Cross-platform service adaptation optimization method for PaaS platform replacement

Abstract

The invention relates to the technical field of data processing, in particular to a cross-platform service adaptation optimization method for PaaS platform replacement, which comprises the following steps of: S1, migrating a mirror image file into a mirror image warehouse of a target migration platform so as to deploy the mirror image file to the target migration platform, and recovering the business of an original platform cluster; s2, sequentially migrating file data and object data on which the application component depends, and sequentially migrating a basic component and database data in an original platform cluster; s3, the configuration of the service is adjusted, the service is verified, and the service flow is switched; s4, acquiring data packet loss rates of the mirror image files in a plurality of mirror image migration periods; and S5, judging the stability of adaptive optimization based on the variance of the data packet loss rate of the mirror image file so as to determine whether to execute a stability adjustment mode or not. According to the invention, the stability of adaptation optimization is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a cross-platform service adaptation optimization method for PaaS platform replacement. Background Art

[0002] In the existing technology, with the continuous development of cloud computing technology, computing power (Platform as a Service) as a model of cloud computing provides services for multiple tenants, provides the application development and testing environment and development tools to tenants in the form of interfaces, hides the operating system and hardware information, and provides great help to developers in development and operation and maintenance.

[0003] Different computing platforms provide various functions and services. However, due to the differences between platforms, some applications often encounter service incompatibility issues when migrating from one computing platform to another, and need to be adapted in a certain way. Most of the existing migration methods used to migrate service program information in the applications of computing platforms are still to collect all program information of the initial computing platform that needs to be migrated, and then directly adjust and replace all its information according to the service model of the target computing platform to be migrated, and then migrate it. However, this undoubtedly increases the adaptation workload required for migration between two similar computing platforms.

[0004] Chinese Patent Publication No.: CN114385233A discloses a cross-platform adaptive data processing workflow system and method, the method includes: the client constructs a workflow based on application programming interface calls and canvas dragging, and sends the constructed workflow to the server through the Istio security gateway; based on Kubernetes technology, the server optimizes and adapts the computing environment of the logical operators of the received workflow; according to the obtained optimal computing environment adaptation result, cross-platform communication is performed by binding a cross-platform address through a cross-platform communication data transmission channel to achieve cross-platform data processing. It can be seen that the cross-platform adaptive data processing workflow system and method have the problem of network delay caused by signal attenuation due to network interference, resulting in interruption of mirror transmission and thus a decrease in the stability of adaptation optimization. Summary of the invention

[0005] To this end, the present invention provides a cross-platform service adaptation optimization method for PaaS platform replacement, which is used to overcome the problem in the prior art that signal attenuation caused by network interference leads to network delay, resulting in interruption of mirror transmission and thus reduced stability of adaptation optimization.

[0006] To achieve the above-mentioned purpose, the present invention provides a cross-platform service adaptation optimization method for PaaS platform replacement, comprising: step S1, scheduling the image file of the original platform cluster to the image warehouse of the original platform, migrating the image file to the image warehouse of the target migration platform to deploy the image file to the target migration platform, and restoring the business of the original platform cluster; step S2, using the migrated image file to deploy the application component, migrating the file data and object data that the application component depends on in sequence, and migrating the basic components and database data in the original platform cluster in sequence; step S3, adjusting the configuration of the business and verifying the business, and switching the business traffic; step S4, obtaining the data packet loss rate of the image file in several image migration cycles; step S5, judging the stability of the adaptation optimization based on the variance of the data packet loss rate of the image file to determine whether to execute the stability adjustment method; wherein the stability adjustment method includes adjusting the number of fragments of the data packet, or determining the stability of the data migration according to the average migration time of the application component to determine whether to execute the migration stability adjustment method, The migration stability adjustment method includes adjusting the backup frequency of the application component, or determining the parallel processing data volume of the database data based on the migration conversion duration of the database data.

[0007] Furthermore, in step S5, determining the stability of the adaptation optimization includes: Obtaining the data packet loss rate of the image file in several image migration cycles, and calculating the variance of the data packet loss rate of the image file; Comparing the variance of the data packet loss rate of the image file with a preset first variance and a preset second variance respectively; If the variance of the data packet loss rate of the image file is greater than the preset first variance, it is determined that the stability of the adaptive optimization does not meet the preset standard.

[0008] Furthermore, if the variance of the data packet loss rate of the mirror file is greater than the preset second variance, the number of fragments of the data packet is increased.

[0009] Further, determining the stability of the data migration includes: The variance of the data packet loss rate of the image file is greater than the preset first variance and less than or equal to the preset second variance, and whether the stability of data migration meets the requirements is determined according to the average migration time of the application component; Comparing the average migration duration of the application component with the preset first migration duration and the preset second migration duration respectively; If the average migration duration of the application component is greater than the preset first migration duration, it is determined that the stability of the data migration does not meet the preset standard.

[0010] Furthermore, the increase range of the number of fragments of the data packet is determined by the difference between the variance of the data packet loss rate of the mirror file and the preset second variance.

[0011] Furthermore, the backup frequency of the application component is adjusted, including: If the average migration duration of the application component is greater than the preset first migration duration and less than or equal to the preset second migration duration, the backup frequency of the application component is increased; If the average migration duration of the application component is greater than the preset second migration duration, the parallel processing data volume of the database data is adjusted according to the migration conversion duration of the database data.

[0012] Furthermore, the increase range of the backup frequency of the application component is determined by the difference between the average migration duration of the application component and a preset first migration duration.

[0013] Further, adjusting the amount of parallel processing data of the database data includes: Get the migration conversion time of database data; Comparing the migration conversion time of the database data with the preset conversion time; If the migration conversion time of the database data is longer than the preset conversion time, the amount of parallel processing of the database data is reduced.

[0014] Furthermore, the migration conversion duration of the database data is a ratio of the number of successful database data migrations to the total number of database data migrations.

[0015] Furthermore, the reduction extent of the parallel processing data volume of the database data is determined by the difference between the migration conversion time of the database data and the preset conversion time.

[0016] Compared with the prior art, the beneficial effect of the present invention lies in that the method of the present invention adjusts the number of fragments of the data packet according to the variance of the data packet loss rate of the image file by setting steps S1-S5, and causes signal attenuation due to network interference, resulting in network delay, resulting in image transmission interruption, and thus causing problems in the image migration process. By increasing the number of fragments of the data packet, the influence of interference on a single data packet is reduced. When some data packets are interfered with, only the affected data packets need to be retransmitted. By adjusting the backup frequency of the application component according to the average migration time of the application component, the file is tampered with due to virus invasion, resulting in an error in the format of the file, which causes problems when the configuration file is modified. This will lead to errors in the migration configuration of application components, thereby increasing the migration time of application components. By increasing the backup frequency of application components, the probability of modification errors in configuration files is reduced. By adjusting the parallel processing data volume of database data according to the migration conversion time of database data, since floating-point numbers are represented in binary form in computers, the precision of floating-point numbers is lost, which will be reduced due to rounding errors, resulting in loss of data precision, causing data type mismatch, and then causing data conversion errors, which in turn lead to errors in the migration of database data. By reducing the parallel processing data volume of database data, the probability of resource competition is reduced, the possibility of errors is reduced, and the stability of adaptation optimization is improved.

[0017] Furthermore, the method of the present invention adjusts the number of fragments of the data packet by setting a preset first variance and a preset second variance. Since the network is interfered with and causes signal attenuation, network delays occur, which causes mirror transmission interruption, thereby causing problems in the mirror migration process. By increasing the number of fragments of the data packet, the impact of interference on a single data packet is reduced. When some data packets are interfered with, only the affected data packets need to be retransmitted, thereby further improving the stability of the adaptation optimization.

[0018] Furthermore, the method of the present invention adjusts the backup frequency of application components by setting a preset first migration duration and a preset second migration duration. Due to virus invasion, files are tampered with, resulting in errors in the file format, which leads to problems in modifying the configuration file, and then to errors in the migration configuration of the application component, thereby increasing the migration duration of the application component. By increasing the backup frequency of the application component, the probability of modification errors in the configuration file is reduced, further achieving improved stability for adaptation optimization.

[0019] Furthermore, the method of the present invention adjusts the amount of parallel processing of database data by setting a preset conversion duration. Since floating-point numbers are represented in binary form in a computer, the precision of floating-point numbers is lost, which will be reduced due to rounding errors, resulting in loss of data precision, resulting in data type mismatch, and further resulting in data conversion errors, which in turn lead to errors in the migration of database data. By reducing the amount of parallel processing of database data, the probability of resource competition is reduced, the possibility of errors is reduced, and the stability of adaptive optimization is further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is an overall flow chart of the cross-platform service adaptation optimization method for PaaS platform replacement according to an embodiment of the present invention; Figure 2 It is a logic flow chart of a cross-platform service adaptation optimization method for PaaS platform replacement according to an embodiment of the present invention; Figure 3 A specific flow chart of the process of adjusting the backup frequency of application components in the cross-platform service adaptation optimization method for PaaS platform replacement according to an embodiment of the present invention; Figure 4 The present invention is a specific flow chart of the process of adjusting the parallel processing data volume of database data in the cross-platform service adaptation optimization method for PaaS platform replacement according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0023] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0024] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively an overall flow chart of a cross-platform service adaptation and optimization method for PaaS platform replacement, a logical flow chart, a specific flow chart of a process for adjusting the backup frequency of application components, and a specific flow chart of a process for adjusting the parallel processing data volume of database data in an embodiment of the present invention. A cross-platform service adaptation and optimization method for PaaS platform replacement of the present invention comprises: Step S1, scheduling the image file of the original platform cluster to the image warehouse of the original platform, migrating the image file to the image warehouse of the target migration platform to deploy the image file to the target migration platform, and restoring the service of the original platform cluster; Step S2, deploying the application components using the migrated image files, migrating the file data and object data that the application components depend on in sequence, and migrating the basic components and database data in the original platform cluster in sequence; Step S3, adjusting the configuration of the service, verifying the service, and switching the service traffic; Step S4, obtaining the data packet loss rate of the image file in several image migration cycles; Step S5, judging the stability of the adaptation optimization based on the variance of the packet loss rate of the image file to determine whether to perform the stability adjustment method; The stability adjustment method includes adjusting the number of fragments of the data packet, or determining the stability of data migration according to the average migration time of the application component to determine whether to perform the migration stability adjustment method. The migration stability adjustment method includes adjusting the backup frequency of the application components. Alternatively, the amount of parallel processing of database data is determined based on the migration and conversion time of the database data.

[0026] Specifically, the image file includes the application code, the startup script, and the creation time of the image file.

[0027] Specifically, the services of the original platform cluster include user data, log data, and user permissions.

[0028] Specifically, file data includes application configuration files, log files, and script files.

[0029] Specifically, object data includes product objects, order objects, and order status.

[0030] Specifically, the basic components include container engines, log management services, and monitoring tools.

[0031] Specifically, database data includes user information, order information, and product information.

[0032] Specifically, the parallel processing data volume of database data is the data volume processed simultaneously when the database data is processed.

[0033] Specifically, before establishing the target cluster in the target migration platform, it is necessary to evaluate business resources. The process includes: underlying hardware architecture, cloud service type, resource capacity, cross-regional resources, resource pool planning and creation, container and service network segment and resource specification planning, and database backup strategy planning; Among them, the underlying hardware architecture includes Intel X86CPU and domestically produced CPU; Cloud service types include Kubernetes clusters and configurations, database types and architectures (cluster, master / slave, standalone), and IaaS virtual machines; Resource capacity includes container PV, CPU, and memory; Cross-regional resources include DMZ and intranet areas; cross-data center (businesses have active-standby or active-active solutions); The container and service network segments and resource specification planning include the specifications and sizes of the Kubernetes master nodes and worker nodes.

[0034] Database backup strategy planning, for example, MySQL has one incremental backup per day, one full backup per Monday, and unified backup to the object storage bucket.

[0035] Specifically, Kubernetes clusters, database middleware and other resources are created based on the business resource assessment results. After creation, vulnerability scanning is required based on the resource and component versions, and patch updates are completed in a timely manner.

[0036] Specifically, the upload methods of the image migration process include: (1) Uploading through the PaaS platform requires packaging the container image in advance; (2) Upload the container image through remote commands and obtain user login credentials in advance; (3) Build business container images through pipelines and connect to business application code repositories in advance.

[0037] Specifically, application component migration includes security policy release, configuration file migration and modification, service exposure settings, certificate upload, and domain name resolution. Security policy release application is for some businesses to request access to other businesses, and firewall security policy release application is required, which is applied by the migration project team; configuration file migration is to migrate and upload the business configuration files on the original PaaS platform to the migration target PaaS platform to meet the configuration files required for business operation; configuration file modification is because the platform migration involves new environment and new resources, the business configuration files need to be appropriately modified and updated to meet the business operation; service exposure mode is set to provide access to the outside of the service, and provide access to the business, and the corresponding exposure modes are ClusterIP (Headless), NodePort and providing services to the outside through Ingress. Select the appropriate service exposure mode according to business attributes and requirements; external resource application includes load balancing F5, DNS domain name, certificate and external IP address.

[0038] In step S2, the file data migration method includes: (1) You can use the access rights of the original storage device in the open new environment to switch between reading and writing; (2) You cannot use the original storage device's migration tool to copy it.

[0039] In step S2, the object data migration method includes: To use the rclone tool to migrate object data, you need to install the rclone program in the cluster. Use the scp command to directly copy the folder storing metadata and object files in minio to the target environment; Use third-party tools to migrate object files and replace minio components.

[0040] Specifically, in order to achieve unified services and standardized delivery of PaaS services, achieve the goal of unified management and consistent component versions, and improve the standardization and convenience of key basic component management, it is necessary to use standard cloud service basic components of the target platform instead. After the deployment of basic components, according to specific business needs, it also involves the opening of monitoring services, log auditing (such as cloud database MySQL) and other related operation and maintenance monitoring functions.

[0041] In step S2, the method for migrating database data includes: (1) MySQL / MariaDB Export and import method: export the database to a sql file and then re-import it. You can use a third-party database migration tool such as Navicat or Dbeaver. Backup and restore method: Use the backup commands provided by MySQL, such as mysqlhotcopy mysqldump; If a cloud host is used as the storage directory, migrate the cloud host directly.

[0042] (2) Redis If it is only used for caching, there is no need to migrate data. If it is used for persistence, then migrate the data. Use the persistence function of redis to generate aof or rdb files and upload data on the target end; Use third-party tools / clients to export and import data, such as redis-dump and redis-migrate.

[0043] (3) PostgreSQL Backup and restore method: Use the pg_dump command to back up the database, and use the pg_restore or psql command to restore the backup file to the target database; Tool method: Use third-party tools such as pgloader.

[0044] (4) InfluxDB Backup and restore method: Use the official tool influxdb backup / restore to migrate between databases of the same type and version.

[0045] (5) MongoDB Export and import method: first use the mongoexport command to export the collection data, and then use mongoimport to import it into the target database; Backup and restore method: use mongodump and mongorestore for backup and restore; Tool method: Third-party tools include Alibaba’s open source mongoshake and Navicat.

[0046] (6) ElasticSearch Backup and restore method: use elasticsearch-dump for backup and restore; Tool method: If the amount of data is small and needs to be migrated online, use logstash for migration.

[0047] Specifically, the process of adjusting the business configuration is to adjust resource allocation, network configuration and service integration according to the matching degree of application components, file data and object data, basic components and database data migrated to the target migration platform.

[0048] Specifically, the key points of business verification include business integrity verification, logic verification, related platform verification, business stability and availability verification, data integrity and consistency verification, and operational performance verification; The business integrity verification includes verifying whether the migrated business system contains all necessary components, such as applications, databases, file systems, network configurations, etc., and whether there are any missing or damaged components. Logical verification includes verifying whether the migrated business system can operate normally, whether there are any functional anomalies or errors, and whether it can meet business needs and expected results; related platform verification includes verifying whether the migrated business system can correctly interact and transmit data with other related systems or platforms, and whether there are compatibility or interface problems; business stability and availability verification includes verifying whether the migrated business system can operate stably, without frequent interruptions or failures, whether it can withstand normal or peak load pressure, and without performance degradation or timeout; data integrity and consistency verification includes verifying whether the migrated data is consistent with the source data, whether there is any loss, duplication, error or inconsistency, and whether it meets the data security indicators; operational performance verification includes verifying whether the migrated business system has achieved the expected operational performance indicators (MBPS, IOPS, RT, etc.), and whether there is any improvement or reduction compared with the source system.

[0049] Specifically, traffic switching methods include DNS switching, load balancing switching, and application layer switching.

[0050] Specifically, DNS switching is to direct traffic from the source system to the target system by modifying the DNS resolution record.

[0051] Specifically, the load balancing switch is to gradually transfer the traffic from the source system to the target system by configuring the existing F5 load balancer.

[0052] Specifically, application layer switching redirects traffic from the source system to the target system by modifying the application's configuration.

[0053] In implementation, the method of the present invention sets steps S1-S5, and adjusts the number of fragments of the data packet according to the variance of the data packet loss rate of the image file. Due to the signal attenuation caused by the interference of the network, network delay occurs, which causes the interruption of the image transmission, thereby causing problems in the image migration process. By increasing the number of fragments of the data packet, the influence of the interference on a single data packet is reduced. When some data packets are interfered with, only the affected data packets need to be retransmitted. By adjusting the backup frequency of the application component according to the average migration time of the application component, the file is tampered with due to virus invasion, resulting in an error in the format of the file, which causes problems when the configuration file is modified, thereby causing the application component to be Errors in the migration configuration of the application components will increase the migration time of the application components. By increasing the backup frequency of the application components, the probability of modification errors in the configuration files is reduced. The amount of parallel processing of database data is adjusted according to the migration conversion time of the database data. Since floating-point numbers are represented in binary form in the computer, the precision of floating-point numbers is lost, which will be reduced due to rounding errors, resulting in loss of data precision, data type mismatch, and further data conversion errors, which will lead to errors in the migration of database data. By reducing the amount of parallel processing of database data, the probability of resource competition is reduced, the possibility of errors is reduced, and the stability of adaptation optimization is improved.

[0054] Specifically, in step S5, determining the stability of the adaptation optimization includes: Obtaining the data packet loss rate of the image file in several image migration cycles, and calculating the variance of the data packet loss rate of the image file; Comparing the variance of the data packet loss rate of the image file with a preset first variance and a preset second variance respectively; If the variance of the data packet loss rate of the image file is greater than the preset first variance, it is determined that the stability of the adaptive optimization does not meet the preset standard.

[0055] It can be understood that the three intervals corresponding to the preset first variance Q1 and the preset second variance Q2 correspond to three situations respectively. The first interval is Q≤Q1, corresponding to the situation where the stability of the adaptation optimization meets the requirements; the second interval is Q1<Q≤Q2, corresponding to the situation where the file is tampered with by a virus, resulting in an error in the format of the file, resulting in problems when the configuration file is modified, and then resulting in errors in the migration configuration of the application component, thereby increasing the migration time of the application component; the third interval is Q>Q2, corresponding to the situation where the network is disturbed and causes signal attenuation, resulting in network delay, resulting in interruption of mirror transmission, and thus causing problems in the mirror migration process. In practice, the range of Q1 is generally [0.015, 0.025], and the range of Q2 is generally [0.025, 0.035].

[0056] Preferably, the first variance Q1 is preset to be 0.02, and the second variance Q2 is preset to be 0.03.

[0057] Specifically, the variance of the packet loss rate of the image file is denoted as Q.

[0058] Specifically, the variance of the data packet loss rate of the image file is the variance of the data packet loss rate of the image file within several image migration cycles. The calculation method of the variance of the data packet loss rate of the image file is a conventional technical means well known to those skilled in the art, so the calculation process of the variance of the data packet loss rate of the image file is not repeated here.

[0059] In implementation, the method of the present invention determines the stability of the adaptive optimization by setting a preset first variance and a preset second variance, thereby reducing the impact of the decreased accuracy of the adaptive optimization due to inaccurate judgment of the stability of the adaptive optimization, and further achieving an improvement in the stability of the adaptive optimization.

[0060] Specifically, if the variance of the data packet loss rate of the mirror file is greater than the preset second variance, the number of fragments of the data packet is increased.

[0061] Specifically, determining the stability of the data migration includes: The variance of the data packet loss rate of the image file is greater than the preset first variance and less than or equal to the preset second variance, and whether the stability of data migration meets the requirements is determined according to the average migration time of the application component; Comparing the average migration duration of the application component with the preset first migration duration and the preset second migration duration respectively; If the average migration duration of the application component is greater than the preset first migration duration, it is determined that the stability of the data migration does not meet the preset standard.

[0062] Specifically, the increase range of the number of fragments of the data packet is determined by the difference between the variance of the data packet loss rate of the mirror file and the preset second variance.

[0063] Specifically, the calculation formula for the number of fragments of the enlarged data packet is: , Among them, V' is the number of fragments of the increased data packet, V is the number of fragments of the current data packet, A is the minimum unit variance for adjusting the number of fragments of the data packet, and v is the number of fragments of the data packet when the variance of the data packet loss rate of the mirror file is the minimum unit variance.

[0064] In practice, the value of A is generally 0.03 to 0.05, and preferably, the preferred embodiment of A is 0.04.

[0065] In implementation, the optional range of the value of v is [30, 50]. Preferably, when the number of fragments of a data packet is less than 40, mirror transmission may be interrupted. Therefore, the preferred embodiment of v is 40.

[0066] For example, a cross-platform service adaptation optimization method for PaaS platform replacement is used to migrate the business of the first platform to the second platform, where V=30, Q=0.05, Calculate V'= =50 pcs.

[0067] In implementation, the method of the present invention adjusts the number of fragments of a data packet by setting a preset first variance and a preset second variance. Since the network is interfered with and causes signal attenuation, network delays occur, which causes mirror transmission interruption, thereby causing problems in the mirror migration process. By increasing the number of fragments of the data packet, the impact of interference on a single data packet is reduced. When some data packets are interfered with, only the affected data packets need to be retransmitted, thereby further improving the stability of the adaptation optimization.

[0068] Specifically, adjusting the backup frequency of the application component includes: Obtain migration durations of application components during several application component migration cycles, and calculate average migration durations of application components; Comparing the average migration duration of the application component with the preset first migration duration and the preset second migration duration respectively; If the average migration duration of the application component is greater than the preset first migration duration and less than or equal to the preset second migration duration, the backup frequency of the application component is increased; If the average migration duration of the application component is greater than the preset second migration duration, it is preliminarily determined that the stability of the database data migration does not meet the preset standard, and a secondary determination is made on whether the stability of the database data migration meets the requirements based on the migration conversion duration of the database data.

[0069] It can be understood that the three intervals corresponding to the preset first migration duration P1 and the preset second migration duration P2 correspond to three situations respectively. The first interval is P≤P1, corresponding to the situation where the stability of data migration meets the requirements; the second interval is P1<P≤P2, corresponding to the situation where the file is tampered with by a virus, resulting in errors in the file format, causing problems when modifying the configuration file, and then causing errors in the migration configuration of the application component, thereby increasing the migration duration of the application component; the third interval is P>P2, corresponding to the floating point number represented in binary form in the computer, resulting in the loss of precision of the floating point number, which will be reduced due to rounding errors, resulting in loss of data precision, resulting in data type mismatch, and then resulting in data conversion errors. In practice, the range of P1 is generally selected from [0.3min, 0.5min], and the range of P2 is generally selected from [0.5min, 0.7min].

[0070] Preferably, the preferred embodiment of the preset first migration duration P1 is 0.4 min, and the preferred embodiment of the preset second migration duration P2 is 0.6 min.

[0071] Specifically, the average migration duration of application components is denoted as P.

[0072] Specifically, the average migration time of application components is calculated as follows: , Where P is the average migration time of application components, X a is the migration duration of the application component in the ath application component migration cycle, n is the number of application component migration cycles, and n is a natural number greater than or equal to 1.

[0073] In implementation, the method of the present invention performs a secondary judgment on the stability of data migration by setting a preset first migration duration and a preset second migration duration, thereby reducing the impact of the decreased stability of the adaptation optimization due to inaccurate secondary judgment on the stability of data migration, and further achieving an improvement in the stability of the adaptation optimization.

[0074] Specifically, the increase range of the backup frequency of the application component is determined by the difference between the average migration duration of the application component and the preset first migration duration.

[0075] Specifically, the calculation formula for the backup frequency of the increased application component is: , Among them, H' is the increased backup frequency of the application component, H is the current backup frequency of the application component, B is the minimum unit migration duration for adjusting the backup frequency of the application component, and h is the backup frequency of the application component when the average migration duration of the application component is the minimum unit migration duration.

[0076] In practice, the value of B is generally 0.6 min to 0.8 min, and preferably, the preferred embodiment of B is 0.7 min.

[0077] In implementation, the optional value range of h is [3 times / h, 6 times / h]. Preferably, when the backup frequency of the application component is less than 5 times / h, it may cause errors in the application component migration configuration. Therefore, the preferred embodiment of h is 5 times / h.

[0078] Specifically, when the value of H' is a decimal, the value of H' is automatically rounded up to an integer.

[0079] For example, a cross-platform service adaptation optimization method for PaaS platform replacement is used to migrate the services of the first platform to the second platform, where H = 3 times / h, P = 0.7min, Calculate H'= ≈6 times / h.

[0080] In implementation, the method of the present invention adjusts the backup frequency of application components by setting a preset first migration duration and a preset second migration duration. Due to virus invasion, files are tampered with, resulting in errors in the file format, which leads to problems in modifying configuration files, and then to errors in the migration configuration of application components, thereby increasing the migration duration of application components. By increasing the backup frequency of application components, the probability of modification errors in the configuration files is reduced, further achieving improved stability for adaptation optimization.

[0081] Specifically, adjusting the amount of parallel processing data of the database data includes: Get the migration conversion time of database data; Comparing the migration conversion time of the database data with the preset conversion time; If the migration conversion time of the database data is longer than the preset conversion time, it is secondarily determined that the stability of the database data migration does not meet the preset standard, and the amount of parallel processing of the database data is reduced.

[0082] It can be understood that the two intervals corresponding to the preset conversion time Y0 correspond to two situations. The first interval is Y≤Y0, corresponding to the situation where the stability of database data migration meets the requirements; the second interval is Y>Y0, corresponding to the floating point number represented in binary form in the computer, resulting in the loss of precision of the floating point number, which will be reduced due to rounding errors, resulting in the loss of data precision, resulting in data type mismatch, and then leading to data conversion errors. In practice, Y0 is generally selected in the range of [0.2s, 0.4s].

[0083] Preferably, the preferred embodiment of the preset conversion time length Y0 is 0.3s.

[0084] Specifically, the migration conversion time of database data is recorded as Y.

[0085] In implementation, the method of the present invention performs a secondary judgment on the stability of database data migration by setting a preset conversion time, thereby reducing the impact of the stability decline of adaptation optimization due to inaccurate secondary judgment on the stability of database data migration, and further achieving an improvement in the stability of adaptation optimization.

[0086] Specifically, the migration conversion duration of the database data is the ratio of the number of successful migrations of the database data to the total number of migrations of the database data.

[0087] Specifically, the reduction extent of the parallel processing data volume of the database data is determined by the difference between the migration conversion time of the database data and the preset conversion time.

[0088] Specifically, the calculation formula for the reduced parallel processing data volume of the database data is: , Among them, L' is the parallel processing data volume of the reduced database data, L is the current parallel processing data volume of the database data, C is the minimum unit conversion time for adjusting the parallel processing data volume of the database data, and l is the parallel processing data volume of the database data when the migration conversion time of the database data is the minimum unit conversion time.

[0089] In practice, the value of C is generally 0.4s to 0.6s, and preferably, the preferred embodiment of C is 0.5s.

[0090] In implementation, the optional value range of l is [750Byte, 850Byte]. Preferably, when the parallel processing data volume of database data is greater than 800Byte, data conversion errors may occur. Therefore, the preferred embodiment of l is 800Byte.

[0091] For example, a cross-platform service adaptation optimization method for PaaS platform replacement is used to migrate the business of the first platform to the second platform, where L=900Byte, Y=0.6s, Calculate L'= ≈420Byte.

[0092] In implementation, the method of the present invention adjusts the amount of parallel processing of database data by setting a preset conversion time. Since floating-point numbers are represented in binary form in a computer, the precision of floating-point numbers is lost, which will be reduced due to rounding errors, resulting in loss of data precision, data type mismatch, and further data conversion errors, which in turn lead to errors in the migration of database data. By reducing the amount of parallel processing of database data, the probability of resource competition is reduced, the possibility of errors is reduced, and the stability of adaptive optimization is further improved.

[0093] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A cross-platform service adaptation optimization method for PaaS platform replacement, characterized in that: include: Step S1, scheduling the image file of the original platform cluster to the image warehouse of the original platform, migrating the image file to the image warehouse of the target migration platform to deploy the image file to the target migration platform, and restoring the service of the original platform cluster; Step S2, deploying the application components using the migrated image files, migrating the file data and object data that the application components depend on in sequence, and migrating the basic components and database data in the original platform cluster in sequence; Step S3, adjusting the configuration of the service, verifying the service, and switching the service traffic; Step S4, obtaining the data packet loss rate of the image file in several image migration cycles; Step S5, judging the stability of the adaptation optimization based on the variance of the packet loss rate of the image file to determine whether to perform the stability adjustment method; The stability adjustment method includes adjusting the number of fragments of the data packet, or determining the stability of data migration according to the average migration time of the application component to determine whether to perform the migration stability adjustment method. The migration stability adjustment method includes adjusting the backup frequency of the application components. Alternatively, the amount of parallel processing of database data is determined based on the migration and conversion time of the database data.

2. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 1 is characterized in that: In the step S5, determining the stability of the adaptation optimization includes: Obtaining the data packet loss rate of the image file in several image migration cycles, and calculating the variance of the data packet loss rate of the image file; Comparing the variance of the data packet loss rate of the image file with a preset first variance and a preset second variance respectively; If the variance of the data packet loss rate of the image file is greater than the preset first variance, it is determined that the stability of the adaptive optimization does not meet the preset standard.

3. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 2 is characterized in that: If the variance of the data packet loss rate of the mirror file is greater than the preset second variance, the number of fragments of the data packet is increased.

4. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 3 is characterized in that: Determining the stability of the data migration includes: The variance of the data packet loss rate of the image file is greater than the preset first variance and less than or equal to the preset second variance, and whether the stability of data migration meets the requirements is determined according to the average migration time of the application component; Comparing the average migration duration of the application component with the preset first migration duration and the preset second migration duration respectively; If the average migration duration of the application component is greater than the preset first migration duration, it is determined that the stability of the data migration does not meet the preset standard.

5. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 4 is characterized in that: The increase range of the number of fragments of the data packet is determined by the difference between the variance of the data packet loss rate of the mirror file and the preset second variance.

6. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 5 is characterized in that: The backup frequency of the application component is adjusted, including: If the average migration duration of the application component is greater than the preset first migration duration and less than or equal to the preset second migration duration, the backup frequency of the application component is increased; If the average migration duration of the application component is greater than the preset second migration duration, the parallel processing data volume of the database data is adjusted according to the migration conversion duration of the database data.

7. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 6 is characterized in that: The increase range of the backup frequency of the application component is determined by the difference between the average migration duration of the application component and the preset first migration duration.

8. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 7, characterized in that: Adjusting the amount of parallel processing of the database data includes: Get the migration conversion time of database data; Comparing the migration conversion time of the database data with the preset conversion time; If the migration conversion time of the database data is longer than the preset conversion time, the amount of parallel processing of the database data is reduced.

9. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 8, characterized in that: The migration conversion duration of the database data is the ratio of the number of successful database data migrations to the total number of database data migrations.

10. The cross-platform service adaptation optimization method for PaaS platform replacement according to claim 9, characterized in that: The reduction range of the parallel processing data volume of the database data is determined by the difference between the migration conversion time of the database data and the preset conversion time.

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