CN115022173B - Service capacity expansion method, device, equipment and storage medium - Google Patents
Service capacity expansion method, device, equipment and storage medium Download PDFInfo
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- CN115022173B CN115022173B CN202210506164.6A CN202210506164A CN115022173B CN 115022173 B CN115022173 B CN 115022173B CN 202210506164 A CN202210506164 A CN 202210506164A CN 115022173 B CN115022173 B CN 115022173B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The application provides a service capacity expansion method, device, equipment and storage medium. The method comprises the following steps: determining a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes; and carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode. The method and the device can be pre-configured with various capacity expansion modes applicable to different service scenes, ensure comprehensive applicability of service capacity expansion, realize automatic capacity expansion of service in different scenes, and improve accuracy of service capacity expansion by adopting a combined capacity expansion mode of a spam capacity expansion mode and a target capacity expansion mode.
Description
Technical Field
The embodiment of the application relates to the technical field of data processing, in particular to a method, a device, equipment and a storage medium for expanding service capacity.
Background
With the rapid development of internet technology, the deployment iteration frequency of service instances running on service clusters facing external users is higher and higher. When the network traffic is continuously increased, the service overhead is increased, so that the pressure caused by the network traffic needs to be shared by expanding the service capacity, that is, expanding the number of service instances corresponding to the pressure bearing, thereby reducing the load of each service instance.
At present, the flow evaluation can be usually carried out manually, so that the service capacity expansion is manually realized; but service expansion is inefficient and error prone. Or k8s in the cloud primary has certain service expansion capability, only supports service expansion under the dimension of a central processing unit (Central Processing Unit, simply called CPU), has certain expansion limitation, and cannot meet a large number of service expansion scenes.
Disclosure of Invention
The application provides a method, a device, equipment and a storage medium for expanding service, which realize automatic expansion of service under different scenes and improve the accuracy and the overall applicability of the expansion of the service.
In a first aspect, an embodiment of the present application provides a method for service expansion, where the method includes:
determining a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes;
and carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode.
In a second aspect, an embodiment of the present application provides a service capacity expansion device, where the device includes:
the capacity expansion mode determining module is used for determining a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes;
And the service capacity expansion module is used for carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode.
In a third aspect, an embodiment of the present application provides an electronic device, including:
the system comprises a processor and a memory, wherein the memory is used for storing a computer program, and the processor is used for calling and running the computer program stored in the memory to execute the service capacity expansion method provided in the first aspect of the application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program that causes a computer to perform a method of service expansion as provided in the first aspect of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a computer program/instruction, characterized in that the computer program/instruction, when executed by a processor, implements a method of service expansion as provided in the first aspect of the present application.
The method, the device, the equipment and the storage medium for expanding the service provided by the embodiment of the application can be used for pre-configuring various expansion modes applicable to different service scenes, so that the comprehensive applicability of the service expansion is ensured. For the current service, firstly, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from a preconfigured multiple capacity expansion mode, and then carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode, so that automatic capacity expansion of the service under different scenes is realized, and the accuracy of service capacity expansion is improved by adopting a joint capacity expansion mode of the spam capacity expansion mode and the target capacity expansion mode.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for service expansion according to an embodiment of the present application;
fig. 2 is a schematic diagram of a service capacity expansion process according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating another method of service expansion according to an embodiment of the present application;
fig. 4 is a schematic diagram of a flow scheduling process in a pressure measurement capacity expansion mode according to an embodiment of the present disclosure;
fig. 5 is a schematic diagram of a BP neural network used in a predictive capacity expansion mode according to an embodiment of the present application;
FIG. 6 is an exemplary block diagram of a preprogrammed task execution plan in a planned expansion mode, as shown in an embodiment of the present application;
FIG. 7 is a schematic block diagram of an apparatus for service expansion according to an embodiment of the present application;
fig. 8 is a schematic block diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In consideration of the problems of easy error and capacity expansion limitation of manual capacity expansion and capacity expansion under a single strategy, a novel service capacity expansion mode is designed. For various service scenes with capacity expansion requirements, a plurality of capacity expansion modes are preconfigured, so that the overall applicability of the service capacity expansion is determined. Then, for any service with capacity expansion requirement, the combined capacity expansion is carried out on the current service by utilizing the spam capacity expansion mode in the multiple capacity expansion modes and the calibrated target capacity expansion mode under the current service, so that the automatic capacity expansion of the service under different scenes is realized, and the accuracy of the capacity expansion of the service is improved.
Fig. 1 is a flowchart of a method for service expansion according to an embodiment of the present application. Referring to fig. 1, the method may specifically include the steps of:
s110, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from the preconfigured multiple capacity expansion modes.
In order to realize automatic capacity expansion of services in different scenes, the method and the device can be used for pre-configuring various capacity expansion modes applicable to the different scenes by analyzing the capacity expansion requirements of the services in the different scenes.
It should be understood that the service in the present application may be a service supported by various applications facing external users, for example, a service focusing on a C-terminal oriented user (To Customer, abbreviated as To ToC) type of service in a business front, and a ToC type service is mainly used for products responsible for being directly provided To an individual user for use, such as an application program, a public number, an applet, and the like. In addition, the service expansion can be to split the flow pressure brought by the corresponding service by expanding the number of service instances used for bearing the operation pressure of the corresponding service, so that the load pressure of each service instance is reduced, and the high efficiency of service operation is ensured.
As an alternative implementation scheme in the present application, the multiple capacity expansion modes in the present application may include four modes of an automatic capacity expansion mode, a pressure measurement capacity expansion mode, a predicted capacity expansion mode, and a planned capacity expansion mode.
The basic capacity expansion functions of the four capacity expansion modes are described below:
1) The automatic capacity expansion mode may be: and automatically expanding the service by judging whether the online traffic of the service exceeds a set traffic threshold in real time. At this time, the automatic capacity expansion mode is applicable to any service scenario, and supports comprehensive execution of service capacity expansion, so that the automatic capacity expansion mode can be used as a spam scheme of other capacity expansion modes. That is, the automatic capacity expansion mode is a spam capacity expansion mode in the present application.
2) The pressure measurement capacity expansion mode can be as follows: service operational limits are tested by periodically scheduling traffic to determine service stability. And then, in the service pressure measurement process, judging whether an automatic capacity expansion mode is reached or not to carry out linkage capacity expansion on the service. At this time, the pressure measurement capacity expansion mode can be suitable for burst service scenes such as rush-red packets, second killing and the like, and can ensure sufficient service capacity by supporting advanced capacity expansion in the service scenes, thereby ensuring the stability of the service in the scenes.
3) The predictive capacity mode may be: by predicting the future flow trend in the service running process, the service is expanded in advance by combining the automatic capacity expansion mode, so that the problem that the automatic capacity expansion mode cannot expand in time due to overlong data loading time is avoided. At this time, the predictive capacity expansion mode is applicable to a service scenario where service data loading time is too long and service capacity expansion time is too long, and supports advanced capacity expansion by predicting future traffic trend of the service, thereby ensuring success rate of service capacity expansion.
4) The planned capacity expansion mode may be: for various timing jobs existing under the service, service instances dedicated to executing such timing jobs are expanded in advance, thereby individually providing timing expansion for the service for which the periodic expansion demand exists. In this case, the planned expansion mode may be applied to a scenario of a timing service, for example, a service involving reconciliation, post-loan run, and the like.
It should be noted that, for the four capacity expansion modes, as shown in fig. 2, the application stores relevant configuration data defined by the user in each capacity expansion mode in a configuration service (Config-server), and the underlying database used by the configuration service (Config-server) may be a relational database management system MySQL database.
In the application, for each service in different scenes, a matched expansion mode may be pre-specified for each service according to the service scene. That is, for each service hosted on-line, each service may be associated with a respective one of the preconfigured multi-capacity expansion modes per each service scenario. Furthermore, in the current service operation process, the spam expansion mode can be determined from the preconfigured multiple expansion modes. Meanwhile, according to the calibrated capacity expansion mode information under the current service, a target capacity expansion mode applicable to the current service can be determined from the multiple capacity expansion modes.
S120, the combined capacity expansion is carried out on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode.
After determining the spam expansion mode and the target expansion mode applicable under the current service, considering that the spam expansion mode is a real-time expansion scheme for judging whether the online traffic of the service exceeds a preset traffic threshold in real time. Therefore, in the running process of the current service, whether the service expansion is needed currently or not can be judged by analyzing the online flow of the current service in real time so as to ensure the timeliness of the service expansion.
Moreover, considering that the target capacity expansion mode is in the real-time running process of the current service, the flow state of the current service in a future period of time can be analyzed periodically in advance. Therefore, in the running process of the current service, the corresponding service instance can be expanded for the current service in advance according to the future flow state which is analyzed in advance at regular intervals, so that the running efficiency of the current service is ensured in advance.
However, since the real-time traffic state of the current service in the running process is affected by the real-time service request, there may be a certain difference between the real-time traffic state of the current service at a certain moment and the traffic state analyzed in advance for the moment, so that after the current service is expanded in advance by using the target capacity expansion mode, the capacity expansion requirement of the current service at the moment may not be met yet. Therefore, the method and the device can further combine the spam capacity expansion mode on the basis of utilizing the target capacity expansion mode to perform the prior capacity expansion on the current service, and perform the secondary capacity expansion on the current service in time when the prior capacity expansion of the target capacity expansion mode does not reach the capacity expansion requirement, so that the accuracy of the service capacity expansion is ensured through the combined capacity expansion of different capacity expansion modes on the service.
In addition, according to the requirements of the services under different scenes on execution timeliness, the method and the device can set corresponding priorities for the services so as to ensure the high efficiency of service operation. In addition, for different services under the same priority, when the service priority is divided for the first time, an identifier is further marked for the different services under the same priority on the basis of the priority, so as to distinguish the grades of the different services under the same priority. For example, a service has a priority label of 2-100, i.e. represents a service with a priority of 2, and its ranking weight under the second priority is assigned a value of 100.
As an optional implementation scheme in the present application, before performing joint capacity expansion on a current service, the present application further determines an adaptive resource pool of the current service according to a service priority of the current service, so as to perform joint capacity expansion on the current service in the adaptive resource pool.
That is, for the resource pools used to provide the deployment service instances, the resource pools of different priorities are also partitioned according to the requirements of the service for execution timeliness. The higher the priority is, the higher the execution efficiency of the service instance deployed in the resource pool is, and the lower the priority is, the lower the execution efficiency of the service instance deployed in the resource pool is correspondingly. Illustratively, as in the online resource pool and the hybrid resource pool in fig. 2, the online resource pool has a higher priority than the hybrid resource pool. Further, before the joint capacity expansion is performed on the current service, the service priority of the current service is first analyzed. Then, an adaptive resource pool suitable for the service priority is determined from a plurality of resource pools under different preset priorities. Furthermore, when the spam capacity expansion mode and the target capacity expansion mode are utilized to carry out joint capacity expansion on the current service, the service instance expanded at the current time of the current service can be directly arranged in the adaptive resource pool, so that the expanded service instance meets the requirement of the current service on execution timeliness, and the accuracy of service capacity expansion is ensured.
The technical scheme provided by the embodiment of the application can be preconfigured into various capacity expansion modes applicable to different service scenes, so that the comprehensive applicability of service capacity expansion is ensured. For the current service, firstly, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from a preconfigured multiple capacity expansion mode, and then carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode, so that automatic capacity expansion of the service under different scenes is realized, and the accuracy of service capacity expansion is improved by adopting a joint capacity expansion mode of the spam capacity expansion mode and the target capacity expansion mode.
As an alternative implementation scheme in the present application, in order to ensure the overall applicability of service capacity expansion, the present application may perform joint capacity expansion on the current service by using a spam capacity expansion mode in a preconfigured multiple capacity expansion mode and a target capacity expansion mode calibrated under the current service. At this time, in the running process of the current service, in addition to the monitoring analysis of the current service real-time traffic state required when the spam expansion mode is used for real-time expansion, the monitoring analysis of the traffic state of the current service in the future period, which is predetermined when the target expansion mode is used for prior expansion, is required. As shown in fig. 2, after relevant configuration data defined by the user in each capacity expansion mode is stored in a configuration service (Config-server), each capacity expansion mode configured by the user is known through a Rule engine (Rule-engine). Then, whether to trigger a certain capacity expansion mode is judged by combining service flow usage collected by the current service, namely corresponding flow monitoring data, through a Monitor-server. When triggering to expand the current service by using a certain expansion mode, an adaptive resource pool of the current service, such as an Online resource pool (Online-service) and a Mixed-service resource pool (Mixed-service), is selected from a plurality of resource pools according to rules of a Rule engine (Rule-engine) through a Scheduler. And expanding a corresponding number of service instances in the adaptive resource pool to realize accurate capacity expansion of the current service. And, monitor the flow situation of every service instance in real time in the course of running of the present service through the monitoring agent (Monitor-agent), and report every flow monitoring data of the present service under service dimension and machine dimension to the monitoring service (Monitor-server), in order to judge whether to trigger a certain expansion mode in the course of running of the present service continuously by the Rule engine (Rule-engine). Next, a detailed explanation will be given of a specific procedure for performing joint capacity expansion of the current service using the spam capacity expansion mode and the target capacity expansion mode.
Fig. 3 is a flowchart of another service capacity expansion method according to an embodiment of the present application. Referring to fig. 3, the method may specifically include the steps of:
s310, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from the preconfigured multiple capacity expansion modes.
S320, performing basic capacity expansion on the current service by utilizing the service flow state predetermined by the target capacity expansion mode.
As an alternative implementation scheme in the present application, considering that the target capacity expansion mode is in the real-time running process of the current service, the possible traffic state of the current service in a future period of time is analyzed periodically in advance. Therefore, according to the future possible traffic state of the current service which is periodically analyzed in advance in the target capacity expansion mode, the possible traffic state of the current service in the subsequent operation process, that is, the predetermined traffic state of the service in the application, can be determined in advance. And then, judging whether the predetermined service flow state exceeds the service flow extremum or not by combining the service flow extremum set in the spam capacity expansion mode. Furthermore, when the capacity expansion requirement of the current service in the subsequent operation process is analyzed in advance, the corresponding prior capacity expansion operation can be performed on the current service in advance, the operation efficiency after the capacity expansion of the service is ensured, and the problem of untimely capacity expansion of the service is avoided.
The target capacity expansion mode in the present application may be one of a pressure measurement capacity expansion mode, a prediction capacity expansion mode, and a planned capacity expansion mode, for example. Next, a description will be given of a process of performing basic expansion of the current service using the barometric expansion mode, the predictive expansion mode, or the planned expansion mode, respectively.
1. The target capacity expansion mode is a pressure measurement capacity expansion mode
In the service scenario of burst such as rush-red packets, second killing, etc., on-line service is generally required to enable sufficient service capacity through capacity expansion in advance so as to ensure service operation stability. Therefore, the flow can be scheduled periodically by adopting the pressure measurement capacity expansion mode so as to test the stability when burst service occurs.
As an optional implementation scheme in the present application, the basic capacity expansion of the current service by using the pressure measurement capacity expansion mode may specifically be: gradually dispatching service flow to a machine room to be tested under the current service; and performing basic capacity expansion on the current service based on the real-time flow parameters after the dispatching of the machine room to be tested and the service flow extremum in the spam capacity expansion mode.
That is, for each machine room represented by each internet data center (Internet Data Center, abbreviated as IDC) running each service instance under the current service, as shown in fig. 4, the present application may select one of the machine rooms from each machine room under the current service as the machine room to be tested. And then, continuously dispatching corresponding service flow to the machine room to be tested by steps, so that the service flow of the machine room to be tested is continuously increased. Furthermore, the service operation limit is tested by continuously increasing the service flow in the machine room to be tested, so that when the real-time flow parameter after the dispatching of the machine room to be tested exceeds the service flow extreme value in the spam capacity expansion mode, corresponding capacity expansion operation is performed on the current service in advance, the current service can realize basic prior capacity expansion before the emergency occurs, and the problem that capacity expansion is not timely because capacity expansion is started when the emergency service occurs is avoided.
The service traffic can be gradually scheduled to the machine room to be tested under the current service by one of the two modes of cutting the machine room and simulating the traffic. Therefore, the service flow scheduled in the machine room to be measured under pressure can be the service flow in other machine rooms under the current service or the simulated flow under the current service.
Firstly, in the machine room cutting mode, as shown in fig. 4, the service flow in charge of other machine rooms (such as IDC1 in fig. 4) except the machine room to be tested under the current service can be gradually switched into the machine room to be tested (such as IDC2 in fig. 4) by the level of F5. At this time, the flow switching process in the machine room cutting mode is: 1% - >5% - >10% - >20% - >50% - >100%. Then, in the process of gradually increasing the service flow in the machine room to be tested, if the real-time flow parameter of the service instance deployed in the machine room to be tested exceeds the service flow extremum in the spam capacity expansion mode, the current service is subjected to basic capacity expansion through the spam capacity expansion mode.
It should be noted that, in order to ensure the operation accuracy of the current service, before the service flow is scheduled to the machine room to be tested gradually, a hook callback check is set in advance, so as to detect whether the core index of the current service is abnormal in real time in the flow scheduling process. And if the core index of the current service is abnormal in the flow dispatching process of the machine room, triggering a fusing operation to terminate the flow dispatching of the service to the machine room to be tested in a machine room dispatching mode, and controlling the current service to fall back to the state before the machine room is switched, so that the accurate operation of the current service is ensured.
Secondly, considering that partial services exist in subsystem or even module level pressure measurement capacity expansion scenes, the pressure measurement capacity expansion mode of the machine room cutting mode is not fine enough. Therefore, the method and the device can adopt a flow simulation mode to gradually dispatch corresponding service flow to the machine room to be pressed. The analog traffic in the present application may be traffic that is self-structured, or may be an online log of current service playback, and the type of the analog traffic is not limited.
It should be noted that, since the analog traffic is performed in the service instance under the current service, it is necessary to distinguish from the real traffic data under the current service. For example, if there is a dependency on databases like MySQL, redis, etc. in the current service for persistent storage, dirty data features and data expiration times under the simulated traffic need to be marked in advance in the simulated traffic to avoid contaminating the real data on the current service line. Moreover, if a model constructed by machine learning/deep learning is used in the current service, data in the simulated flow is marked so as to avoid screening the simulated flow during model training, and the model accuracy is prevented from being influenced by the simulated flow of the simulation type during model training.
2. The target capacity expansion mode is a prediction capacity expansion mode
In a service scene of overlong service data loading time and overlong service capacity expansion time, the future flow trend of the current service can be predicted by predicting the capacity expansion mode, so that the advanced capacity expansion is realized, and the success rate of the service capacity expansion is further ensured.
As an optional implementation scheme in the present application, the base capacity expansion of the current service by using the predicted capacity expansion mode may specifically be: predicting corresponding future flow trends based on historical service flows of the current service; and based on the future flow trend and the service flow extremum in the spam capacity expansion mode, carrying out basic capacity expansion on the current service in advance.
Optionally, two modes of filtering algorithm and neural network can be adopted in the application to predict the corresponding future flow trend according to the historical service flow of the current service. And then, according to the comparison situation of the maximum flow parameter in the future flow trend and the service flow extremum in the spam capacity expansion mode, carrying out basic capacity expansion on the current service in advance.
First, for a more linear historical service flow under the current service, a Kalman filtering algorithm can be adopted to predict the future flow trend of the current service, and the actual duration of service start can be analyzed.
Secondly, for the nonlinear historical service traffic under the current service, a Back Propagation (BP) neural network can be trained in advance for predicting the time sequence of the future traffic trend under the current service.
As shown in fig. 5, the input parameters of the BP neural network in the present application may include a time point, a process CPU utilization, a Memory Device (MEM) utilization, a time consuming (cost time), a service per second query rate (QPS), a connection number, and a module start time. The output parameter is the CPU utilization rate corresponding to the current service at a certain time point. Moreover, the number of nodes at the input layer and the number of nodes at the output layer in the BP neural network are determined. Hidden layers can use empirical formulas
Where l is the number of hidden layer neurons, n is the number of input layer neurons, m is the number of output layer neurons, and α is a constant between 1 and 10. In this application, n=6 and m=1, and the number of neurons in the hidden layer can be determined to be between 4 and 13 by using the above formula, and in this application, the number of neurons in the hidden layer can be selected to be 6. Then, the sigmoid tangent function tansig is selected as the excitation function to train the BP neural network.
3. The target capacity expansion mode is a planned capacity expansion mode
Under the service scene of timing tasks such as checking, post-loan batch running and the like, the planning capacity expansion mode supports the custom arrangement of the service side on each timing operation existing under the current service, namely, the time point and the sequence of the operation starting by the user decision. As shown in fig. 6, the present application may customize a directed acyclic graph (Directed Acyclic Graph, abbreviated as DAG) to schedule the execution plan sequence of each timing job (job) under the current service, thereby obtaining a corresponding task execution plan. As shown in FIG. 6, job1 will boot at 2:00AM, job2 will boot at 2:10AM, and job3 will boot at 2:30 AM. Then, the method can trigger the job4 after the job1 is completed, can trigger the job5 after the job2 and the job3 are both completed, and finally, the method can trigger the execution of the job6 after the job4 and the job5 are both completed.
As an optional implementation scheme in the present application, the base capacity expansion of the current service by using the predicted capacity expansion mode may specifically be: based on a task execution plan pre-arranged under the current service, performing basic capacity expansion on the current service so as to run the task execution plan on the expanded service instance; if the task execution plan is completed, destroying the service instance after the capacity expansion.
That is, for each timing job under the current service, a pre-scheduled task execution plan including the execution time and execution order of each timing job may be acquired in advance. Then, before the earliest job start time, each task execution plan pre-arranged under the current service can be expanded in advance, and a plurality of service instances dedicated to executing each task execution plan can be expanded in advance. Such timed jobs within the task execution plan may then be run exclusively by the expanded service instance as the individual jobs are initiated at regular times. The service instance expanded by the planned expansion mode has task-specific characteristics, and is not used in support of other service jobs except for the task execution plan. Therefore, after all timing jobs in the task execution plan are executed, the service instance after the capacity expansion is timely destroyed to release corresponding service resources.
It should be noted that, because the service instance expanded by the planned expansion mode has a low requirement on the service execution efficiency, the service priority of the current service in the planned expansion mode is low. Thus, at the time of actual expansion, the actual expansion operation is performed by the scheduler (scheduler) to schedule into a lower priority resource pool (e.g., a hybrid resource pool).
S330, auxiliary capacity expansion is carried out on the current service by utilizing the configured service flow extremum and the real-time flow parameters of the current service in the spam capacity expansion mode.
On the basis of performing prior expansion on the current service by utilizing the target expansion mode, the situation that the prior expansion of the target expansion mode possibly does not meet the actual expansion requirement is considered. Therefore, the method and the device can further combine the spam expansion mode, and judge whether the prior expansion of the target expansion mode reaches the actual expansion requirement or not by comparing the real-time flow parameter of the current service with the service flow extremum in the spam expansion mode. When the real-time flow parameter of the current service exceeds the service flow extreme value in the spam capacity expansion mode, the prior capacity expansion of the target capacity expansion mode is described to not reach the actual capacity expansion requirement, and then the auxiliary capacity expansion is further carried out on the current service in time, so that the service capacity expansion accuracy is ensured through the combined capacity expansion of different capacity expansion modes.
As an alternative implementation in the present application, the spam expansion mode in the present application is an automatic expansion mode. Therefore, the auxiliary capacity expansion of the current service by using the spam capacity expansion mode can be specifically: and if the real-time flow parameter of the current service is larger than the service flow extremum in the spam capacity expansion mode, carrying out iterative capacity expansion on the current service based on the number of the current service instances under the current service and a preset iterative capacity expansion rule.
Wherein the service traffic extremum within the spam dilatation mode includes at least one of service traffic usage, service response time, and service query rate per second.
It should be understood that the present application may configure the service traffic usage described above by setting the CPU mean, minimum, maximum, etc. of the current service process. For example, when the process CPU utilization of the current service reaches 50%, the current service may be expanded. Further, the service response time is configured by setting an average value, a 90-ary value, or a 99-ary value of the response time of the current service process.
According to one aspect of the application, it is determined whether the real-time traffic parameter of the current service is greater than a service traffic extremum within the spam dilatation mode by at least one of service traffic usage, service response time, and service query per second rate. Then, when determining to expand the current service, the present application may determine, according to a preset iterative expansion rule, the number of service instances that need to be expanded each time based on the number of current service instances under the current service, and analyze whether the real-time flow parameter after expansion is lower than the expansion expected value. If the real-time flow parameter after analysis and expansion is higher than the expected expansion value, continuing expansion based on the number of the current service instances, and recursively iterating expansion until the real-time flow parameter is lower than the expected expansion value.
It should be noted that, the preset iterative capacity expansion rule in the present application includes two types of iterative and proportional iterations based on the multiple of the number of the current service instances.
1) Multiple iteration: the method can be based on the number of the current service instances existing under the current service preferentially, firstly expands the number of the service instances by one time, and then compares the difference between the real-time flow parameters and the expected expansion value; if the capacity is still more than one time higher than the expected capacity expansion value, the capacity expansion is continued to be carried out according to one time; and if the capacity is less than one time of the expected capacity expansion value, carrying out capacity expansion according to the number of the current service examples which is 0.5 time. And repeating the expansion recursion until the real-time flow parameter is lower than the expansion expected value, and stopping expanding the current service.
2) Proportional iteration: if the number of the current service instances under the current service is greater than 10, carrying out iterative capacity expansion according to the number of 10% of the total number of the current service instances each time until the real-time flow parameter does not exceed the capacity expansion expected value; if the number of the current service instances under the current service is less than 10, iterating according to the maximum capacity expansion 1 service instance at each time until the real-time flow parameter does not exceed the capacity expansion expected value.
The capacity expansion expected value is an expected flow parameter supporting efficient operation of the current service, for example, if the service flow extremum in the spam capacity expansion mode is the service flow usage rate and the extremum is 50%, that is, if the service flow usage rate of the current service is higher than 50%, the current service needs to be expanded. At this time, the service flow rate usage rate of the capacity expansion expected value is 30%, that is, the service flow rate usage rate of the current service is reduced to 30% or less by the capacity expansion.
The technical scheme provided by the embodiment of the application can be preconfigured into various capacity expansion modes applicable to different service scenes, so that the comprehensive applicability of service capacity expansion is ensured. For the current service, firstly, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from a preconfigured multiple capacity expansion mode, and then carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode, so that automatic capacity expansion of the service under different scenes is realized, and the accuracy of service capacity expansion is improved by adopting a joint capacity expansion mode of the spam capacity expansion mode and the target capacity expansion mode.
Fig. 7 is a schematic block diagram of a service expansion device according to an embodiment of the present application. As shown in fig. 7, the apparatus 700 may include:
the capacity expansion mode determining module 710 is configured to determine a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes;
and a service expansion module 720, configured to perform joint expansion on the current service by using the spam expansion mode and the target expansion mode.
Further, the service expansion module 720 may include:
the target capacity expansion unit is used for performing basic capacity expansion on the current service by utilizing the service flow state predetermined by the target capacity expansion mode;
And the spam capacity expansion unit is used for carrying out auxiliary capacity expansion on the current service by utilizing the configured service flow extremum in the spam capacity expansion mode and the real-time flow parameters of the current service.
Further, if the target capacity expansion mode is a pressure measurement capacity expansion mode, the target capacity expansion unit may be specifically configured to:
gradually dispatching service flow to a machine room to be tested under the current service;
performing basic capacity expansion on the current service based on the real-time flow parameters after the dispatching of the machine room to be tested and the service flow extremum in the spam capacity expansion mode;
the service flow scheduled in the machine room to be tested is the service flow in other machine rooms under the current service or the simulation flow under the current service.
Further, if the target capacity expansion mode is a predicted capacity expansion mode, the target capacity expansion unit may be specifically configured to:
predicting a corresponding future flow trend based on the historical service flow of the current service;
and based on the future flow trend and the service flow extremum in the spam capacity expansion mode, carrying out basic capacity expansion on the current service in advance.
Further, if the target capacity expansion mode is a planned capacity expansion mode, the target capacity expansion unit may be specifically configured to:
Performing basic capacity expansion on the current service based on a task execution plan pre-arranged under the current service so as to operate the task execution plan on the expanded service instance;
and if the task execution plan is executed, destroying the service instance after the capacity expansion.
Further, the bottom expansion unit may be specifically configured to:
if the real-time flow parameter of the current service is larger than the service flow extremum in the spam capacity expansion mode, carrying out iterative capacity expansion on the current service based on the number of current service instances under the current service and a preset iterative capacity expansion rule;
wherein the service traffic extremum within the spam dilatation mode includes at least one of service traffic usage, service response time, and service query rate per second.
Further, the service capacity expansion device 700 may further include:
and the resource pool adapting module is used for determining an adapting resource pool of the current service according to the service priority of the current service so as to jointly expand the current service in the adapting resource pool.
In the embodiment of the application, a plurality of capacity expansion modes applicable to different service scenes can be preconfigured, so that the overall applicability of service capacity expansion is ensured. For the current service, firstly, determining a spam capacity expansion mode and a target capacity expansion mode calibrated under the current service from a preconfigured multiple capacity expansion mode, and then carrying out joint capacity expansion on the current service by utilizing the spam capacity expansion mode and the target capacity expansion mode, so that automatic capacity expansion of the service under different scenes is realized, and the accuracy of service capacity expansion is improved by adopting a joint capacity expansion mode of the spam capacity expansion mode and the target capacity expansion mode.
It should be understood that apparatus embodiments and method embodiments may correspond with each other and that similar descriptions may refer to the method embodiments. To avoid repetition, no further description is provided here. Specifically, the apparatus 700 shown in fig. 7 may perform the method embodiments provided herein, and the foregoing and other operations and/or functions of each module in the apparatus 700 are respectively for implementing corresponding flows in each method of the embodiment herein, and are not described herein for brevity.
The apparatus 700 of the embodiments of the present application is described above in terms of functional modules in conjunction with the accompanying drawings. It should be understood that the functional module may be implemented in hardware, or may be implemented by instructions in software, or may be implemented by a combination of hardware and software modules. Specifically, each step of the method embodiments in the embodiments of the present application may be implemented by an integrated logic circuit of hardware in a processor and/or an instruction in software form, and the steps of the method disclosed in connection with the embodiments of the present application may be directly implemented as a hardware decoding processor or implemented by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in a well-established storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, and the like. The storage medium is located in a memory, and the processor reads information in the memory, and in combination with hardware, performs the steps in the above method embodiments.
Fig. 8 is a schematic block diagram of an electronic device 800 provided by an embodiment of the present application.
As shown in fig. 8, the electronic device 800 may include:
a memory 810 and a processor 820, the memory 810 being for storing a computer program and transmitting the program code to the processor 820. In other words, the processor 820 may call and run a computer program from the memory 810 to implement the methods in embodiments of the present application.
For example, the processor 820 may be configured to perform the above-described method embodiments according to instructions in the computer program.
In some embodiments of the present application, the processor 820 may include, but is not limited to:
a general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
In some embodiments of the present application, the memory 810 includes, but is not limited to:
volatile memory and/or nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (Double Data Rate SDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), and Direct memory bus RAM (DR RAM).
In some embodiments of the present application, the computer program may be partitioned into one or more modules that are stored in the memory 810 and executed by the processor 820 to perform the methods provided herein. The one or more modules may be a series of computer program instruction segments capable of performing the specified functions, which are used to describe the execution of the computer program in the electronic device.
As shown in fig. 8, the electronic device may further include:
a transceiver 830, the transceiver 830 being connectable to the processor 820 or the memory 810.
It will be appreciated that the various components in the electronic device are connected by a bus system that includes, in addition to a data bus, a power bus, a control bus, and a status signal bus.
The present application also provides a computer storage medium having stored thereon a computer program which, when executed by a computer, enables the computer to perform the method of the above-described method embodiments. Alternatively, embodiments of the present application also provide a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of the method embodiments described above.
When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces, in whole or in part, a flow or function consistent with embodiments of the present application. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, functional modules in the embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes or substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. A method of service expansion, comprising:
determining a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes;
Determining an adaptive resource pool of the current service according to the service priority of the current service;
utilizing the spam capacity expansion mode and the target capacity expansion mode to carry out joint capacity expansion on the current service in the adaptive resource pool;
the device comprises a spam capacity expansion mode, a target capacity expansion mode and a target capacity expansion mode, wherein the spam capacity expansion mode comprises an automatic capacity expansion mode, a pressure measurement capacity expansion mode, a prediction capacity expansion mode and a planned capacity expansion mode;
the joint capacity expansion comprises a basic capacity expansion which is executed in advance in the target capacity expansion mode and an auxiliary capacity expansion which is executed in later in the spam capacity expansion mode.
2. The method of claim 1, wherein the utilizing the spam expansion mode and the target expansion mode to jointly expand the current service within the adapted resource pool comprises:
performing basic capacity expansion on the current service by utilizing a service flow state predetermined by the target capacity expansion mode;
and carrying out auxiliary capacity expansion on the current service by utilizing the configured service flow extremum in the spam capacity expansion mode and the real-time flow parameter of the current service.
3. The method of claim 2, wherein if the target capacity expansion mode is a pressure measurement capacity expansion mode, the performing basic capacity expansion on the current service using the service traffic state predetermined by the target capacity expansion mode comprises:
Gradually dispatching service flow to a machine room to be tested under the current service;
performing basic capacity expansion on the current service based on the real-time flow parameters after the dispatching of the machine room to be tested and the service flow extremum in the spam capacity expansion mode;
the service flow scheduled in the machine room to be tested is the service flow in other machine rooms under the current service or the simulation flow under the current service.
4. The method of claim 2, wherein if the target capacity expansion mode is a predicted capacity expansion mode, the performing basic capacity expansion on the current service using the service traffic state predetermined by the target capacity expansion mode comprises:
predicting a corresponding future flow trend based on the historical service flow of the current service;
and based on the future flow trend and the service flow extremum in the spam capacity expansion mode, carrying out basic capacity expansion on the current service in advance.
5. The method of claim 2, wherein if the target capacity expansion mode is a planned capacity expansion mode, the performing basic capacity expansion on the current service using the service traffic state predetermined by the target capacity expansion mode comprises:
Performing basic capacity expansion on the current service based on a task execution plan pre-arranged under the current service so as to operate the task execution plan on the expanded service instance;
and if the task execution plan is executed, destroying the service instance after the capacity expansion.
6. The method of claim 2, wherein said utilizing the configured service flow extremum in the spam expansion mode and the real-time flow parameters of the current service to facilitate expansion of the current service comprises:
if the real-time flow parameter of the current service is larger than the service flow extremum in the spam capacity expansion mode, carrying out iterative capacity expansion on the current service based on the number of current service instances under the current service and a preset iterative capacity expansion rule;
wherein the service traffic extremum within the spam dilatation mode includes at least one of service traffic usage, service response time, and service query rate per second.
7. A device for expanding a service, comprising:
the capacity expansion mode determining module is used for determining a spam capacity expansion mode and a calibrated target capacity expansion mode under the current service from the preconfigured multiple capacity expansion modes;
The resource pool adapting module is used for determining an adapting resource pool of the current service according to the service priority of the current service;
the service capacity expansion module is used for carrying out joint capacity expansion on the current service in the adaptive resource pool by utilizing the spam capacity expansion mode and the target capacity expansion mode;
the device comprises a spam capacity expansion mode, a target capacity expansion mode and a target capacity expansion mode, wherein the spam capacity expansion mode comprises an automatic capacity expansion mode, a pressure measurement capacity expansion mode, a prediction capacity expansion mode and a planned capacity expansion mode;
the joint capacity expansion comprises a basic capacity expansion which is executed in advance in the target capacity expansion mode and an auxiliary capacity expansion which is executed in later in the spam capacity expansion mode.
8. An electronic device, comprising:
a processor and a memory for storing a computer program, the processor for invoking and running the computer program stored in the memory to perform the method of service expansion of any of claims 1-6.
9. A computer-readable storage medium storing a computer program for causing a computer to perform the method of service expansion of any one of claims 1-6.
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