Platform as a Service (PaaS) self-management methods, devices, equipment, and storage media

Abstract

This invention provides a self-management method, apparatus, device, and storage medium for a platform as a service (PAS), comprising: collecting key parameters of the PAS within a target period; performing an upgrade operation on the PAS when the CPU utilization or memory utilization is greater than a predefined alarm threshold; generating a comprehensive score of service profile parameters and an actual score corresponding to each type of service profile parameter within the target period according to preset rules when both CPU utilization and memory utilization are less than the predefined alarm threshold; performing an upgrade operation on the PAS when the comprehensive score is less than a predefined service profile threshold; and performing a downgrade operation on the PAS when the actual score of the throughput indicator is less than a predefined target threshold. This invention improves the performance of the PAS by modifying resource parameter configurations through upgrade or downgrade operations.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a self-management method, apparatus, device, and storage medium for a platform as a service. Background Technology

[0002] In the era of container technology, numerous business applications are being deployed to the cloud using Kubernetes container orchestration. Some of these applications, in order to quickly deploy and provide services, rely on PaaS (Platform as a Service) deployments. To lower the barrier to entry for configuring PaaS services, some empirical parameters are provided. These parameters are called PaaS service configuration items. Each configuration item undergoes relevant testing, and the optimal processing capacity corresponding to that configuration item is determined. The parameters corresponding to this optimal processing capacity, summarized from experience, are called the service profile metrics of the PaaS service.

[0003] As the volume of business applications changes, the ability of PaaS services to process business may become a bottleneck affecting the performance of business applications. For example, as the volume of business applications increases, the PaaS service can no longer handle the previous volume of business. At this time, the operating parameters of the PaaS service exceed the service profile index value of the PaaS service, which causes the PaaS service to reduce its ability to process business, and thus reduces the performance of business applications. Summary of the Invention

[0004] The purpose of this invention is to provide a self-management method, apparatus, device, and storage medium for Platform as a Service (PaaS) to address the problem that the operating parameters of a PaaS service exceed the service profile index values, thereby causing a decrease in the PaaS service's ability to process business, and consequently a decrease in the performance of business applications. The specific technical solution is as follows:

[0005] In a first aspect of this invention, a self-management method for a platform-as-a-service is provided, characterized in that the method comprises:

[0006] Collect key parameters of the platform as a service within the target period, including CPU utilization, memory utilization, and service profile parameters. The service profile parameters include at least one type of parameter among response time, throughput, concurrent users, and mean time between failures.

[0007] If the CPU usage or memory usage is determined to be greater than a predefined alarm threshold, the platform as a service will be upgraded.

[0008] If the CPU utilization and memory utilization are both less than a predefined alarm threshold, the comprehensive score of the service profile parameters within the target period and the actual score corresponding to each type of service profile parameter are generated according to preset rules.

[0009] If the overall score is determined to be less than a predefined service profile threshold, the platform as a service will be upgraded.

[0010] If the actual score of the throughput metric is determined to be less than a predefined target threshold, the platform-as-a-service will be downgraded.

[0011] Optionally, before collecting the key parameters of the platform-as-a-service within the target collection period, the following further information is included:

[0012] Obtain the type and service performance level of the Platform as a Service under the target specifications;

[0013] The target specification file name in the platform as a service is determined by the type of platform as a service and the service performance level. The higher the value of the service performance level, the better the service performance supported by the corresponding platform as a service.

[0014] Define target parameters for the Platform-as-a-Service (PaaS) under the target specification. These target parameters include resource parameters.

[0015] System parameters, extended parameters, and target service profile parameters, wherein the target parameters are all array-type structures. The array structures of the resource parameters, system parameters, and extended parameters include fields specifying parameter names, fields specifying parameter values, and fields describing the function of the parameters. The array structure of the target service profile parameters includes fields specifying parameter names, fields specifying parameter values, fields specifying the target weight of the parameters, and fields describing the function of the parameters.

[0016] The alarm thresholds for the use of the central processing unit, memory, and storage under the target specifications of the Platform as a Service are determined by the resource parameters.

[0017] The system parameters ensure the normal startup and operation of the platform as a service under the target specifications, and the extended parameters ensure the normal use of the target functions of the platform as a service under the target specifications.

[0018] The target service profile parameters are used to generate the service profile threshold of the platform as a service under the target specifications.

[0019] By defining the target specification file name and target parameters under the target specification, a target resource instance of the corresponding specification is created in the target cluster;

[0020] The target resource instance completes the deployment of the target specification Platform as a Service in the target cluster.

[0021] Optionally, when it is determined that both the CPU utilization and memory utilization are less than a predefined alarm threshold, generating a comprehensive score for the service profile parameters within the target period and an actual score corresponding to each type of service profile parameter according to preset rules includes:

[0022] If it is determined that both the CPU utilization and memory utilization are less than a predefined alarm threshold, obtain the predefined target service profile parameters, floating parameters, indicator difference parameters, and target weights for each type of target service profile parameter.

[0023] Based on the target service profile parameters, the floating parameters, and the index difference parameters, the target score range for each type of target service profile parameter is determined.

[0024] After retrieving the service profile parameters in the target score range, determine the actual score corresponding to each type of service profile parameter within the target period.

[0025] The target weights are used to weight the actual scores corresponding to the service profile parameters of each type;

[0026] The weighted actual scores are summed to obtain the comprehensive score of the service profile parameters within the target period.

[0027] Optionally, the predefined target service profile parameters include at least one type of parameter among target response time, target throughput metric, target concurrent users, and target mean time between failures metric.

[0028] The step of determining the target score range for each target service profile parameter based on the target service profile parameters, the floating parameter, and the indicator difference parameter includes:

[0029] For any of the target service profile parameters, a first score range is determined by the target service profile parameters and the floating parameters;

[0030] For any of the target service profile parameters, a second score interval is determined by the target service profile parameters, the floating parameter, and the indicator difference parameter;

[0031] The target score range for any of the target service profile parameters is determined based on the first score range and the second score range.

[0032] Optionally, the step of retrieving the service profile parameters from the target score range and determining the actual score corresponding to each type of service profile parameter within the target period includes:

[0033] The target score range is divided according to the type of target service profile parameter. Each target score range under the division corresponds to a type of target service profile parameter.

[0034] Add a type marker to the partition interval, the type marker being used to indicate the type of the target service profile parameter in the current partition interval;

[0035] Select the first type of service profile parameter from the service profile parameters;

[0036] The first type is retrieved from the type tags to determine the target partitioning interval;

[0037] The service profile parameters of the first type are compared with the target segmentation interval to determine the actual score corresponding to the service profile parameters of the first type.

[0038] Optionally, after performing a degradation operation on the platform-as-a-service when the actual score of the throughput metric is determined to be less than a predefined target threshold, the method further includes:

[0039] Obtain information about the replicas of services running on Platform as a Service and their upgrade / downgrade operation modes;

[0040] If it is determined that a single-copy service exists in the Platform as a Service and the upgrade / downgrade operation mode is manual, a prompt message will be sent to the user.

[0041] Obtain upgrade / degrade operation strategies for workloads in the Platform as a Service;

[0042] If it is determined that the upgrade / downgrade operation strategy is not a rolling update and the upgrade / downgrade operation mode is manual, a prompt message will be sent to the user.

[0043] Optionally, the step of upgrading the platform-as-a-service when the overall score is determined to be less than a predefined service profile threshold further includes:

[0044] If the overall score is determined to be less than a predefined service profile threshold, the first resource parameter that the platform as a service needs to be reconfigured is determined.

[0045] Get the name of the first specification file of the resource instance corresponding to the current Platform as a Service;

[0046] The second resource parameter corresponding to the resource instance is determined by searching the target specification file by the name of the first specification file.

[0047] The platform as a service is upgraded based on the first resource parameter and the second resource parameter.

[0048] In a second aspect of the invention, a self-management device for a platform-as-a-service is also provided, characterized in that it comprises:

[0049] The data collection module is used to collect key parameters of the platform as a service within the target period. The key parameters include CPU utilization, memory utilization, and service profile parameters. The service profile parameters include at least one type of parameter among response time, throughput, concurrent users, and mean time between failures.

[0050] The first upgrade module is used to perform an upgrade operation on the platform as a service when it is determined that the CPU usage or memory usage is greater than a predefined alarm threshold.

[0051] The first generation module is used to generate a comprehensive score of service profile parameters within a target period and an actual score corresponding to each type of service profile parameter, according to preset rules, when it is determined that the central processing unit utilization rate and memory utilization rate are both less than a predefined alarm threshold.

[0052] The second upgrade module is used to perform an upgrade operation on the platform as a service when it is determined that the comprehensive score is less than a predefined service profile threshold.

[0053] The first degradation module is used to perform a degradation operation on the platform as a service when it is determined that the actual score of the throughput indicator is less than a predefined target threshold.

[0054] In a third aspect of the present invention, a communication device is also provided, comprising: a transceiver, a memory, a processor, and a program stored in the memory and executable on the processor;

[0055] The processor is used to read programs from memory to implement the self-management method of any of the Platform as a Service described above.

[0056] In a fourth aspect of the invention, a computer-readable storage medium is also provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform any of the self-management methods of the Platform as a Service described above.

[0057] The self-management method for a Platform as a Service (PaaS) provided in this invention collects key parameters of the PaaS within a target period. These key parameters include CPU utilization, memory utilization, and service profile parameters. Service profile parameters include at least one type of parameter selected from response time, throughput, concurrent users, and mean time between failures (MTBF). If CPU utilization or memory utilization exceeds a predefined alarm threshold, an upgrade operation is performed on the PaaS. By judging CPU utilization or memory utilization, it is determined whether the performance degradation of the PaaS is due to unreasonable resource parameter configuration. If a bottleneck is identified, an upgrade operation is performed to resolve the issue. If both CPU utilization and memory utilization are less than the predefined alarm threshold, a system is generated according to preset rules. The system calculates the comprehensive score of service profile parameters within the target period and the actual score corresponding to each type of service profile parameter. If the comprehensive score is less than a predefined service profile threshold, an upgrade operation is performed on the platform-as-a-service (PAS). If the actual score of the throughput indicator is less than a predefined target threshold, a downgrade operation is performed on the PAS. These operations prevent the PAS from experiencing performance degradation, reduced throughput, and high latency in request processing due to unreasonable resource parameter configuration. This embodiment of the invention determines whether the performance of the PAS is reduced due to unreasonable resource parameter configuration from different dimensions by judging CPU utilization, memory utilization, throughput indicators, and service profile parameters. Then, by modifying the resource parameter configuration through upgrade or downgrade operations, the performance of the PAS is improved. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0059] Figure 1 A flowchart illustrating the steps of a self-management method for a platform-as-a-service provided in this embodiment of the invention;

[0060] Figure 2 for Figure 1 The flowchart shown is a self-management method for a platform-as-a-service provided in an embodiment of the present invention, specifically step 103.

[0061] Figure 3 This is a schematic diagram of data flow in a self-management method for a platform-as-a-service provided in an embodiment of the present invention;

[0062] Figure 4 This is an operation flowchart of a self-management platform as a service provided by an embodiment of the present invention;

[0063] Figure 5This is a schematic diagram of the structure of a self-management device for a platform as a service provided in an embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the structure of a communication device provided in an embodiment of the present invention. Detailed Implementation

[0065] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the various embodiments of the present invention will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details are presented in the various embodiments of the present invention to facilitate a better understanding of this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for ease of description and should not constitute any limitation on the specific implementation of the present invention. The various embodiments can be combined with and referenced by each other without contradiction.

[0066] Reference Figure 1 The diagram illustrates a flowchart of a self-management method for a platform-as-a-service provided by an embodiment of the present invention. The method may include:

[0067] Step 101: Collect key parameters of the platform as a service within the target period. The key parameters include CPU utilization, memory utilization, and service profile parameters. The service profile parameters include at least one type of parameter among response time, throughput, concurrent users, and mean time between failures.

[0068] The target period in this embodiment of the invention can be any period of the Platform as a Service (PaaS) operation. However, to determine the current operating status of the PaaS, the target period is determined to be the period closest to the current moment. Because this embodiment of the invention triggers PaaS service lifecycle management (i.e., upgrade or downgrade) operations when it determines that the PaaS deployed on the Kubernetes cluster has reached a service bottleneck, it is necessary to obtain key parameters of the PaaS within a period to determine whether a service bottleneck has been reached.

[0069] It's important to note that PaaS services provide foundational software services, such as databases and message queues, to support users in focusing on their business applications. In a PaaS environment, CRDs (Custom Resource Definitions) and CRs (Custom Resources) can be used to better manage and deploy applications. CRDs are a feature of Kubernetes that allows users to customize resources by extending the Kubernetes API. Through CRDs, users can define their own API objects, and CRs are concrete resource instances created based on CRDs. Once a CRD is defined, multiple CR instances of that type can be created. Each CR instance contains user-defined specific data and behaviors. Therefore, when deploying a PaaS service, it's necessary to first declare and define the CRs related to the CRD resources that set the parameters usable by the PaaS service. The code is as follows:

[0070]

[0071]

[0072] The `rolloutInfo` attribute defined in the annotations section of the code specifies the name of the specification file required for upgrading or downgrading the PaaS service. The specification name follows the rule: `system-xxx-level1-flavor`, where `xxx` represents the PaaS service type; `level` + a number indicates the performance of the PaaS service supported by the higher the number. The `paasFlavorType` field represents the PaaS service type adapted to the current specification. `flavorInfos` primarily describes the specification parameters set for the current specification, consisting of five parameters: `resourceParamsInfo`, `systemParamsInfo`, `extenderParamsInfo`, `servicePortrait`, and `thresholdScore`. `resourceParamsInfo` is used to define the specified specifications. The system defines the resource parameters required for the PaaS service under the current specification. These parameters determine the upper and lower limits of resource usage, such as CPU, memory, and storage, or the normal usable size. They can be used to determine alarm thresholds for CPU, memory, and storage. `systemParamsInfo` defines the built-in system parameters of the PaaS service under the current specification. These parameters vary greatly depending on the PaaS service and are used to ensure the normal startup and operation of the PaaS service. `extenderParamsInfo` defines some extended parameters to assist in the implementation of PaaS service functions, mainly for easy expansion, and can also be used to ensure the normal use of certain functions in the platform as a service. `servicePortrait` defines the service profile parameters of the PaaS service under the current specification, including response time, throughput metrics, concurrent users, and mean time between failures (MTBF). It also sets weights for each type of service profile parameter. `thresholdScore` is a service profile threshold automatically generated based on the set service profile parameters. It is used to determine whether the service profile parameters have reached a bottleneck. If the threshold is lowered, an upgrade operation will be triggered in the PaaS service lifecycle. After defining each PaaS service specification, it is created on the Kubernetes cluster, generating resources of the corresponding specification. The process involves obtaining defined specification resources, converting them into YAML parameters, system parameters, and extended parameters for deploying PaaS services, and then completing the deployment of the PaaS service. Specific steps include:

[0073] Obtain the type and service performance level of the Platform as a Service under the target specifications;

[0074] The target specification file name in the Platform as a Service (PaaS) is determined by the type of Platform as a Service and the service performance level. The higher the value of the service performance level, the better the service performance supported by the corresponding Platform as a Service.

[0075] Define target parameters for the Platform as a Service under the target specification. Target parameters include resource parameters, system parameters, extended parameters, and target service profile parameters. All target parameters are array-type structures. The array structures of resource parameters, system parameters, and extended parameters include fields specifying parameter names, fields specifying parameter values, and fields describing the function of the parameters. The array structure of target service profile parameters includes fields specifying parameter names, fields specifying parameter values, fields specifying the target weight of the parameters, and fields describing the function of the parameters.

[0076] Determine the alarm thresholds for CPU, memory, and storage usage under the target specifications for the Platform as a Service by using resource parameters;

[0077] System parameters ensure the normal startup and operation of the platform as a service under the target specifications, and extended parameters ensure the normal use of the target functions of the platform as a service under the target specifications.

[0078] Generate service profile thresholds for the platform-as-a-service under the target specifications using the target service profile parameters.

[0079] By defining the target specification file name and target parameters under the target specification, a target resource instance of the corresponding specification is created in the target cluster;

[0080] Deploy the target specification Platform as a Service in the target cluster using the target resource instance.

[0081] Among them, resourceParamsInfo, systemParamsInfo, extendParamsInfo, and servicePortrait are all array-type parameters, with array elements being parameter objects. The structure code of the resourceParamsInfo parameter definition will be used as an example:

[0082] "propertyKey":"cpu", / / Parameter type

[0083] "propertyValue":"1", / / Parameter value

[0084] "description":"Recommended CPU resource size" / / Parameter description

[0085] The propertyKey field specifies the parameter name, the propertyValue field specifies the parameter value, and the description field indicates the function of the parameter. The structure of the systemParamsInfo, extendParamsInfo and resourceParamsInfo parameters is the same, but the structure of the servicePortrait parameter also includes weight settings, such as "Weights":"10".

[0086] By pre-setting various parameters in the target specification of the platform as a service, it is easier to determine whether the parameters during actual operation reach the service bottleneck of the platform as a service, and thus determine whether an upgrade or downgrade operation is needed.

[0087] Step 102: If the CPU usage or memory usage is determined to be greater than a predefined alarm threshold, perform an upgrade operation on the platform as a service.

[0088] In this embodiment of the invention, the decision to perform an upgrade operation is determined by monitoring CPU usage or memory usage, as these metrics are directly related to the performance and stability of the application. Monitoring these data can be done using monitoring tools such as Prometheus, Grafana, and Datadog. Alarm rules are set in the monitoring tools to trigger alarms when CPU usage or memory usage exceeds a preset threshold. The alarm rules include alarm thresholds, which are determined by resource parameters set when defining the Platform as a Service (PaaS) specifications. For example, an alarm may be triggered when CPU usage exceeds 80% or memory usage exceeds 70%. The specific parameter settings are determined according to the PaaS specifications, and this invention does not impose specific limitations on them. After an alarm is triggered, an upgrade operation for the PaaS will be initiated.

[0089] Step 103: If the CPU utilization and memory utilization are both less than the predefined alarm threshold, generate the comprehensive score of the service profile parameters within the target period and the actual score corresponding to each type of service profile parameter according to the preset rules.

[0090] In this embodiment of the invention, when both the CPU utilization and memory utilization are determined to be less than a predefined alarm threshold, it is considered that the CPU utilization or memory utilization has not yet reached the service bottleneck of the platform as a service. At this time, other parameters can be judged. Here, the judgment is made on the service profile parameters. Because when defining the parameters of the platform as a service with the target specifications, a service profile parameter threshold is pre-set, which is a score threshold, a score value must also be calculated for the currently collected service profile parameters. For this purpose, this embodiment of the invention sets a score generation rule. According to this rule, the actual score corresponding to each type of service profile parameter and the comprehensive score of the service profile parameters within the target period can be generated.

[0091] Step 104: If the overall score is determined to be less than the predefined service profile threshold, upgrade the platform as a service.

[0092] In this embodiment of the invention, if the overall score is less than a predefined service profile threshold, it is considered that the performance of the platform as a service is low, possibly because a service bottleneck has been reached, and an upgrade operation is required.

[0093] When performing an upgrade, it is necessary to determine the resource parameters that need to be reconfigured during the upgrade, then identify the target resource instance to be upgraded, determine the resource parameter configuration of the platform as a service under the current specification from the target resource instance, and update it to achieve the upgrade. The specific steps include:

[0094] If the overall score is determined to be less than a predefined service profile threshold, the first resource parameter that the platform as a service needs to be reconfigured is determined.

[0095] Get the name of the first specification file of the resource instance corresponding to the current Platform as a Service;

[0096] The second resource parameter corresponding to the resource instance is determined by searching the target specification file by the name of the first specification file.

[0097] The platform as a service is upgraded based on the first and second resource parameters.

[0098] The above steps enable automation of operations and real-time monitoring, reducing human error and improving the efficiency of service management.

[0099] The first resource parameter can be a new configuration parameter for the central processing unit, memory, and storage. The name of the first specification file corresponds to "system-xxx-level1-flavor" mentioned in the code above. During the upgrade operation, the update operation is first performed based on the resource parameter information obtained during the upgrade and the resource parameter information (CR information) of the platform as a service under the current specification. Then, the PAAS Operator service listens for changes in the corresponding CR information and begins to perform the corresponding resource update operation.

[0100] It should be noted that the steps for performing a downgrade operation can be the same as those described above, except that the final step is to perform a downgrade operation.

[0101] Step 105: If the actual score of the throughput metric is determined to be less than the predefined target threshold, the platform as a service is downgraded.

[0102] In this embodiment of the invention, when calculating the actual score of the service profile parameters, one situation is that the platform as a service has reached a service bottleneck, resulting in reduced performance and a low overall score. Another situation is that the services currently being used by the platform as a service are very few, far less than the service bottleneck. In this case, the calculated overall score is also low. Upgrading would not solve this problem but would only exacerbate it. Therefore, the actual score of the throughput indicator is compared. If the actual score of the throughput indicator is less than a predefined target threshold, it is considered that the platform as a service provides too many service resources and needs to be reduced. Therefore, a downgrade operation is required.

[0103] It is important to note that thorough checks and preparations are crucial before performing upgrades or downgrades to ensure service stability and reliability. These checks include: verifying that all currently running PaaS services are multi-replica. If not, and some are single-replica, it's necessary to determine if it's in manual mode. If so, a warning should be issued to the user, and subsequent operations should proceed after user confirmation. This is because multi-replica improves service availability and fault tolerance, while upgrading or downgrading a single-replica service can cause service interruption; therefore, a warning is required (the warning should include the potential risks of manual upgrades, such as service interruption and data loss). The check also examines the upgrade strategy for the corresponding workload of the current PaaS service. If it's not a rolling update and is in manual mode, a warning should be issued to the user (the warning should explain that a non-rolling update strategy might directly replace all Pod replicas, potentially causing service interruption). After user confirmation, subsequent operations should proceed. Specific steps include:

[0104] Obtain information about the replicas of services running on Platform as a Service and their upgrade / downgrade operation modes;

[0105] If it is determined that a single-copy service exists in the Platform as a Service and the upgrade / downgrade operation mode is manual, a prompt message will be sent to the user.

[0106] Obtain upgrade / degrade operation strategies for workloads in the Platform as a Service;

[0107] If it is determined that the upgrade / downgrade operation strategy is not a rolling update and the upgrade / downgrade operation mode is manual, a prompt message will be sent to the user.

[0108] Furthermore, if the service is running in automatic mode, meaning the upgrade policy is executed automatically, then users should be explicitly informed of the risks associated with performing PaaS service lifecycle operations (upgrade / downgrade operations) under this policy. Users should be informed that they may not be able to control every step of the upgrade in automatic mode, and therefore need to be prepared for potential problems. Users should clearly indicate that they understand and accept the risks of performing upgrade operations in automatic mode. This can be achieved through confirmation buttons on the user interface, command-line input, or other forms of interaction.

[0109] By following the above inspection steps, we can ensure that users fully understand the risks of upgrading or downgrading PaaS services and make informed decisions, which helps reduce unexpected situations during the operation and improves the overall stability of the service.

[0110] The self-management method for a Platform as a Service (PaaS) provided in this invention collects key parameters of the PaaS within a target period. These key parameters include CPU utilization, memory utilization, and service profile parameters. Service profile parameters include at least one type of parameter selected from response time, throughput, concurrent users, and mean time between failures (MTBF). If CPU utilization or memory utilization exceeds a predefined alarm threshold, an upgrade operation is performed on the PaaS. By judging CPU utilization or memory utilization, it is determined whether the performance degradation of the PaaS is due to unreasonable resource parameter configuration. If a bottleneck is identified, an upgrade operation is performed to resolve the issue. If both CPU utilization and memory utilization are less than the predefined alarm threshold, a system is generated according to preset rules. The system calculates the comprehensive score of service profile parameters within the target period and the actual score corresponding to each type of service profile parameter. If the comprehensive score is less than a predefined service profile threshold, an upgrade operation is performed on the platform-as-a-service (PAS). If the actual score of the throughput indicator is less than a predefined target threshold, a downgrade operation is performed on the PAS. These operations prevent the PAS from experiencing performance degradation, reduced throughput, and high latency in request processing due to unreasonable resource parameter configuration. This embodiment of the invention determines whether the performance of the PAS is reduced due to unreasonable resource parameter configuration from different dimensions by judging CPU utilization, memory utilization, throughput indicators, and service profile parameters. Then, by modifying the resource parameter configuration through upgrade or downgrade operations, the performance of the PAS is improved.

[0111] Reference Figure 2 , showed Figure 1 The flowchart shown in this embodiment of the invention illustrates step 103 of a self-management method for a platform-as-a-service, the method of which may include:

[0112] Step 1031: If it is determined that both the CPU utilization and memory utilization are less than the predefined alarm threshold, obtain the predefined target service profile parameters, floating parameters, indicator difference parameters, and target weights for each type of target service profile parameter.

[0113] In this embodiment of the invention, when it is determined that both the CPU utilization and memory utilization are less than a predefined alarm threshold, the comprehensive score of the service profile parameters within the target period and the actual score corresponding to each type of service profile parameter are generated according to a preset rule. Here, the preset rule refers to determining the comprehensive score of the service profile parameters and the actual score corresponding to each type of service profile parameter based on the predefined target service profile parameters, floating parameters, index difference parameters, and target weights for each type of target service profile parameter.

[0114] Step 1032: Determine the target score range for each type of target service profile parameter based on the target service profile parameters, floating parameters, and index difference parameters.

[0115] In this embodiment of the invention, predefined target service profile parameters include at least one type of parameter selected from target response time, target throughput, target concurrent users, and target mean time between failures (MTBF). Based on the target service profile parameters, a floating parameter, and a parameter difference, a target score range for each type of target service profile parameter is determined. For example, the target response time, target throughput, target concurrent users, and target MTBF are set in the target service profile parameters using 'a'. s b s c s d s The identifier, the floating parameter is represented by λ. a , λ b , λ c , λ d index difference parameter β a β b β c β d The score range of 100 points is set as follows: [a s -λ a ,a s +λ a ],[b s -λ b ,b s +λ b ], [c s -λ c ,c s +λ c ],[d s -λ d ,d s +λ d The service profile parameter score for this range is 100 points, and the score range of 90 points is set as follows:

[0116] [a s -λ a -β a ,a s -λ a ), [b s -λ b -β b ,b s -λ b ), [c s -λ c -β c ,c s -λ c ),

[0117] [d s -l d -b d ,d s -l d ). s -l a -2*b a ,a s -l a -b a ),[b s -l b -2*b b ,b s -l b -b b ),[c s -l c -2*b c ,c s -l c -b c ),[d s -l d -2*b d ,d s -l d -b d ). s -l a -3*b a ,a s -l a -2*b a ),

[0118] [b s -l b -3*b b ,b s -l b -2*b b ),[c s -l c -3*b c ,c s -l c -2*b c ),

[0119] [d s -l d -3*b d ,d s -l d -2*b d ). s -l a -4*b a ,

[0120] a s -λ a -3*β a ), [b s -λ b -4*β b ,b s -λ b -3*β b ), [c s -λ c -4*β c ,

[0121] c s -λ c -3*β c ), [d s -λ d -4*β d ,d s -λ d -3*β d Furthermore, other score ranges can be set according to the above rules, but this invention does not impose specific limitations on them.

[0122] It can be seen that when the value fluctuates around the target service profile parameter, the performance of the platform as a service is considered unaffected, so the score is set to full. The interval determined by the target service profile parameter and the fluctuation parameter is the first scoring interval. When the value exceeds the fluctuation parameter range, an incrementally increasing indicator difference parameter is set, with the score decreasing as the value moves further away from the target service profile parameter. The interval determined by the target service profile parameter, the fluctuation parameter, and the indicator difference parameter is the second scoring interval. The specific steps include:

[0123] For any target service profile parameter, the first score range is determined by the target service profile parameter and the floating parameter;

[0124] For any target service profile parameter, the second score interval is determined by the target service profile parameter, the floating parameter, and the indicator difference parameter.

[0125] The target score range for any target service profile parameter is determined based on the first score range and the second score range.

[0126] As shown in the example above, the second scoring interval includes the 90-point, 80-point, 70-point, and 60-point intervals, while the first scoring interval only includes the 100-point interval.

[0127] The above rules set target score ranges for different types of service profile parameters. These score ranges not only determine the actual scores for the collected service profile parameters, but also set target thresholds for judging throughput indicators. The specific steps include:

[0128] Obtain the first throughput metric from the target service profile parameters;

[0129] Obtain the second throughput metric for Platform as a Service in each target score range;

[0130] Determine the proportion of the second throughput indicator in the first throughput indicator within each target score interval;

[0131] If the percentage is less than or equal to a preset value, the maximum score in the corresponding target score range will be used as the target threshold for the throughput indicator.

[0132] Referring to the example above, the second throughput indicator accounts for 90% of the first throughput indicator in the 100-point range, 80% in the 90-point range, 70% in the 80-point range, 60% in the 70-point range, and 50% in the 60-point range. The preset value is set to 60%. It can be seen that the proportion between the 70-point and 60-point ranges is less than or equal to the preset value, and 70 points is greater than 60 points. Therefore, 70 points is taken as the target threshold for the throughput indicator.

[0133] Step 1033: After searching the service profile parameters in the target score range, determine the actual score corresponding to each type of service profile parameter within the target period.

[0134] After determining the target score range, this embodiment of the invention needs to determine the actual score corresponding to each type of service profile parameter in the service profile based on these score ranges. This is because the score range is also set according to the type of target service profile parameter. Different types of target service profile parameters can have different floating parameters and index difference parameters (for example, the target service profile parameter for response time is 2s, the floating parameter is 0.3s, and the index difference parameter is 0.15s; the target service profile parameter for user concurrency is 5, the floating parameter is 2, and the index difference parameter is 1).

[0135] The resulting intervals will not be the same. In this case, the target score interval can be divided according to the type of the target service profile parameter, and a mark can be added to the divided interval. Then, when comparing the service profile parameters, the type of the service profile parameter is directly compared with the added mark to determine the score interval that this type of service profile parameter needs to be compared. The actual score corresponding to this type of service profile parameter is determined by comparing this type of service profile parameter in this interval.

[0136] It should be noted that the target score range is divided according to the type of target service profile parameters.

[0137] Among them, each target score interval under the division range corresponds to a target service profile parameter type;

[0138] Add a type marker to the partition interval. The type marker is used to indicate the type of the target service profile parameter in the current partition interval.

[0139] Select the first type of service profile parameter from the service profile parameters;

[0140] The first type is searched within the type tags to determine the target partitioning interval;

[0141] The service profile parameters of the first type are compared within the target segmentation range to determine the actual score corresponding to the service profile parameters of the first type.

[0142] Dividing the score range according to type makes it easier to narrow down the comparison range and improve the speed when determining the actual score later.

[0143] Step 1034: Weight the actual score corresponding to each type of service profile parameter by the target weight.

[0144] In this embodiment of the invention, after obtaining the actual score corresponding to each type of service profile parameter, the actual score corresponding to each type of service profile parameter is weighted. This is because the service profile threshold defined when defining the target parameters of the platform as a service is the comprehensive score of all target service profile parameters. Therefore, when making the comparison, it is also necessary to convert the actual score corresponding to each type of service profile parameter into a comprehensive score. This embodiment of the invention obtains the comprehensive score by weighted summation. Therefore, the actual score corresponding to each type of service profile parameter is weighted first.

[0145] Step 1035: Sum the weighted actual scores to obtain the comprehensive score of the service profile parameters within the target period.

[0146] In this embodiment of the invention, the scoring formula is set as: Score = Score a weight a +Score b weight b +Score c weight c +Score d weight d Therefore, in the above process, the actual scores corresponding to each type of service profile parameter are weighted, and the weighted results are summed to obtain the comprehensive score of the service profile parameters within the target period.

[0147] The above process yields a comprehensive score for the platform-as-a-service service profile parameters within the target period. This score helps us assess the overall performance and health of the service.

[0148] It should be noted that the self-management method of the platform-as-a-service provided in this embodiment of the invention can be implemented through three devices, for example, such as... Figure 3 As shown, the system includes a PaaS service parameter setting device, a PaaS service detection and alarm device, and a PaaS service lifecycle management device. The PaaS service parameter setting device is used to set resource parameters for the PaaS service, describing the specific resource size required by the PaaS service; system parameters, PaaS service-specific parameters, used to ensure normal service startup and operation; extended parameters, used to support user-defined parameters, used to ensure other service features and functions; service profile parameters, used to support user-defined parameters, used to ensure other service features and functions, and service profile thresholds, etc. These parameter values ​​are derived from empirical parameters solidified through business practice testing and industry data comparison; these parameters are used by the deployed PaaS service. The PaaS service detection and alarm device generates alarm messages based on built-in alarm thresholds, compares them with service profile thresholds, and generates operational suggestions. The PaaS service lifecycle management device updates the read PaaS service update parameter values ​​to new PaaS services in a rolling update manner.

[0149] Furthermore, the self-management process of the aforementioned platform-as-a-service can also be achieved through... Figure 4 The process involves the PaaS service parameter setting device creating and querying PaaS service specification resources based on the designed PaaS service specification parameters. The PaaS service detection and alarm device generates upgrade suggestions based on the PaaS service specification resources and built-in algorithms. Upon receiving the establishment, the PaaS service lifecycle management device first determines whether it is in manual mode. If so, it checks the PaaS service workload, the number of PaaS service workload replicas, and the upgrade strategy. After the user confirms, it obtains the currently used specification information and parses out the specification name required for subsequent operations. If it is not in manual mode, it directly obtains the currently used specification information and parses out the specification name required for subsequent operations. Based on the specification name required for subsequent operations, it obtains the specification information, organizes the PaaS service CR resources, detects changes in the PaaS service CR, and performs relevant update operations.

[0150] Reference Figure 5 The diagram illustrates the structure of a self-management device for a platform-as-a-service provided in an embodiment of the present invention. Figure 5 As shown, the device may include:

[0151] The data acquisition module 201 is used to collect key parameters of the platform as a service within the target period. The key parameters include CPU utilization, memory utilization, and service profile parameters. The service profile parameters include at least one type of parameter among response time, throughput, concurrent users, and mean time between failures.

[0152] The first upgrade module 202 is used to perform an upgrade operation on the platform as a service when it is determined that the CPU usage or memory usage exceeds a predefined alarm threshold.

[0153] The first generation module 203 is used to generate, according to preset rules, the comprehensive score of service profile parameters within the target period and the actual score corresponding to each type of service profile parameter when it is determined that the central processing unit utilization and memory utilization are both less than the predefined alarm threshold.

[0154] The second upgrade module 204 is used to upgrade the platform as a service when the overall score is determined to be less than a predefined service profile threshold.

[0155] The first degradation module 205 is used to perform a degradation operation on the platform as a service when the actual score of the throughput indicator is determined to be less than a predefined target threshold.

[0156] Optional, self-management mechanisms for Platform as a Service also include:

[0157] The first acquisition module is used to acquire the type and service performance level of the platform as a service under the target specification.

[0158] The first determination module is used to determine the target specification file name in the platform as a service under the target specification by the type of platform as a service and the service performance level. The higher the value of the service performance level, the better the service performance supported by the corresponding platform as a service.

[0159] The first definition module is used to define target parameters for the platform-as-a-service under the target specification. The target parameters include resource parameters, system parameters, extended parameters, and target service profile parameters. All target parameters are array-type structures. The array structures of resource parameters, system parameters, and extended parameters include fields specifying parameter names, fields specifying parameter values, and fields describing the function of the parameters. The array structure of target service profile parameters includes fields specifying parameter names, fields specifying parameter values, fields specifying the target weight of the parameters, and fields describing the function of the parameters.

[0160] The second determination module is used to determine the alarm thresholds for the use of central processing unit, memory and storage under the target specifications of the platform as a service by means of resource parameters.

[0161] The first protection module is used to ensure the normal startup and operation of the platform as a service under the target specifications through system parameters, and to ensure the normal use of the target functions of the platform as a service under the target specifications through extended parameters.

[0162] The second generation module is used to generate service profile thresholds for the platform as a service under the target specifications based on the target service profile parameters.

[0163] The first creation module is used to create target resource instances of the corresponding specifications in the target cluster by defining the target specification file name and target parameters under the target specification.

[0164] The first deployment module is used to deploy the Platform as a Service of the target specification in the target cluster using the target resource instance.

[0165] Optionally, the first generation module 203 specifically includes:

[0166] The first acquisition submodule is used to acquire predefined target service profile parameters, floating parameters, indicator difference parameters, and target weights for each type of target service profile parameter when it is determined that both the CPU utilization and memory utilization are less than the predefined alarm threshold.

[0167] The first determination submodule is used to determine the target score range for each type of target service profile parameter based on the target service profile parameters, the floating parameters, and the index difference parameters.

[0168] The second determination submodule is used to retrieve the service profile parameters within the target score range and then determine the actual score corresponding to each type of service profile parameter within the target period.

[0169] The first weighting submodule is used to weight the actual score corresponding to each type of service profile parameter by the target weight.

[0170] The first summation submodule is used to sum the weighted actual scores to obtain the comprehensive score of the service profile parameters within the target period.

[0171] Optionally, predefined target service profile parameters include at least one type of parameter among target response time, target throughput metric, target concurrent users, and target mean time between failures metric.

[0172] The first determination submodule specifically includes:

[0173] The first determining unit is used to determine the first score range for any target service profile parameter by using the target service profile parameter and the floating parameter.

[0174] The second determining unit is used to determine the second score interval for any target service profile parameter by using the target service profile parameter, the floating parameter, and the index difference parameter.

[0175] The third determining unit is used to determine the target score range of any target service profile parameter based on the first score range and the second score range.

[0176] The second determination submodule specifically includes:

[0177] The segmentation submodule is used to divide the target score range according to the type of target service profile parameters. Each segmentation range corresponds to a type of target service profile parameter.

[0178] Add a tagging submodule to add type tags to the partitioned intervals. The type tags are used to indicate the type of the target service profile parameters in the current partitioned interval.

[0179] The selection submodule is used to select the first type of service profile parameter from the service profile parameters.

[0180] The retrieval submodule is used to search for the first type in the type tags and determine the target division interval.

[0181] The comparison submodule is used to compare the service profile parameters of the first type within the target division range to determine the actual score corresponding to the service profile parameters of the first type.

[0182] Optional, self-management mechanisms for Platform as a Service also include:

[0183] The second acquisition module is used to acquire information about the copies of services running in the Platform as a Service and the upgrade / downgrade operation mode.

[0184] The first notification module is used to send a notification message to the user if it is determined that a single copy of the service exists in the platform as a service and the upgrade / downgrade operation mode is manual.

[0185] The third acquisition module is used to acquire the upgrade / downgrade operation strategy of the workload in the platform as a service.

[0186] The second notification module is used to send a notification message to the user when it is determined that the upgrade / downgrade operation strategy is not a rolling update and the upgrade / downgrade operation mode is manual.

[0187] Optionally, the second upgrade module 204 specifically includes:

[0188] The third determination submodule is used to determine the first resource parameter that the platform as a service needs to be reconfigured when the overall score is less than a predefined service profile threshold.

[0189] The second acquisition submodule is used to obtain the name of the first specification file of the resource instance corresponding to the current platform as a service.

[0190] The fourth determination submodule is used to search the target specification file by the name of the first specification file to determine the second resource parameter corresponding to the resource instance.

[0191] The upgrade submodule is used to perform upgrade operations on the platform as a service based on the first resource parameter and the second resource parameter.

[0192] The self-management method for a Platform as a Service (PaaS) provided in this invention collects key parameters of the PaaS within a target period. These key parameters include CPU utilization, memory utilization, and service profile parameters. Service profile parameters include at least one type of parameter selected from response time, throughput, concurrent users, and mean time between failures (MTBF). If CPU utilization or memory utilization exceeds a predefined alarm threshold, an upgrade operation is performed on the PaaS. By judging CPU utilization or memory utilization, it is determined whether the performance degradation of the PaaS is due to unreasonable resource parameter configuration. If a bottleneck is identified, an upgrade operation is performed to resolve the issue. If both CPU utilization and memory utilization are less than the predefined alarm threshold, a system is generated according to preset rules. The system calculates the comprehensive score of service profile parameters within the target period and the actual score corresponding to each type of service profile parameter. If the comprehensive score is less than a predefined service profile threshold, an upgrade operation is performed on the platform-as-a-service (PAS). If the actual score of the throughput indicator is less than a predefined target threshold, a downgrade operation is performed on the PAS. These operations prevent the PAS from experiencing performance degradation, reduced throughput, and high latency in request processing due to unreasonable resource parameter configuration. This embodiment of the invention determines whether the performance of the PAS is reduced due to unreasonable resource parameter configuration from different dimensions by judging CPU utilization, memory utilization, throughput indicators, and service profile parameters. Then, by modifying the resource parameter configuration through upgrade or downgrade operations, the performance of the PAS is improved.

[0193] This invention also provides a communication device, such as... Figure 6 As shown, it includes a processor 301, a communication interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304.

[0194] Memory 303 is used to store computer programs;

[0195] When processor 301 executes a program stored in memory 303, it performs the following steps:

[0196] Key parameters of the platform-as-a-service (PAS) are collected within the target period. These key parameters include CPU utilization, memory utilization, and service profile parameters, including response time.

[0197] A parameter of at least one of the following types: throughput, concurrent users, and mean time between failures (MTBF).

[0198] If the CPU usage or memory usage is determined to be greater than a predefined alarm threshold, the platform as a service will be upgraded.

[0199] If the CPU utilization and memory utilization are both less than a predefined alarm threshold, the comprehensive score of the service profile parameters within the target period and the actual score corresponding to each type of service profile parameter are generated according to preset rules.

[0200] If the overall score is determined to be less than a predefined service profile threshold, the platform as a service will be upgraded.

[0201] If the actual score of the throughput metric is determined to be less than a predefined target threshold, the platform-as-a-service will be downgraded.

[0202] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0203] The communication interface is used for communication between the aforementioned terminal and other devices.

[0204] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0205] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0206] The present invention also provides a readable storage medium that, when the instructions in the storage medium are executed by the processor of an electronic device, enables the electronic device to perform the self-management method of the Platform as a Service described above.

[0207] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0208] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. The structure required to construct such a device is readily apparent from the above description. Furthermore, this invention is not directed to any particular programming language. It should be understood that the contents of the invention described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of the invention.

[0209] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0210] Similarly, it should be understood that, in order to simplify the invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed invention requires more features than expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into this detailed description, wherein each claim itself is a separate embodiment of the invention.

[0211] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0212] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the sorting device according to the present invention. The present invention can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0213] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0214] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0215] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0216] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0217] It should be noted that the various data-related processes in the embodiments of this application are carried out in compliance with the relevant data protection laws and policies of the country where the location is located, and with the authorization granted by the owner of the corresponding device.

Claims

1. A method for self-management of a platform as a service, characterized in that, The method comprises: collecting key parameters of the platform as a service in a target period, wherein the key parameters comprise a central processing unit usage rate, a memory usage rate, and service image parameters, the service image parameters comprising a response time, a throughput index, a number of concurrent users, and a mean time between failures index; in a case where the central processing unit usage rate or the memory usage rate is greater than a predefined alarm threshold, performing an upgrade operation on the platform as a service; in a case where the central processing unit usage rate and the memory usage rate are both less than the predefined alarm threshold, generating a comprehensive score of the service image parameters in the target period and an actual score corresponding to each type of service image parameter according to a preset rule, the comprehensive score of the service image parameters being obtained by weighting the actual score corresponding to each type of service image parameter by a predefined target weight, the actual score corresponding to each type of service image parameter being determined after searching in a target score interval of each type of target service image parameter, the target score interval of each type of target service image parameter being determined according to a predefined target service image parameter, a floating parameter, and an index difference parameter; in a case where the comprehensive score is less than a predefined service image threshold, if the actual score of the throughput index is greater than or equal to a predefined target threshold, performing an upgrade operation on the platform as a service, and if the actual score of the throughput index is less than the predefined target threshold, performing a downgrade operation on the platform as a service.

2. The method of claim 1, wherein, Before the collecting key parameters of the platform as a service in a target period, the method further comprises: obtaining a type and a service performance level of the platform as a service under a target specification; determining a target specification file name of the platform as a service under the target specification by the type and the service performance level of the platform as a service, wherein the greater the value of the service performance level is, the better the service performance supported by the corresponding platform as a service is; defining target parameters for the platform as a service under the target specification, the target parameters comprising resource parameters, system parameters, extension parameters, and target service image parameters, the target parameters all being array type structures, wherein the array structure of the resource parameters, the system parameters, and the extension parameters comprises a field of a specified parameter name, a field of a specified parameter value, and a field of a description of a parameter function, and the array structure of the target service image parameters comprises a field of a specified parameter name, a field of a specified parameter value, a field of a target weight of a specified parameter, and a field of a description of a parameter function; determining alarm thresholds of a central processing unit, memory, and storage used by the platform as a service under the target specification by the resource parameters; guaranteeing normal startup and operation of the platform as a service under the target specification by the system parameters, and guaranteeing normal use of target functions of the platform as a service under the target specification by the extension parameters; generating a service image threshold of the platform as a service under the target specification by the target service image parameters; creating a target resource instance of a corresponding specification in a target cluster by the target specification file name and the target parameters defined under the target specification. The target specification platform as a service is deployed in the target cluster through the target resource instance.

3. The method of claim 1, wherein, The method further comprises: determining a target service image parameter, a floating parameter, an index difference parameter, and a target weight of each type of target service image parameter when the central processor usage and the memory usage are both less than the predefined alarm threshold; The method further comprises: determining a target score interval of each type of target service image parameter according to the target service image parameter, the floating parameter, and the index difference parameter; The method further comprises: determining an actual score of each type of service image parameter in the target period after searching the service image parameter in the target score interval; The method further comprises: weighting the actual score of each type of service image parameter by the target weight; The method further comprises: summing the weighted actual scores to obtain a comprehensive score of the service image parameter in the target period. The target service image parameter comprises a target response time, a target throughput index, a target number of concurrent users, and a target mean time between failures index.

4. The method of claim 3, wherein, The method further comprises: determining a first score interval of any target service image parameter according to the target service image parameter and the floating parameter; The method further comprises: determining a second score interval of any target service image parameter according to the target service image parameter, the floating parameter, and the index difference parameter; The method further comprises: determining a target score interval of any target service image parameter according to the first score interval and the second score interval. The method further comprises: dividing the target score interval according to the type of target service image parameter, wherein the target score interval in each division range corresponds to one type of target service image parameter; The method further comprises: adding a type label on the division interval, wherein the type label is used to indicate the type of target service image parameter in the current division interval; 5. The method of claim 4, wherein, The method further comprises: selecting a first type of service image parameter from the service image parameter; The method further comprises: searching the target division interval in the type label to determine the target division interval of the first type; The method further comprises: comparing the first type of service image parameter in the target division interval to determine the actual score of the first type of service image parameter. The method further comprises: obtaining copy information of a running service in the platform as a service and an upgrade / downgrade operation mode when the actual score of the throughput index is less than the predefined target threshold; ​ ​ 6. The method of claim 1, wherein, ​ ​ ​ ​ In a case where it is determined that the upgrade / downgrade operation strategy is not a rolling update and the upgrade / downgrade operation mode is a manual mode, a prompt message is sent to a user.

7. The method of claim 2, wherein, In a case where it is determined that the comprehensive score is less than a predefined service image threshold, if the actual score of the throughput index is greater than or equal to a predefined target threshold, the platform as a service is upgraded. In a case where it is determined that the comprehensive score is less than a predefined service image threshold, if the actual score of the throughput index is greater than or equal to a predefined target threshold, a first resource parameter of the platform as a service that needs to be reconfigured is determined. A name of a first specification file of a resource instance corresponding to the current platform as a service is acquired. The second resource parameter corresponding to the resource instance is determined by searching the target specification file through the name of the first specification file. The platform as a service is upgraded based on the first resource parameter and the second resource parameter.

8. A platform as a service self-management apparatus, characterized by, Comprise: The acquisition module is configured to acquire key parameters of the platform as a service in a target period, wherein the key parameters comprise a central processing unit (CPU) usage rate, a memory usage rate, service image parameters, and the service image parameters comprise a response time, a throughput index, a number of concurrent users, and a mean time between failures (MTBF) index. The first upgrade module is configured to upgrade the platform as a service in a case where the CPU usage rate or the memory usage rate is greater than a predefined alarm threshold. The first generation module is configured to generate a comprehensive score of the service image parameters in the target period and an actual score corresponding to each type of service image parameter according to a preset rule in a case where the CPU usage rate and the memory usage rate are both less than the predefined alarm threshold, wherein the comprehensive score of the service image parameters is obtained by weighting the actual score corresponding to each type of service image parameter by a predefined target weight, the actual score corresponding to each type of service image parameter is determined after searching in a target score interval of each type of target service image parameter, and the target score interval of each type of target service image parameter is determined according to a predefined target service image parameter, a floating parameter, and an index difference parameter. The second upgrade module is configured to upgrade the platform as a service in a case where the comprehensive score is less than a predefined service image threshold, if the actual score of the throughput index is greater than or equal to a predefined target threshold, and downgrade the platform as a service in a case where the actual score of the throughput index is less than the predefined target threshold.

9. A communication device, characterized by Comprise: A transceiver, a memory, a processor, and a program stored in the memory and executable on the processor; The processor is configured to read the program in the memory to implement the steps in the self-management method of the platform as a service according to any one of claims 1-7.

10. A readable storage medium for storing a program, characterized by The program stored in the memory is executed by the processor to implement the steps in the self-management method of the platform as a service according to any one of claims 1-7.

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