environment is put into production.

[0157] 3. Batch file analysis device 303:

[0158] The device mainly analyzes the growth of batch file size before and after the show, including: the total size increment of batch files, and the single batch file with a larger increment.

[0159] The specific statistics are as follows:

[0160] 1) Total size increment of batch files before and after the show (unit: GB):

[0161]

[0162] Among them, n1 and n2 refer to the total number of batch files at the time points before and after the projection, and TRKS_ALLOC refers to the number of tracks allocated to the batch files.

[0163] The growth percentage is calculated based on this increment, and the original batch file size of the production environment is calculated according to this percentage to predict the batch file size after the production environment is put into production.

[0164] 2) Batch files with large increments before and after the show:

[0165] Batch files with large increments mainly refer to statistics on the storage growth of all batch files before and after the show (calculated in GB), with a focus on analyzing the top ten batch files with storage increments in each environment (domestic personal, corporate and overseas partitions, etc.). If the total size increment of batch files is too large compared with historical situations, you can sort by increment, query more files, do statistical screening by application, locate specific application information, and notify production of the growth percentage of batch files for this application.

[0166] 4. Transition file analysis device 304:

[0167] The device mainly analyzes the size of the transition files during the casting process. The specific process is: the collected transition files are classified according to the application. Since the transition files are similar to the batch files, the total size increment of the transition files before and after the casting can be calculated with reference to the method of the batch file analysis device 303, that is, the size of the transition files increased during the casting process, and then the project transformation content involved in the application is understood, and it is analyzed whether the transformation content involves migration and whether the size of the transition files is reasonable.

[0168] In one embodiment of the present invention, the specific analysis method is: statistically analyze the amount of historical migration data and the size of storage space, make a fitting model according to the least squares method, and then fit the migration file size according to the amount of migration data involved in the application. The fitting result needs to be dynamically adjusted with reference to the field size and number of fields of the table involved. If the size of the migration file is reasonable, a simple conversion of the data volume of the same application in the production environment can be made to predict the space occupied by the production migration file.

[0169] In addition, the size of Data Set needs to be specified when it is allocated, which requires the use of several related parameters. That is, the size of the first allocation, the second allocation, and the directory area. The so-called first allocation size refers to the size of the Data Set when it is first allocated. During the use of the host, the content of the file will increase. When it exceeds the size of the first allocation, the system will automatically expand it. The size of each expansion is the second allocation size. Since the first and second allocation parameters of the test environment and the production environment are different, it is necessary to select appropriate first and second allocation parameters after the production environment is put into production based on the statistical migration situation.

[0170] 5. Shared file analysis device 305:

[0171] The device analyzes the growth of shared files before and after the launch. For details, please refer to the calculation method of the batch file analysis device 303. After calculating the shared file increment, the shared file size after the production environment is put into production is predicted based on the growth percentage and the original shared file size of the production environment.

[0172] like Figure 5 , which is a schematic diagram of the operation flow of a large host storage estimation system provided by an embodiment of the present invention.

[0173] This embodiment proposes a mainframe storage estimation system based on associating the test environment with the production environment, and realizes the functions of capacity collection and storage analysis in the z / OS operating system. Based on the characteristics of IBM mainframes, this embodiment proposes a storage statistics idea specifically for IBM mainframes. Based on the data before and after the test environment is put into operation, the capacity of the production environment after production can be analyzed and estimated, which can effectively avoid the risk of excessive application system load rate and ultimately system failure due to the exhaustion of storage capacity.

[0174] The embodiment of the present invention also provides a storage capacity prediction device for a large mainframe after it is put into production. Figure 6 Shown include:

[0175] The time node determination module 601 is used to determine the storage information statistics time points before and after the mainframe production drill according to the mainframe production drill time;

[0176] The data collection module 602 is used to collect storage data information before and after the commissioning drill of the mainframe according to the determined storage information statistics time point before and after the commissioning drill of the mainframe;

[0177] The data analysis module 603 is used to generate storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the mainframe environment parameters after the production.

[0178] The storage capacity prediction device for a large mainframe after production provided by the present invention aims at the capacity estimation demand of IBM mainframes, and proposes a mainframe storage estimation device based on associating the test environment and the production environment. By analyzing and comparing the capacity conditions before and after the production drill of the test environment, the performance capacity risks that may occur in the production after production are predicted. The prediction method mainly organizes various storage information during the production drill of the test environment according to the characteristics that the new version is ready in advance of the production environment, analyzes its growth trend, and then prompts various storage growth demands that appear after production is put into production based on the existing data volume of production, prompting production to reserve sufficient space. The present invention can effectively deal with changes caused by real-time factors such as program iteration and user information changes. By predicting the capacity change trend, it avoids the risk of excessive application system load rate and ultimately system failure due to exhaustion of storage capacity in the system.

[0179] For those skilled in the art, the implementation method of the storage capacity prediction device for a large mainframe after it is put into production provided by the present invention can be clearly known through the description of the above embodiments, and will not be described in detail here.

[0180] It should be noted that the method and device for predicting the storage capacity of a large mainframe after it is put into production disclosed in the present invention can be used for predicting the storage capacity of a large mainframe after it is put into production in the financial field, and can also be used for predicting the storage capacity of a large mainframe after it is put into production in any field other than the financial field. The present disclosure does not limit the application field of the method and device for predicting the storage capacity of a large mainframe after it is put into production.

[0181] This embodiment also provides an electronic device, which may be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the electronic device may refer to the embodiments of the aforementioned method and device, the contents of which are incorporated herein, and the repeated parts are not repeated.

[0182] Figure 7 FIG. 6 is a schematic block diagram of a system structure of an electronic device 600 according to an embodiment of the present invention. Figure 7 As shown, the electronic device 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. It should be noted that this figure is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0183] In one embodiment, the storage capacity prediction function of the mainframe after production can be integrated into the central processor 100. The central processor 100 can be configured to perform the following control:

[0184] Determine the storage information statistics time point before and after the mainframe production drill according to the mainframe production drill time;

[0185] Collect storage data information before and after the production drill of the mainframe according to the determined storage information statistics time point before and after the production drill of the mainframe;

[0186] The storage capacity prediction data after the mainframe is put into production is generated based on the collected storage data information before and after the mainframe production drill and the environmental parameters of the mainframe after production.

[0187] In another embodiment, the storage capacity prediction device for the mainframe after it is put into production can be configured separately from the central processor 100. For example, the storage capacity prediction device for the mainframe after it is put into production can be configured as a chip connected to the central processor 100, and the storage capacity prediction function for the mainframe after it is put into production can be realized through the control of the central processor.

[0188] like Figure 7 As shown, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processor 130, a display 160, and a power supply 170. It is worth noting that the electronic device 600 does not necessarily have to include Figure 7 In addition, the electronic device 600 may also include Figure 7 For components not shown, reference may be made to the prior art.

[0189] like Figure 7 As shown, the central processor 100 is sometimes also referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices. The central processor 100 receives inputs and controls the operations of various components of the electronic device 600.

[0190] The memory 140 may be, for example, one or more of a cache, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory or other suitable devices. The above-mentioned information related to the failure may be stored, and a program for executing the relevant information may also be stored. The CPU 100 may execute the program stored in the memory 140 to implement information storage or processing.

[0191] The input unit 120 provides input to the CPU 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0192] The memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, etc. It may also be a memory that saves information even when the power is off, can be selectively erased, and is provided with more data, examples of which are sometimes referred to as EPROMs, etc. The memory 140 may also be some other type of device. The memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application / function storage unit 142, which is used to store application programs and function programs or processes for executing the operation of the electronic device 600 through the central processor 100.

[0193] The memory 140 may also include a data storage unit 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device. The driver storage unit 144 of the memory 140 may include various drivers for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0194] The communication module 110 is a transmitter / receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter / receiver) 110 is coupled to the central processor 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0195] Based on different communication technologies, multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module and / or a wireless LAN module. The communication module (transmitter / receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide an audio output via the speaker 131 and receive an audio input from the microphone 132, thereby realizing a common telecommunication function. The audio processor 130 may include any suitable buffer, decoder, amplifier, etc. In addition, the audio processor 130 is also coupled to the central processor 100, so that the sound can be recorded on the local machine through the microphone 132, and the sound stored on the local machine can be played through the speaker 131.

[0196] An embodiment of the present invention further provides a computer-readable program, wherein when the program is executed in an electronic device, the program enables a computer to execute the method for predicting storage capacity of a large mainframe after production as described in the above embodiment in the electronic device.

[0197] An embodiment of the present invention further provides a storage medium storing a computer-readable program, wherein the computer-readable program enables a computer to execute the post-production storage capacity prediction of a large mainframe as described in the above embodiment in an electronic device.

[0198] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. Many features and advantages of these embodiments are clear from this detailed description, and therefore the appended claims are intended to cover all such features and advantages of these embodiments that fall within their true spirit and scope. In addition, since many modifications and changes are readily apparent to those skilled in the art, the embodiments of the present invention are not intended to be limited to the precise structure and operation illustrated and described, but all suitable modifications and equivalents that fall within their scope may be included.

[0199] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0201] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0202] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1A step that specifies a function in one or more boxes.

[0203] The present invention uses specific embodiments to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for predicting storage capacity of a large mainframe after it is put into production, characterized in that: The method includes: Determine the storage information statistics time point before and after the mainframe production drill according to the mainframe production drill time; Collecting storage data information before and after the mainframe production drill according to the determined storage information statistics time point before and after the mainframe production drill, the storage data information includes: table space parameter statistics information, database file information, batch file information, migration file information and shared file information; Generate storage capacity forecast data after the mainframe is put into production based on the collected storage data information before and after the mainframe production drill and the environmental parameters of the mainframe after production; The method of generating storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe is put into production and the environmental parameters of the mainframe after the mainframe is put into production includes: Determine the table space usage rate according to the table space parameter statistical information, and obtain the table space information that meets the preset table space usage rate threshold condition; According to the database file information and batch file information before and after the production drill, the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data are determined respectively; Determine, based on the table space information, a table space that meets a preset table space usage rate threshold condition after the mainframe is put into production; The database file size, batch file size, migration file size and shared file size data of the mainframe after it is put into production are determined based on the mainframe environment parameters and the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data; the mainframe environment parameters include: the original table file size data of the mainframe after it is put into production, and the original batch file size data of the mainframe after it is put into production.

2. The method for predicting storage capacity of a large mainframe after it is put into production according to claim 1, characterized in that: The storage information statistical time points before and after the production drill include: the time point before the production drill and the time point after the production drill; wherein, The time point before the production drill includes: the time point when the last batch of operations of the large mainframe database ends before the production drill; The time point after the production drill includes: the time point when the first batch of operations of the large mainframe database ends after the production drill.

3. The method for predicting storage capacity of a large mainframe after it is put into production according to claim 1, characterized in that: The table space parameter statistics include: database name, table space name, number of partitions, number of active pages, number of pages where active rows are located; The database file information includes: data collection date, data collection time, environmental information, table name, database name and the number of allocated tracks; The batch file information includes: data collection date, data collection time, batch file name, number of allocated tracks and data collection type.

4. A storage capacity prediction device for a large mainframe after it is put into production, characterized in that: The device comprises: A time node determination module is used to determine the storage information statistics time points before and after the mainframe production drill according to the mainframe production drill time; A data collection module is used to collect storage data information before and after the commissioning drill of the mainframe according to the determined storage information statistical time point before and after the commissioning drill of the mainframe, wherein the storage data information includes: table space parameter statistics information, database file information, batch file information, migration file information and shared file information; A data analysis module is used to generate storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe is put into production and the environmental parameters of the mainframe after the production; The method of generating storage capacity prediction data after the mainframe is put into production based on the collected storage data information before and after the mainframe is put into production and the environmental parameters of the mainframe after the mainframe is put into production includes: Determine the table space usage rate according to the table space parameter statistical information, and obtain the table space information that meets the preset table space usage rate threshold condition; According to the database file information and batch file information before and after the production drill, the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data are determined respectively; Determine, based on the table space information, a table space that meets a preset table space usage rate threshold condition after the mainframe is put into production; The database file size, batch file size, migration file size and shared file size data of the mainframe after it is put into production are determined based on the mainframe environment parameters and the database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data; the mainframe environment parameters include: the original table file size data of the mainframe after it is put into production, and the original batch file size data of the mainframe after it is put into production.

5. The device for predicting storage capacity of a large mainframe after it is put into production as claimed in claim 4, characterized in that: The table space parameter statistics include: database name, table space name, number of partitions, number of active pages, number of pages where active rows are located; The database file information includes: data collection date, data collection time, environmental information, table name, database name and the number of allocated tracks; The batch file information includes: collection date, collection time, batch file name, number of allocated tracks and collection type; The data analysis module includes: An analysis data determination unit is used to determine storage data analysis data according to storage data information before and after the commissioning drill of the mainframe; wherein the storage data analysis data includes: table space information, database file incremental data, batch file incremental data, migration file incremental data and shared file incremental data; The prediction unit is used to determine the storage capacity prediction data after the mainframe is put into production according to the determined storage data analysis data and the mainframe environmental parameters collected after the production.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 3 is implemented.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 3.

8. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 3 are implemented.

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