WO2020052151A1

WO2020052151A1 - Service node management method, apparatus and device, and computer readable storage medium

Info

Publication number: WO2020052151A1
Application number: PCT/CN2018/122637
Authority: WO
Inventors: 徐欣
Original assignee: 深圳壹账通智能科技有限公司
Priority date: 2018-09-13
Filing date: 2018-12-21
Publication date: 2020-03-19
Also published as: CN109413147A; CN109413147B

Abstract

A service node management method, apparatus and device, and a computer readable storage medium. The method comprises: reading a plurality of rated parameters of a service node and a parameter coefficient corresponding to each rated parameter, and determining a plurality of performance parameters of the service node and the limit capacity of the service node according to each rated parameter and each parameter coefficient (S10); reading a plurality of real-time parameters of the service node, and determining the real-time capacity of the service node according to each real-time parameter and the performance parameter (S20); and determining the redundancy degree of the service node according to the limit capacity and the real-time capacity, and performing increase and decrease management on the service node according to a relationship between the redundancy degree and a preset threshold interval (S30). According to the method, the increase and decrease management of the service node can be automatically and accurately performed on the basis of the relationship between the redundancy degree that reflects the access traffic currently provided by the service node and the preset threshold interval, so that the process of performing increase and decrease management by manual repeated operations can be avoided, the operation and maintenance costs can be reduced, and the high availability of each service node in a hybrid cloud architecture can be improved.

Description

Management method, device, device and computer-readable storage medium of service node

This application claims the priority of a Chinese patent application filed on September 13, 2018, with the Chinese Patent Office, application number 201811071652.9, and the invention name "Management method, device, device, and computer-readable storage medium for service nodes". The contents are incorporated in the application by reference.

Technical field

The present application mainly relates to the technical field of network communications, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for managing a service node.

Background technique

With the development of cloud technology, many Internet companies have increased the cloud platform network environment while using the traditional IDC (Internet Data Center) network environment; and the server nodes of the traditional IDC network environment run the company's vital services If there is a huge migration risk when moving to a cloud platform network environment, the traditional IDC network environment and cloud platform network environment need to be used in a hybrid cloud architecture.

However, the current hybrid cloud architecture where the traditional IDC network environment and the cloud platform network environment jointly provide services to the outside cannot accurately obtain the load and bandwidth occupation status information of each service node; making it impossible to achieve automatic elastic scaling of service nodes based on real-time traffic That is, it is not possible to automatically add or delete service nodes; as a result of a sudden increase in access requests, it is impossible to accurately and automatically deploy service nodes to respond to access requests, requiring manual repeated operations for management, increasing operation and maintenance costs, and without Conducive to high availability of hybrid cloud architecture.

Summary of the Invention

The main purpose of this application is to provide a service node management method, device, device, and computer-readable storage medium. The purpose is to solve the problem that the existing hybrid cloud architecture cannot accurately and automatically deploy service nodes in response to changes in the number of access requests. High and low availability issues.

In order to achieve the above object, the present application provides a method for managing a service node. The method for managing a service node includes the following steps:

Reading a plurality of rated parameters of a service node and parameter coefficients corresponding to each of the rated parameters, and determining a plurality of performance parameters of the service node according to each of the rated parameters and each of the parameter coefficients, and the Service node capacity;

Read a plurality of real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

Determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval.

In addition, in order to achieve the foregoing object, the present application also proposes a management device for a service node, where the management device for the service node includes:

The reading module is configured to read multiple rated parameters of the service node and parameter coefficients corresponding to the rated parameters, and determine multiple values of the service node according to the rated parameters and the parameter coefficients. Performance parameters, and the ultimate capacity of the service node;

A determining module, configured to read multiple real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

A management module, configured to determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval .

In addition, in order to achieve the foregoing object, the present application also proposes a management device of a service node. The management device of the service node includes: a memory, a processor, a communication bus, and a management program of the service node stored on the memory;

The communication bus is used to implement connection and communication between the processor and the memory;

The processor is configured to execute a management program of the service node to implement the following steps:

In addition, in order to achieve the above object, the present application also provides a computer-readable storage medium, where the computer-readable storage medium stores one or more programs, and the one or more programs can be processed by one or more processors. Performed for:

The method for managing a service node of this embodiment determines multiple performance parameters and limit capacities of a service node according to the read multiple rated parameters of the service node and parameter coefficients corresponding to the rated parameters; The multiple real-time parameters of the server and the determined multiple performance parameters further determine the real-time capacity of the server, and then determine the redundancy of the service node according to the limit capacity and real-time capacity; because the real-time capacity represents the real-time use of resources in the service node The degree of redundancy can reflect the amount of access services that the service node can provide in real time through the redundancy; and the preset threshold interval characterizes the reasonable range of the amount of access services provided by the service node. The relationship between the redundancy and the preset threshold interval is used to judge Whether the amount of service provided by the service node in real time is reasonable, and then increase or decrease the management of the service node based on the rationality; avoid manual repeated operations to increase or decrease the management, and realize the automatic increase and decrease management deployment of the service node, reducing the operation and maintenance cost and improving The high availability of each service node in the hybrid cloud architecture is described.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic flowchart of a first embodiment of a management method of a service node according to the present application; FIG.

2 is a schematic diagram of functional modules of a first embodiment of a management device for a service node of the present application;

FIG. 3 is a schematic diagram of a device structure of a hardware operating environment involved in a method according to an embodiment of the present application.

The implementation, functional features and advantages of the purpose of this application will be further described with reference to the embodiments and the drawings.

detailed description

It should be understood that the specific embodiments described herein are only used to explain the application, and are not used to limit the application.

This application provides a method for managing a service node.

Please refer to FIG. 1. FIG. 1 is a schematic flowchart of a first embodiment of a management method for a service node of this application. In this embodiment, the method for managing a service node includes:

Step S10, reading a plurality of rated parameters of the service node and parameter coefficients corresponding to each of the rated parameters, and determining a plurality of performance parameters of the service node according to each of the rated parameters and each of the parameter coefficients, And the limit capacity of the service node;

The service node management method of the present application is applied to a control center of a network environment platform, and is applicable to realize the management of each service node in the network environment platform through the control center. The network environment of this embodiment is a hybrid cloud architecture that provides services to both the traditional IDC network environment and the cloud platform network environment, such as the hybrid cloud architecture of anti-fraud platforms in financial institutions; the service nodes include the traditional IDC network environment and the cloud platform. In the network environment, for all nodes that provide services to the outside, external access initiates an access request to the service node, accesses the network environment through the service node, and obtains the resources of the network environment. The network environment includes a large number of service nodes. While providing different types of services, the load of each service node is shared; there are differences in hardware resources between different service nodes, making each service node's ability to provide services different, such as 8 The difference between a core 8G service node and a 4-core 8G service node. That is, different hardware resources indicate that the service node has different performance. The parameters of the hardware resources of the service node are used as the rated parameters of the service node, including the number of cores, frequency, memory type, storage size value, disk type value, IO bandwidth value, The network bandwidth value allocated by this service node. In addition, considering the difference in the degree of influence of different rated parameters on the performance of the service node, different parameter coefficients are set for each rated parameter in advance, and the effect of each rated parameter on the performance of the service node is reflected by the different parameter coefficients; The coefficients are determined through experiments. A number of different parameter coefficients are set for experiments, and the best parameter coefficients are determined by fitting.

In the process of managing a service node, multiple rated parameters of the service node and parameter coefficients corresponding to each of the rated parameters are read to determine the performance of the service node. Because different rated parameters characterize the performance of the service node in different aspects, multiple performance parameters of the service node can be determined according to different rated parameters and corresponding parameter coefficients. For example, the performance parameters determined by the number of cores and frequency of the CPU (Central Processing Unit) in the service node determined by the number of cores, frequency, and their corresponding parameter coefficients; and the value of the memory type, memory size, and Corresponding parameter coefficients, performance parameters determined by memory type and memory size in the determined service node; performance determined by disk type and IO bandwidth in the service node determined by disk type value, IO bandwidth value, and corresponding parameter coefficients Parameter; the value of the network bandwidth allocated by the serving node and its corresponding parameter coefficient, and the performance value parameter of the determined network bandwidth. After determining the performance parameters, the performance of the service node as a whole can be reflected by the performance parameters; the capacity of the overall service can be determined by the limit capacity, that is, the limit capacity of the service node is determined according to the performance parameters to characterize The maximum access that a service node can provide when all hardware resources are fully used.

Step S20: Read multiple real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

Understandably, because different users access the service node at different times, the service nodes receive different amounts of access at different times. In this embodiment, real-time capacity is used to characterize the access capability provided by the service node in real time. Read multiple real-time parameters of the service node. The real-time parameters can include real-time CPU usage, real-time occupancy of memory, real-time IO occupancy of disk, and actual occupancy of network bandwidth. The influence of various performance parameters determines the real-time capacity of the service node.

Step S30: Determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval.

Further, after determining the maximum amount of access that the serving node can provide and the actual amount of access provided, the redundancy of the serving node can be determined according to both, that is, the remaining accessible access amount of the serving node; among which the redundancy is the largest The limit value is close to 1, and the minimum limit value is close to 0; the maximum limit value represents the maximum access amount that the service node can access, and the minimum limit value represents the minimum access amount that the service node can access. Because the service node is used to provide services to the outside, the bandwidth and response speed of the service node need to be considered; under a certain bandwidth, the response speed is related to the traffic; if the traffic is too large, the response speed may be lowered. Therefore, it is necessary to control the amount of access within a certain range. Specifically, a preset threshold interval is set in advance, and the amount of access of the service node is determined according to the relationship between the redundancy and the preset threshold interval; Management to ensure that the number of visits to service nodes is within a certain range.

The preset threshold interval is determined through multiple experiments, including the numerical interval of the upper boundary threshold and the lower boundary threshold; the upper boundary threshold represents greater redundancy, the number of service node visits is small, and the resource consumption is small; and the lower boundary The threshold indicates that the redundancy is small, the number of accesses to the service node is large, and the resource consumption is large. For the lower boundary threshold of a large number of visits, considering that the limit capacity of the service node is the theoretical maximum access that can be accessed, in actual use, due to the influence of external environmental factors, this theoretical value may not be reached. Therefore, the set lower boundary threshold value is smaller than the limit capacity value. In order to accurately reflect that the service node's resources are about to be exhausted, the lower boundary threshold is determined through stress test experiments. Specifically, the service node has an interface exposed to the outside, and the access test of the service node is implemented by means of the interface call; the frequency of the call to the interface can reflect the number of access requests of the service node; the interface is called using different call frequencies to Perform stress test on the service node to determine the time it takes for the stress test to reach the inflection point, where the inflection point is the sharp consumption point of the service node resources; the time consumption can also reflect the sufficient condition of the service node resources, and the shorter the time consumption, the richer the service node resources; Therefore, the inflection point of the balance between call frequency and time consumption is tested. At this inflection point, the call frequency is high and the time consumption is high. It means that the service node is accessed a lot and the resources are about to be exhausted. The value of this inflection point is used as Lower boundary threshold. According to the relationship between the redundancy and the preset threshold interval, the steps of increasing or decreasing the service node include:

Step S31: comparing the redundancy with a preset threshold interval to determine whether the redundancy is within a preset threshold interval;

Furthermore, after determining the redundancy of the serving node, the redundancy is compared with a preset threshold interval to determine whether the redundancy is within the preset threshold interval, that is, whether the access amount of the serving node is within an appropriate range. Compare the redundancy with the upper boundary threshold and the lower boundary threshold of the preset threshold interval to determine whether the redundancy is larger than the lower boundary threshold and smaller than the upper boundary threshold.

Step S32: when the redundancy is less than a lower boundary value of a preset threshold interval, adding a new service node corresponding to the service node;

When it is determined that the redundancy of the serving node is less than the lower boundary value of the preset threshold interval, it indicates that the remaining number of accessible access requests of the serving node is small, and the current amount of access is large, which may affect its response speed. Thereby, a new service node corresponding to the service node is automatically added, and the corresponding relationship between the two is that the new service node is consistent with the type of service provided by the original service node. Allocate resources such as IP addresses, CPU cores, memory size values, and bandwidth values to the newly added service nodes to use the newly added service nodes to provide services and share the access pressure of the original service nodes.

Step S33: When the redundancy is greater than the upper boundary value of a preset threshold interval, perform a delete operation on the serving node.

When it is determined that the redundancy of the serving node is greater than the upper boundary value of the preset threshold interval, it indicates that the remaining access requests of the serving node are large, the current access volume is small, and the resources are not fully utilized. Therefore, the service node is automatically deleted to release and recycle system resources. At the same time, the access request of this service node is connected to other service nodes that provide similar services provided by this service node, so that while providing services to the outside world, Make full use of system resources.

Further, in another embodiment of the method for managing server nodes of the present application, the plurality of performance parameters of the service node and the limit of the service node are determined according to each of the rated parameters and each of the parameter coefficients. The steps for capacity include:

Step S11: group each of the rated parameters and each of the parameter coefficients based on parameter attributes, and generate multiple performance parameters of the service node according to the rated parameters and the parameter coefficients in each group;

Furthermore, different rated parameters have different parameter attributes, which characterize the performance of the service node in different aspects; thus, the rated parameters can be grouped according to the parameter attributes, and the rated parameters with the same parameter attributes can be divided into the same group; The rated parameters characterize the performance of the service node in the same respect. Because the performance is also related to the parameter coefficients, while grouping the rated parameters, the parameter coefficients corresponding to the rated parameters are also divided into groups corresponding to the rated parameters to reflect the rated parameters and parameter coefficients in each group. Multiple performance parameters for service node performance. Specifically, since the performance parameters of the service node on the CPU are determined by the number of cores and frequency, the number of cores and frequency are set to have the same parameter attributes, and the two and their corresponding parameter coefficients are divided into the same group. Because the performance parameters of the service node in memory are determined by the memory type value and the memory size, the memory type value and the memory size are set to have the same parameter attributes, and the two and their corresponding parameter coefficients are divided into the same group. The performance parameters of the service node in terms of disk type are determined by the disk type value and the IO bandwidth value. The disk type value and the IO bandwidth value are set to have the same parameter attributes, and the two and their corresponding parameter coefficients are divided into the same group. . The performance parameters of the service node in terms of network bandwidth are determined by the network bandwidth value allocated by the service node, and the network bandwidth value allocated by the service node and its corresponding parameter coefficient are divided into a group. After grouping each of the rated parameters and their parameter coefficients, according to the rated parameters and their parameter coefficients in each group, performance parameters reflecting the performance of the service node in each group can be generated.

Specifically, in order to determine multiple performance parameters of the service node according to the rated parameters in each group and their corresponding parameter coefficients, preset formulas are set through multiple experiments in advance; the rated parameters in each group and their corresponding The coefficients of each parameter are input into the preset formula and are calculated instead of the independent variables therein. The result obtained by the calculation is the performance parameter. Because the rated parameters in each group are different, which characterizes the performance of the server node in different aspects, the rated parameters in each group and their corresponding parameter coefficients are entered into a preset formula during calculation to obtain different performance parameters. Specifically, the preset formula is:

y = m ₁ * x ₁ + m ₂ * x ₂

Among them, y is a performance parameter, x1, x2 are each rated parameter, and m1, m2 are parameter coefficients corresponding to the rated parameter.

The performance parameters are different due to the different values of x1, x2 and m1, m2. Specifically, when determining the performance parameter y determined by the number of CPU cores and frequency in the service node, that is, the performance parameter of the service node in terms of CPU, x1 and x2 are the number of cores and frequency (GHZ), and m1 and m2 are respectively It is a parameter coefficient corresponding to the number of cores and frequency. When determining the performance parameter y determined by the memory type and memory size in the service node, that is, the performance parameter of the service node in terms of memory, x1 and x2 are the memory type value and memory size value (GB), and m1 and m2 are respectively Parameter coefficient corresponding to the memory type value and memory size value; where the memory type value is a parameter value set according to different memory types, such as ddr2 (Double-Data-RateTwoSynchronousDynamicRandomAccessMemory, the second generation double data rate synchronous dynamic random storage The value of the memory type is lower than that of the ddr3 type. When determining the performance parameter y determined by the disk type and IO bandwidth in the service node, that is, the performance parameter of the service node in terms of disk type, x1 and x2 are the disk type value and the IO bandwidth value (MB), and m1 and m2 are Parameter coefficients corresponding to the disk type value and the IO bandwidth value respectively; wherein the disk type value is a parameter value set according to different disk types, such as ssd (Solid State Drives, solid state hard disk) disks whose disk type values are more than mechanical disks The disk type value is high. When determining the performance value parameter y determined by the network bandwidth in the serving node, the network bandwidth is determined only by the network bandwidth value allocated by the serving node, so x1 is used as the network bandwidth value allocated by the serving node, and k1 is the same as The parameter coefficient corresponding to the network bandwidth value assigned by the serving node; and the value of x2 is 0, which is related to only x1 and k1. The rated parameters in each group and their corresponding parameter coefficients are respectively input into preset formulas according to the above combination to obtain multiple performance parameters that characterize the performance of each aspect of the service node.

Step S12: Read a preset weight value corresponding to each of the performance parameters, and input each of the preset weight value and each of the performance parameters into a first preset formula to calculate a limit capacity of the service node. ;

The first preset formula: ValX = k ₁ lgA ₁ + k ₂ lgA ₂ + k ₃ lgA ₃ + k ₄ lgA ₄ , ValX is the limit capacity, A1, A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each the preset weight value, and lg is a logarithmic value.

Further, after determining a plurality of performance parameters that characterize the performance of each aspect of the service node, although the performance capabilities of the service node as a whole can be reflected by the performance parameters, the impact of the performance of each aspect on the overall performance of the service node varies. ; Some performance parameters have a greater impact on the ability of the service node to provide services as a whole, while other performance parameters have a smaller impact on the ability of the service nodes to provide services as a whole. In order to characterize the impact of various performance parameters on the overall performance of the service node, different preset weight values are set in advance for performance parameters reflecting various aspects of performance. The preset weight values are determined through multiple experiments or historical data statistics to characterize each performance parameter. Impact on overall performance. After determining each performance parameter, combined with a preset weight value corresponding to each performance parameter, a limit capacity that reflects the overall performance of the service node is determined. Specifically, a first preset formula is set through multiple experiments in advance, and each performance parameter and its corresponding preset weight value are input into the first preset formula, and the independent variables therein are calculated and calculated. The result is the limit capacity of the service node, where the first preset formula is: ValX = k ₁ lgA ₁ + k ₂ lgA ₂ + k ₃ lgA ₃ + k ₄ lgA ₄

Among them, ValX is the limit capacity, A1 is the performance parameter determined by the number of CPU cores and frequency, A2 is the performance parameter determined by the memory type and memory size, A3 is the performance parameter determined by the disk type and IO bandwidth, and A4 is the network bandwidth The determined performance value parameters, k1, k2, k3, and k4 are preset weight values corresponding to A1, A2, A3, and A4, respectively, and lg is a logarithmic value.

The performance parameters determined by the number and frequency of CPU cores and their corresponding preset weight values calculated by the preset formula will be replaced by A1 and k1 in this first preset formula, respectively, and the memory type calculated by the preset formula will be The performance parameters determined by the memory size and their corresponding preset weight values replace A2 and k2 in this first preset formula, respectively. The performance parameters determined by the disk type and IO bandwidth and their corresponding calculations will be calculated by the preset formula. The preset weight values of A1 and K3 are replaced in this first preset formula, respectively. The performance value parameters determined by the network bandwidth calculated by the preset formula and their corresponding preset weight values are used to replace the first preset values. Let A4 and k4 in the formula be used to calculate the first preset formula after the replacement operation. The obtained calculation result is the limit capacity, which represents the maximum access amount that the service node can withstand.

Further, in another embodiment of the method for managing server nodes of this application, the step of determining the real-time capacity of the service node according to each of the real-time parameters and the performance parameters includes:

Inputting each of the real-time parameters, each of the performance parameters, and each of the preset weight values into a second preset formula to calculate a real-time capacity of the service node;

The second preset formula: ValY = k ₁ lgA ₁ * Q ₁ + k ₂ lgA ₂ * Q ₂ + k ₃ lgA ₃ * Q ₃ + k ₄ lgA ₄ * Q ₄ , ValY is real-time capacity, A1 A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each of the preset weight values, Q1, Q2, Q3, and Q4 are real-time parameters, and lg is a logarithmic value.

In this embodiment, in order to count the real-time capacity of the access requests accessed by the service nodes in real time, a second preset formula is set in advance; each real-time parameter, each performance parameter, and each preset weight corresponding to each performance parameter are read. The value is input into the second preset formula, which is calculated instead of the independent variables. The result of the calculation is the real-time capacity of the service node. Specifically, the second preset formula is: ValY = k ₁ lgA ₁ * Q ₁ + k ₂ lgA ₂ * Q ₂ + k ₃ lgA ₃ * Q ₃ + k ₄ lgA ₄ * Q ₄

Among them, ValY is the real-time capacity, Q1 is the real-time CPU usage, Q2 is the real-time occupancy of memory, Q3 is the real-time IO occupancy of the disk, Q4 is the actual occupancy of network bandwidth, and A1 is the performance determined by the number of CPU cores and frequency. Parameter, A2 is a performance parameter determined by the memory type and memory size, A3 is a performance parameter determined by the disk type and IO bandwidth, A4 is a performance value parameter determined by the network bandwidth, k1, k2, k3, and k4 are the same as A1, respectively. The preset weights corresponding to A2, A3, and A4, where lg is the logarithmic value.

The read real-time CPU usage rate and the performance parameters determined by the number and frequency of CPU cores and their corresponding preset weight values calculated from the preset formula are used to replace Q1, A1 and k1; will read the real-time occupancy of the memory, and the performance parameters determined by the memory type and memory size and their corresponding preset weight values calculated from the preset formula, respectively replacing Q2 in this second preset formula , A2, and k2; the real-time IO occupancy of the disk to be read, and the performance parameters determined by the disk type and IO bandwidth and their corresponding preset weight values calculated by the preset formula, respectively, replace this second preset Q3, A3, and k3 in the formula; the actual occupancy of the network bandwidth read, and the performance value parameter determined by the network bandwidth calculated from the preset formula and its corresponding preset weight value, respectively replace this second preset Set Q4, A4, and k4 in the formula; calculate the second preset formula after the replacement operation, and the calculation result obtained is the real-time capacity, which represents the access amount that the service node currently accesses in real time.

Further, in another embodiment of the method for managing server nodes of the present application, the step of determining the redundancy of the service node according to the limit capacity and the real-time capacity includes:

Inputting the limit capacity and the real-time capacity into a third preset formula to calculate the redundancy of the service node;

The third preset formula is: R = 1-ValY / ValX, R is redundancy, ValY is real-time capacity, and ValX is limit capacity.

Furthermore, after determining the limit capacity and real-time capacity of the service node, in order to determine the redundancy of the service node according to the limit capacity and real-time capacity, a third preset formula is set in advance through experiments; the determined limit capacity and real-time capacity are Transfer it to the third preset formula and calculate it instead of the independent variables. The obtained result is the redundancy of the service node. Specifically, the third preset formula is: R = 1-ValY / ValX

Among them, R is redundancy, ValY is real-time capacity, and ValX is limit capacity.

The limit capacity calculated by the first preset formula and the real-time capacity calculated by the second preset formula are used to replace ValX and ValY in the third preset formula, respectively; After calculation, the obtained calculation result is the redundancy of the service node; it represents the currently accessible access amount of the service node, and the service node is managed and added or deleted through this redundancy.

Further, in another embodiment of the method for managing server nodes of the present application, after the step of adding a new service node corresponding to the service node includes:

Step S34: Read the newly added rated parameters corresponding to the new service node, compare the newly added rated parameters with the rated parameters of other service nodes, and determine that among the rated parameters of the other service nodes and the newly added Target rated parameters matching the rated parameters;

Understandably, because each service node has different resources and different service capabilities, for example, the service capability of an 8-core 8G service node is better than that of a 4-core 8G service node. In order to better respond to the access request, the access request should be preferentially accessed to a service node with a good service capability. In order to characterize the pros and cons of the service capabilities of each service node, this embodiment provides a mechanism for allocating access weights to each service node according to the resources possessed by each service node, and the higher the resource allocation, the greater the access weight allocated. . Therefore, after adding a new service node, it is necessary to assign an access weight to this new service node. Specifically, after a new service node is added, the rated parameters corresponding to the new service node are read, and the rated parameters that it has are used as the new rated parameters to distinguish it from the rated parameters of other service nodes; The new rated parameters are compared with the rated parameters of other service nodes to determine the target rated parameters that match the new rated parameters among the rated parameters of other service nodes. Among them, during the matching process, priority is given to the rated parameter that is completely consistent with the newly added rated parameter, and this completely consistent rated parameter is used as the target rated parameter; if there are no rated parameters of other service nodes that are completely consistent with the newly added rated parameter, The rated parameter determines the rated parameter with the least difference from this newly added rated parameter, and uses the rated parameter with the least difference as the target rated parameter.

Step S35: Determine a service node to which the target rated parameter belongs, and allocate an access weight to the new service node according to the access weight owned by the homed service node.

Further, because the access weight of the service node is related to the resource configuration of the service node, and the resource configuration is characterized by the rated parameters; after the target rated parameter is determined, the target rated parameter is completely consistent with the newly added rated parameter, or the difference is small, so that The resource configuration characterized by the target rated parameter is the same as or similar to the resource configuration characterized by the new rated parameter; thus, the access weight of the new service node can be determined by the access weight of the service node corresponding to the target rated parameter. Because the target rated parameter and the newly-added rated parameter are compared with the rated parameters of other service nodes, the service node from which the target rated parameter is derived, that is, the service node to which the target rated parameter belongs; can be determined according to the comparison process; The access weight that the service node has, based on this access weight, assigns an access weight to the new service node. When the new rated parameter is completely consistent with the target rated parameter, the access weight of the new service node can be set to be the same as the access weight of the owned service node; and when the new rated parameter represents a service capability higher than the target rating Parameter, the access weight of the new service node is set to be higher than the access weight of the owned service node; when the service capability represented by the newly added rated parameter is lower than the target rated parameter, the access weight of the new service node is set to Lower access weight than the home service node. Assigning access weights to the new service node according to the access weights of the service nodes with similar rating parameters can make the access weight of the new service node more accurate; it is convenient for subsequent access requests to each service node according to the access weight to provide more Excellent service.

Further, in another embodiment of the method for managing server nodes of the present application, after the step of assigning an access weight to the new service node includes:

Step S36: When a service request is received, determine a target service node corresponding to the service request according to the type of the service request, and determine whether there are multiple target service nodes;

Furthermore, when a user needs to access the network environment and obtain network resources, the user can send a service request through a terminal connected to the network environment; and for different network resources, the identifier carried in the sent service request is not same. When receiving this service request, read the identifier carried in it, and compare this identifier with each identifier in the preset identification type table to determine the identifier corresponding to the carried identifier in the identification type table. According to the type corresponding to the corresponding identifier in the identification type table, the type of the service request can be determined. Because the service types corresponding to different service nodes are different, after determining the type of service request, the service node that provides this type of service can be further determined in each service node, and this service node is used as the target service node corresponding to the service request. Because for some service requests with a large number of requests, in order to provide better services, multiple service nodes are usually set up; after determining the target service node, it is necessary to further determine whether there are multiple target service nodes in order to increase Among the target service nodes, a service node serving the service request is determined.

Step S37: if there are multiple target service nodes, determine the size relationship of the target access weights of the target service nodes, and call the target access node with the maximum target access weight to respond to the service request .

In this implementation, the same identifier is set for service nodes of the same type. After the target service node is determined, the identifier carried in it is read, and it is determined whether other service nodes also carry this identifier. If no other service node also carries this identifier, it means that there are no multiple target service nodes, and this target service node is determined as the service node providing the service request service, and it will respond to the service request. If other service nodes also carry this identifier, it means that there are multiple target service nodes, then the target access weights of each target service node are read, and the target access weights are compared to determine their respective size relationships. Because the larger the target access weight, the better the service provided by the target service node; the maximum value of the target access weight is determined from the size relationship, and the target access node with this maximum target access weight is determined as the service request service The service node calls it to respond to the service request to optimize the service of the service request.

In addition, referring to FIG. 2, this application provides a management device for a service node. In a first embodiment of a management device for a service node in this application, the management device for the service node includes:

The reading module 10 is configured to read a plurality of rated parameters of a service node and parameter coefficients corresponding to each of the rated parameters, and determine the multiplicity of the service node according to each of the rated parameters and each of the parameter coefficients. Performance parameters, and the maximum capacity of the service node;

A determining module 20, configured to read multiple real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

The management module 30 is configured to determine the redundancy of the service node according to the limit capacity and the real-time capacity, and increase or decrease the service node according to the relationship between the redundancy and a preset threshold interval. management.

In the management device of the service node in this embodiment, the reading module 10 determines multiple performance parameters and limit capacities of the service node according to the read multiple rated parameters of the service node and parameter coefficients corresponding to the rated parameters; The determination module 20 further determines the real-time capacity of the server according to the read real-time parameters of the server and the determined multiple performance parameters, and then the management module 30 determines the redundancy of the service node according to the limit capacity and real-time capacity; The capacity represents the real-time usage of each resource in the service node, so that the redundancy can reflect the amount of access services that the service node can provide in real time; and the preset threshold interval characterizes the reasonable range of the amount of access services provided by the service node, using redundancy The relationship between the degree and the preset threshold interval is used to determine whether the service volume provided by the service node is reasonable in real time, and then to increase or decrease the service node based on the rationality; avoiding manual repeated operations for increase or decrease management, and to automatically increase or decrease the service node Managed deployment, reduced operation and maintenance costs, and improved High availability of service nodes.

Each virtual function module of the management device of the service node is stored in the memory 1005 of the management device of the service node shown in FIG. 3. When the processor 1001 executes the management program of the service node, each module in the embodiment shown in FIG. 2 is implemented. Functions.

It should be noted that a person of ordinary skill in the art can understand that all or part of the steps for implementing the foregoing embodiments may be completed by hardware, or may be performed by a program instructing related hardware. The program may be stored in a computer-readable format. Among the storage media, the above-mentioned storage media may be a read-only memory, a magnetic disk, or an optical disk.

Referring to FIG. 3, FIG. 3 is a schematic diagram of a device structure of a hardware operating environment involved in the method according to the embodiment of the present application.

The management device of the service node in the embodiment of the present application may be a personal computer (PC), or a terminal device such as a smart phone, a tablet computer, an e-book reader, or a portable computer.

As shown in FIG. 3, the management device of the service node may include a processor 1001, such as a CPU (Central Processing Unit, central processing unit), a memory 1005, and a communication bus 1002. The communication bus 1002 is used to implement connection and communication between the processor 1001 and the memory 1005. The memory 1005 may be a high-speed RAM (random access memory), or a non-volatile memory (for example, a magnetic disk memory). The memory 1005 may optionally be a storage device independent of the foregoing processor 1001.

Optionally, the management device of the service node may further include a user interface, a network interface, a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi (Wireless Fidelity) module, and the like. The user interface may include a display, an input unit such as a keyboard, and the optional user interface may further include a standard wired interface and a wireless interface. The network interface may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

Those skilled in the art can understand that the management device structure of the service node shown in FIG. 3 does not constitute a limitation on the management device of the service node, and may include more or fewer components than shown, or a combination of certain components. Or different component arrangements.

As shown in FIG. 3, the memory 1005 as a computer-readable storage medium may include an operating system, a network communication module, and a management program of a service node. The operating system is a program that manages and controls the hardware and software resources of the management device of the service node, and supports the management program of the service node and other software and / or programs. The network communication module is used to implement communication between components in the memory 1005 and to communicate with other hardware and software in the management device of the service node.

In the management device of the service node shown in FIG. 3, the processor 1001 is configured to execute a management program of the service node stored in the memory 1005, and implement steps in the embodiments of the management method of the service node described above.

The present application provides a computer-readable storage medium. The computer-readable storage medium stores one or more programs, and the one or more programs can also be executed by one or more processors for implementing the foregoing. The steps in the embodiments of the method for managing a service node.

It should also be noted that in this article, the terms "including", "including" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements And includes other elements not explicitly listed, or elements inherent to such a process, method, article, or device. Without more restrictions, an element limited by the sentence "including a ..." does not exclude that there are other identical elements in the process, method, article, or device that includes the element.

The above-mentioned serial numbers of the embodiments of the present application are merely for description, and do not represent the superiority or inferiority of the embodiments.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods in the above embodiments can be implemented by means of software plus a necessary universal hardware platform, and of course, also by hardware, but in many cases the former is better. Implementation. Based on such an understanding, the technical solution of this application that is essentially or contributes to the existing technology may be embodied in the form of a software product, which is stored in a computer-readable storage medium (such as The ROM / RAM, magnetic disk, and optical disc) include several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in the embodiments of the present application.

The above is only a preferred embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made by using the description and drawings of the present application under the concept of the present application, or directly / indirectly used in Other related technical fields are included in the patent protection scope of this application.

Claims

A method for managing a service node, wherein the method for managing a service node includes the following steps:

Reading a plurality of rated parameters of a service node and parameter coefficients corresponding to each of the rated parameters, and determining a plurality of performance parameters of the service node according to each of the rated parameters and each of the parameter coefficients, and the Service node capacity;

Read a plurality of real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

Determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval.
The method for managing a service node according to claim 1, wherein, according to each of the rated parameters and each of the parameter coefficients, a plurality of performance parameters of the service node and a limit of the service node are determined. The steps for capacity include:

Grouping each of the rated parameters and each of the parameter coefficients based on parameter attributes, and generating a plurality of performance parameters of the service node according to the rated parameters and the parameter coefficients in each group;

Reading a preset weight value corresponding to each of the performance parameters, and inputting each of the preset weight value and each of the performance parameters into a first preset formula to calculate a limit capacity of the service node;

The first preset formula is: ValX = k 1 lgA 1 + k 2 lgA 2 + k 3 lgA 3 + k 4 lgA 4 , ValX is a limit capacity, and A1, A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each the preset weight value, and lg is a logarithmic value.
The method for managing a service node according to claim 2, wherein the step of determining the real-time capacity of the service node according to each of the real-time parameters and the performance parameter comprises:

Inputting each of the real-time parameters, each of the performance parameters, and each of the preset weight values into a second preset formula to calculate a real-time capacity of the service node;

The second preset formula: ValY = k 1 lgA 1 * Q 1 + k 2 lgA 2 * Q 2 + k 3 lgA 3 * Q 3 + k 4 lgA 4 * Q 4 , ValY is real-time capacity, A1 A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each of the preset weight values, Q1, Q2, Q3, and Q4 are real-time parameters, and lg is a logarithmic value.
The method for managing a service node according to claim 3, wherein the step of determining the redundancy of the service node according to the limit capacity and the real-time capacity comprises:

Inputting the limit capacity and the real-time capacity into a third preset formula to calculate the redundancy of the service node;

The third preset formula is: R = 1-ValY / ValX, R is redundancy, ValY is real-time capacity, and ValX is limit capacity.
The method for managing a service node according to claim 1, wherein the step of increasing or decreasing the service node according to the relationship between the redundancy and a preset threshold interval comprises:

Comparing the redundancy with a preset threshold interval to determine whether the redundancy is within a preset threshold interval;

When the redundancy is less than a lower boundary value of a preset threshold interval, adding a new service node corresponding to the service node;

When the redundancy is greater than the upper boundary value of the preset threshold interval, the service node is deleted.
The method for managing a service node according to claim 5, wherein after the step of adding a new service node corresponding to the service node comprises:

Read the new rated parameters corresponding to the new service node, and compare the new rated parameters with the rated parameters of other service nodes to determine that the rated parameters of the other service nodes match the new rated parameters Target rated parameters

Determining a service node to which the target rated parameter belongs, and assigning an access weight to the new service node according to the access weight of the home service node.
The method for managing a service node according to claim 6, wherein after the step of allocating an access weight to the new service node comprises:

When a service request is received, determining a target service node corresponding to the service request according to the type of the service request, and determining whether there are multiple target service nodes;

If there are multiple target service nodes, determine the size relationship of the target access weights of the target service nodes, and call the target access node with the maximum target access weight to respond to the service request.
A management device for a service node, characterized in that the management device for the service node includes:

The reading module is configured to read multiple rated parameters of the service node and parameter coefficients corresponding to the rated parameters, and determine multiple values of the service node according to the rated parameters and the parameter coefficients. Performance parameters, and the ultimate capacity of the service node;

A determining module, configured to read multiple real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

A management module, configured to determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval .
A management device of a service node, characterized in that the management device of the service node includes: a memory, a processor, a communication bus, and a management program of the service node stored on the memory;

The communication bus is used to implement connection and communication between the processor and the memory;

The processor is configured to execute a management program of the service node to implement the following steps:

Reading a plurality of rated parameters of a service node and parameter coefficients corresponding to each of the rated parameters, and determining a plurality of performance parameters of the service node according to each of the rated parameters and each of the parameter coefficients, and the Service node capacity;

Read a plurality of real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

Determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval.
The management device of a service node according to claim 9, characterized in that, according to each of the rated parameters and each of the parameter coefficients, a plurality of performance parameters of the service node and a limit of the service node are determined The steps for capacity include:

Grouping each of the rated parameters and each of the parameter coefficients based on parameter attributes, and generating a plurality of performance parameters of the service node according to the rated parameters and the parameter coefficients in each group;

Reading a preset weight value corresponding to each of the performance parameters, and inputting each of the preset weight value and each of the performance parameters into a first preset formula to calculate a limit capacity of the service node;

The first preset formula is: ValX = k 1 lgA 1 + k 2 lgA 2 + k 3 lgA 3 + k 4 lgA 4 , ValX is a limit capacity, and A1, A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each the preset weight value, and lg is a logarithmic value.
The management device of a service node according to claim 10, wherein the step of determining the real-time capacity of the service node according to each of the real-time parameters and the performance parameter comprises:

Inputting each of the real-time parameters, each of the performance parameters, and each of the preset weight values into a second preset formula to calculate a real-time capacity of the service node;

The second preset formula: ValY = k 1 lgA 1 * Q 1 + k 2 lgA 2 * Q 2 + k 3 lgA 3 * Q 3 + k 4 lgA 4 * Q 4 , ValY is real-time capacity, A1 A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each of the preset weight values, Q1, Q2, Q3, and Q4 are real-time parameters, and lg is a logarithmic value.
The management device of a service node according to claim 11, wherein the step of determining the redundancy of the service node according to the limit capacity and the real-time capacity comprises:

Inputting the limit capacity and the real-time capacity into a third preset formula to calculate the redundancy of the service node;

The third preset formula is: R = 1-ValY / ValX, R is redundancy, ValY is real-time capacity, and ValX is limit capacity.
The management device of a service node according to claim 9, wherein the step of performing increase or decrease management on the service node according to the relationship between the redundancy and a preset threshold interval comprises:

Comparing the redundancy with a preset threshold interval to determine whether the redundancy is within a preset threshold interval;

When the redundancy is less than a lower boundary value of a preset threshold interval, adding a new service node corresponding to the service node;

When the redundancy is greater than the upper boundary value of the preset threshold interval, the service node is deleted.
The management device of a service node according to claim 13, wherein after the step of adding a new service node corresponding to the service node, the processor is configured to execute a management program of the service node to implement The following steps:

Read the new rated parameters corresponding to the new service node, and compare the new rated parameters with the rated parameters of other service nodes to determine that the rated parameters of the other service nodes match the new rated parameters Target rated parameters

Determining a service node to which the target rated parameter belongs, and assigning an access weight to the new service node according to the access weight of the home service node.
A computer-readable storage medium is characterized in that a management program of a service node is stored on the computer-readable storage medium, and the management program of the service node is executed by a processor to implement the following steps:

Reading a plurality of rated parameters of a service node and parameter coefficients corresponding to each of the rated parameters, and determining a plurality of performance parameters of the service node according to each of the rated parameters and each of the parameter coefficients, and the Service node capacity;

Read a plurality of real-time parameters of the service node, and determine the real-time capacity of the service node according to each of the real-time parameters and the performance parameter;

Determine the redundancy of the service node according to the limit capacity and the real-time capacity, and perform increase and decrease management on the service node according to the relationship between the redundancy and a preset threshold interval.
The computer-readable storage medium of claim 15, wherein the determining of a plurality of performance parameters of the service node and a limit of the service node are based on each of the rated parameters and each of the parameter coefficients. The steps for capacity include:

Grouping each of the rated parameters and each of the parameter coefficients based on parameter attributes, and generating a plurality of performance parameters of the service node according to the rated parameters and the parameter coefficients in each group;

Reading a preset weight value corresponding to each of the performance parameters, and inputting each of the preset weight value and each of the performance parameters into a first preset formula to calculate a limit capacity of the service node;

The first preset formula is: ValX = k 1 lgA 1 + k 2 lgA 2 + k 3 lgA 3 + k 4 lgA 4 , ValX is a limit capacity, and A1, A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each the preset weight value, and lg is a logarithmic value.
The computer-readable storage medium of claim 16, wherein the step of determining the real-time capacity of the service node according to each of the real-time parameters and the performance parameters comprises:

Inputting each of the real-time parameters, each of the performance parameters, and each of the preset weight values into a second preset formula to calculate a real-time capacity of the service node;

The second preset formula: ValY = k 1 lgA 1 * Q 1 + k 2 lgA 2 * Q 2 + k 3 lgA 3 * Q 3 + k 4 lgA 4 * Q 4 , ValY is real-time capacity, A1 A2, A3, and A4 are each of the performance parameters, k1, k2, k3, and k4 are each of the preset weight values, Q1, Q2, Q3, and Q4 are real-time parameters, and lg is a logarithmic value.
The computer-readable storage medium of claim 17, wherein the step of determining the redundancy of the service node according to the limit capacity and the real-time capacity comprises:

Inputting the limit capacity and the real-time capacity into a third preset formula to calculate the redundancy of the service node;

The third preset formula is: R = 1-ValY / ValX, R is redundancy, ValY is real-time capacity, and ValX is limit capacity.
The computer-readable storage medium of claim 15, wherein the step of adding or subtracting the service node according to the relationship between the redundancy and a preset threshold interval comprises:

Comparing the redundancy with a preset threshold interval to determine whether the redundancy is within a preset threshold interval;

When the redundancy is less than a lower boundary value of a preset threshold interval, adding a new service node corresponding to the service node;

When the redundancy is greater than the upper boundary value of the preset threshold interval, the service node is deleted.
The computer-readable storage medium of claim 15, wherein after the step of adding a new service node corresponding to the service node, a management program of the service node is executed by a processor to implement the following steps:

Read the new rated parameters corresponding to the new service node, and compare the new rated parameters with the rated parameters of other service nodes to determine that the rated parameters of the other service nodes match the new rated parameters Target rated parameters

Determining a service node to which the target rated parameter belongs, and assigning an access weight to the new service node according to the access weight of the home service node.