CN115473834A - Monitoring task scheduling method and system - Google Patents
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Abstract
The disclosure provides a monitoring task scheduling method and system, and relates to the technical field of emerging information. The method comprises the following steps: determining network delay parameters and health degree data between each monitoring node in the monitoring cluster and each monitoring target in the service application cluster, and network bandwidth parameters of each monitoring node; setting a weight coefficient of each monitoring target according to the monitoring requirement of each monitoring target; calculating the monitoring cost of each monitoring node on each monitoring target according to the network delay parameter, the health degree data, the network bandwidth parameter and the weight coefficient of each monitoring target; and scheduling the monitoring tasks according to the monitoring cost of each monitoring node on each monitoring target. The method and the device can reasonably schedule the monitoring tasks of the service application with different monitoring requirements in a large-scale distributed scene, and reduce the risk of inconsistency of the monitoring data with the actual state.
Description
Technical Field
The present disclosure relates to the field of emerging information technologies, and in particular, to a method and a system for scheduling a monitoring task.
Background
With the application of the cloud native technology in cluster management, currently, both the monitoring application cluster and the service application cluster gradually exhibit the characteristics of large scale and distribution, generally, in order to ensure the reliability of cluster monitoring, the monitoring application and the service application cluster are deployed in a cluster, the clusters may be distributed in a data center across regions, and the performance of different nodes of the monitoring cluster has differences when the monitoring data is collected. The monitoring performance requirements of different business applications are different, and therefore, reasonable monitoring task scheduling needs to be carried out.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to provide a method and a system for scheduling a monitoring task, which can improve the rationality of the scheduling of the monitoring task.
According to one aspect of the disclosure, a method for scheduling a monitoring task is provided, which includes: determining network delay parameters and health degree data between each monitoring node in the monitoring cluster and each monitoring target in the service application cluster, and network bandwidth parameters of each monitoring node; setting a weight coefficient of each monitoring target according to the requirement of each monitoring target on monitoring; calculating the monitoring cost of each monitoring node on each monitoring target according to the network delay parameter, the health degree data, the network bandwidth parameter and the weight coefficient of each monitoring target; and scheduling the monitoring tasks according to the monitoring cost of each monitoring node on each monitoring target.
In some embodiments, the monitoring task scheduling is performed according to the monitoring cost of each monitoring node for each monitoring target and the task saturation of each monitoring node.
In some embodiments, the weighting factors include: the first weight coefficient is determined according to the real-time requirement of the monitoring target on the monitoring data; the second weight coefficient is determined according to the monitoring data loss rate requirement of the monitoring target; and the third weight coefficient is determined according to the requirement of the monitoring target on the scale of the monitoring data.
In some embodiments, a global monitoring cost matrix is formed according to the monitoring cost of each monitoring node for each monitoring target.
In some embodiments, constructing the global monitoring cost matrix comprises: determining a first generation value of the ith monitoring node to the jth monitoring target according to the network delay between the ith monitoring node and the jth monitoring target and a first weight coefficient of the jth monitoring target; determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target; determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in the monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in the service application cluster.
In some embodiments, performing the monitoring task scheduling comprises: generating a task allocation strategy which meets the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node on each monitoring target; generating a task allocation file of each monitoring node according to the task allocation strategy; and configuring the monitoring cluster according to the task allocation file.
According to another aspect of the present disclosure, a monitoring task scheduling system is further provided, including: the monitoring agent module is configured to determine a network delay parameter and health degree data between each monitoring node in the monitoring cluster and each monitoring target in the service application cluster, and a network bandwidth parameter of each monitoring node; the weight management module is configured to set a weight coefficient of each monitoring target according to the requirement of each monitoring target on monitoring; the cost analysis calculation module is configured to calculate the monitoring cost of each monitoring node on each monitoring target according to the network delay parameter, the health degree data, the network bandwidth parameter and the weight coefficient of each monitoring target; and the task scheduling module is configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node on each monitoring target.
In some embodiments, the task scheduling module is further configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node for each monitoring target and the task saturation of each monitoring node.
In some embodiments, the weighting factors include: the first weight coefficient is determined according to the real-time requirement of the monitoring target on the monitoring data; the second weight coefficient is determined according to the monitoring data loss rate requirement of the monitoring target; and the third weight coefficient is determined according to the requirement of the monitoring target on the scale of the monitoring data.
In some embodiments, the cost analysis calculation module is further configured to construct a global monitoring cost matrix according to the monitoring cost of each monitoring node for each monitoring target.
In some embodiments, the cost analysis calculation module is further configured to determine a first cost value of the ith monitoring node to the jth monitoring target according to the network delay between the ith monitoring node and the jth monitoring target and the first weight coefficient of the jth monitoring target; determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target; determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in a monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in a service application cluster.
In some embodiments, the task scheduling module is further configured to generate a task allocation policy that satisfies a minimum cost of the monitoring requirement according to a monitoring cost of each monitoring node for each monitoring target; generating a task allocation file of each monitoring node according to the task allocation strategy; and configuring the monitoring cluster according to the task allocation file.
According to another aspect of the present disclosure, a monitoring task scheduling system is further provided, including: a memory; and a processor coupled to the memory, the processor configured to execute the above-described monitoring task scheduling method based on instructions stored in the memory.
According to another aspect of the present disclosure, a non-transitory computer-readable storage medium is also presented, on which computer program instructions are stored, which instructions, when executed by a processor, implement the monitoring task scheduling method as described above.
In the embodiment of the disclosure, the monitoring cost of each monitoring node for each monitoring target is calculated by measuring and calculating the network delay, the health degree data, the network bandwidth of each monitoring node and the weight coefficient of each monitoring target, so that the monitoring tasks of the service applications with different monitoring requirements can be reasonably scheduled in a large-scale distributed scene, and the risk that the monitoring data is inconsistent with the actual state is reduced.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:
FIG. 1 is a flow diagram of some embodiments of a monitor task scheduling method of the present disclosure;
FIG. 2 is a schematic flow chart diagram illustrating further embodiments of a monitor task scheduling method according to the present disclosure;
FIG. 3 is a schematic flow chart diagram illustrating further embodiments of a monitor task scheduling method according to the present disclosure;
FIG. 4 is a schematic block diagram of some embodiments of a monitor task scheduling system according to the present disclosure; and
fig. 5 is a schematic structural diagram of another embodiment of the monitoring task scheduling system according to the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
Fig. 1 is a flow diagram of some embodiments of a monitoring task scheduling method of the present disclosure.
At step 110, network delay parameters and health data between each monitoring node in the monitoring cluster and each monitoring target in the business application cluster, and network bandwidth parameters of each monitoring node are determined.
The examples of different nodes of the monitoring cluster are influenced by factors such as application health degree, physical topological distance, network bandwidth and the like, and the performance of the monitoring cluster is different when monitoring data are collected. In addition, different service applications have different requirements on real-time performance and accuracy of monitoring data, problems such as delay in updating of the monitoring data, data loss, omission of alarm information and the like occur frequently in large-scale clusters due to high network delay and insufficient network bandwidth between a monitoring node and a monitoring target, and for applications with high monitoring requirements such as network element applications, the state of the monitoring data and the actual state of the monitoring data have great difference due to network delay and the like, so that operation and maintenance personnel have the possibility of misjudgment on the actual service condition. Therefore, in this embodiment, in one monitoring period, the monitoring agent detects the network delay parameter, the health data, and the network bandwidth parameter of the monitoring cluster node of the monitoring cluster and the target cluster, so as to perform large-scale distributed application cluster monitoring task scheduling in the following.
In some embodiments, a business application cluster contains multiple business applications, and a business application is usually a cluster containing multiple pods (container instances), and when monitoring is divided, individual sub-modules of the business application are usually divided into a monitoring target, and a monitoring target may include one or more pods.
In step 120, a weighting factor for each monitored target is set according to the requirement of each monitored target for monitoring.
In some embodiments, different weight coefficients are set for different monitoring targets according to real-time performance of the monitoring targets on monitoring data, tolerance of health conditions of monitoring nodes and different scales of the monitoring data.
For example, the weight coefficient of each monitoring target includes: the system comprises a first weight coefficient, a second weight coefficient and a third weight coefficient, wherein the first weight coefficient is determined according to the real-time requirement of a monitoring target on monitoring data, the second weight coefficient is determined according to the requirement of the monitoring target on the loss rate of the monitoring data, and the third weight coefficient is determined according to the requirement of the monitoring target on the scale of the monitoring data.
In step 130, the monitoring cost of each monitoring node for each monitoring target is calculated according to the network delay parameter, the health degree data, the network bandwidth parameter, and the weight coefficient of each monitoring target.
In some embodiments, a global monitoring cost matrix is formed according to the monitoring cost of each monitoring node for each monitoring target.
For example, according to the network delay between the ith monitoring node and the jth monitoring target and the first weight coefficient of the jth monitoring target, determining the first generation value of the ith monitoring node on the jth monitoring target; determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target; determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in the monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in the service application cluster.
In step 140, a monitoring task is scheduled according to the monitoring cost of each monitoring node for each monitoring target.
In some embodiments, the monitoring task scheduling is performed according to the monitoring cost of each monitoring node for each monitoring target and the task saturation of each monitoring node.
In some embodiments, a task allocation strategy meeting the minimum cost of monitoring requirements is generated according to the monitoring cost of each monitoring node on each monitoring target; generating a task allocation file of each monitoring node according to the task allocation strategy; and configuring the monitoring cluster according to the task allocation file.
For example, there are two monitoring nodes a and b, and three monitoring targets p1, p2, and p3, the cost for monitoring p1, p2, and p3 by monitoring node a is 1, 1.5, and 2, respectively, the cost for monitoring p1, p2, and p3 by monitoring node b is 1.5, 1, and 1, respectively, and with the minimum cost for satisfying the monitoring requirement in production as the task allocation policy, the most reasonable allocation policy is that monitoring node a monitors p1, and monitoring node b monitors p2 and p3. If the monitoring node b can only monitor 1 monitoring target again, otherwise, the task saturation of the monitoring node is exceeded, the most reasonable distribution strategy is that the monitoring node a monitors p1 and p2, and the monitoring node b monitors p3.
In the embodiment, the monitoring cost of each monitoring node for each monitoring target is calculated by measuring and calculating the network delay, the health degree data, the network bandwidth of each monitoring node and the weight coefficient of each monitoring target of the monitoring cluster and the service application cluster, so that the monitoring tasks of the service applications with different monitoring requirements can be reasonably scheduled in a large-scale distributed scene, and the risk that the monitoring data is inconsistent with the actual state is reduced.
Fig. 2 is a flowchart illustrating a monitoring task scheduling method according to another embodiment of the disclosure.
In step 210, in a monitoring period, the service discovery module performs service discovery on the service application cluster through a service discovery mechanism, and transmits monitoring target information to the monitoring agent module and the cost analysis and calculation module. As shown in fig. 3.
In step 220, the monitoring agent module in the monitoring cluster calculates and calculates data such as network delay, health degree, network bandwidth and the like of each application in the service application cluster, and reports the result to the cost analysis and calculation module.
In step 230, the weight management module assigns weights to all monitored targets, wherein the weights are set according to the requirements of the specific service application on monitoring.
In some embodiments, the user enters information such as weights through a WebUI (portal for user interaction).
In step 240, the cost analysis calculation module calculates a global cost matrix according to the cost calculation model.
In some embodiments, the formula COST is utilized ij ＝N ij ×W1+H ij ×W2+B i xW 3 to obtain a global COST matrix COST ij (ii) a Wherein, COST ij The element(s) of (b) represents the cost of monitoring the target j by the monitoring node i; n is a radical of hydrogen ij Is a network delay matrix whose elements represent the network delay between monitoring node i and monitoring target j; w1 is a weight matrix of the monitoring target to network delay, the weight matrix is a diagonal matrix, and the value of the weight matrix depends on the real-time requirement of the monitoring target on monitoring data; h ij A health degree matrix, wherein elements of the health degree matrix represent the health degree between a monitoring node i and a monitoring target j; w2 is a weight matrix of the monitoring target required by the health degree, and the weight value depends on the requirement of the target application on the monitoring data loss rate; b i A network bandwidth matrix of the monitoring node, the value of which depends on the network bandwidth condition of the monitoring node; w3 is a weight matrix of the network bandwidth requirement of the monitoring target, and the weight value depends on the scale of the monitoring index of the monitoring target.
In step 250, the task scheduling module generates a task allocation strategy that satisfies the minimum cost of the monitoring requirement according to the global cost matrix and the task saturation condition of the monitoring node.
In step 260, the task scheduling module generates a task allocation file corresponding to the monitoring instance according to the policy, and issues the task allocation file to the monitoring agent module.
In step 270, the monitoring agent configures the monitoring system instance according to the task allocation file and starts to execute the monitoring task.
In the embodiment, different weights are set according to the monitoring requirements of the monitoring target, so that monitoring resources are reasonably scheduled, the monitoring requirements of applications with higher weight coefficients are met preferentially, the real-time performance and effectiveness of the application monitoring data can be improved, and the risk of misjudgment of operation and maintenance personnel on the application state is reduced. And the monitoring tasks of all the nodes of the monitoring cluster are reasonably planned according to the global cost matrix and the saturation of the tasks of the monitoring cluster, so that the nodes can be effectively distributed to execute the proper monitoring tasks, and the operation efficiency of the monitoring system is improved.
Fig. 4 is a schematic structural diagram of some embodiments of the monitoring task scheduling system of the present disclosure, which includes a monitoring agent module 410, a weight management module 420, a cost analysis calculation module 430, and a task scheduling module 440.
The monitoring agent module 410 is configured to determine network delay parameters and health data between each monitoring node in the monitoring cluster and each monitoring target in the business application cluster, as well as network bandwidth parameters for each monitoring node.
In some embodiments, in a monitoring period, the service discovery module performs service discovery on the service application cluster through a service discovery mechanism, transmits monitoring target information to the monitoring agent module, and the monitoring agent module calculates and calculates data such as network delay, health degree, network bandwidth and the like of each application in the service application cluster.
The weight management module 420 is configured to set a weight coefficient for each monitoring target according to the requirements of each monitoring target for monitoring.
In some embodiments, different weight coefficients are set for different monitoring targets according to the real-time performance of the monitoring data, the tolerance of the health condition of the monitoring node and the different scales of the monitoring data.
For example, the weight coefficient of each monitored target includes: the system comprises a first weight coefficient, a second weight coefficient and a third weight coefficient, wherein the first weight coefficient is determined according to the real-time requirement of a monitoring target on monitoring data, the second weight coefficient is determined according to the requirement of the monitoring target on the loss rate of the monitoring data, and the third weight coefficient is determined according to the requirement of the monitoring target on the scale of the monitoring data.
The cost analysis calculation module 430 is configured to calculate a monitoring cost of each monitoring node for each monitoring target according to the network delay parameter, the health data, the network bandwidth parameter, and the weight coefficient of each monitoring target.
In some embodiments, the cost analysis and calculation module 430 constructs a global monitoring cost matrix according to the monitoring cost of each monitoring node for each monitoring target.
For example, according to the network delay between the ith monitoring node and the jth monitoring target and the first weight coefficient of the jth monitoring target, determining the first generation value of the ith monitoring node on the jth monitoring target; determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target; determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in a monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in a service application cluster.
The task scheduling module 440 is configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node for each monitoring target.
In some embodiments, the task scheduling module 440 performs monitoring task scheduling according to the monitoring cost of each monitoring node for each monitoring target and the task saturation of each monitoring node.
In some embodiments, the task scheduling module 440 generates a task allocation policy that satisfies the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node for each monitoring target; generating a task allocation file of each monitoring node according to the task allocation strategy; and configuring the monitoring cluster according to the task allocation file.
In the embodiment, each node can be effectively distributed to execute a proper monitoring task, and the operation efficiency of the monitoring system is improved.
Fig. 5 is a schematic structural diagram of another embodiment of the monitoring task scheduling system according to the present disclosure. The system 500 includes a memory 510 and a processor 520. Wherein: the memory 510 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is used to store the instructions in the above embodiments. Processor 520 is coupled to memory 510 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 520 is configured to execute instructions stored in the memory.
In some embodiments, processor 520 is coupled to memory 510 by a BUS BUS 530. The system 500 may also be coupled to an external storage system 550 via a storage interface 540 for facilitating retrieval of external data, and may also be coupled to a network or another computer system (not shown) via a network interface 560. And will not be described in detail herein.
In the embodiment, the data instruction is stored in the memory, and the instruction is processed by the processor, so that the problem of reasonable scheduling of the monitoring tasks of the service application which is required by different monitoring in a large-scale distributed scene can be solved, and the risk of inconsistency between the monitoring data and the actual state is reduced.
In further embodiments, a computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the above-described embodiments. As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (14)
1. A monitoring task scheduling method comprises the following steps:
determining a network delay parameter and health degree data between each monitoring node in a monitoring cluster and each monitoring target in a service application cluster, and a network bandwidth parameter of each monitoring node;
setting a weight coefficient of each monitoring target according to the requirement of each monitoring target on monitoring;
calculating the monitoring cost of each monitoring node on each monitoring target according to the network delay parameter, the health degree data, the network bandwidth parameter and the weight coefficient of each monitoring target; and
and scheduling the monitoring tasks according to the monitoring cost of each monitoring node on each monitoring target.
2. The supervisory task scheduling method of claim 1, further comprising:
and scheduling the monitoring tasks according to the monitoring cost of each monitoring node for each monitoring target and the task saturation of each monitoring node.
3. The monitoring task scheduling method according to claim 1 or 2, wherein the weighting factor includes:
the first weight coefficient is determined according to the real-time requirement of the monitoring target on the monitoring data;
the second weight coefficient is determined according to the requirement of the monitoring target on the loss rate of the monitoring data; and
and the third weight coefficient is determined according to the monitoring target requirement on the monitoring data scale.
4. The monitor task scheduling method according to claim 3, further comprising:
and forming a global monitoring cost matrix according to the monitoring cost of each monitoring node on each monitoring target.
5. The monitoring task scheduling method of claim 4, wherein constructing a global monitoring cost matrix comprises:
determining a first generation value of the ith monitoring node to the jth monitoring target according to the network delay between the ith monitoring node and the jth monitoring target and a first weight coefficient of the jth monitoring target;
determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target;
determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and
and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in the monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in the service application cluster.
6. The monitoring task scheduling method according to claim 1 or 2, wherein performing monitoring task scheduling includes:
generating a task allocation strategy which meets the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node on each monitoring target;
generating a task allocation file of each monitoring node according to the task allocation strategy; and
and configuring the monitoring cluster according to the task allocation file.
7. A supervisory task scheduling system comprising:
the monitoring agent module is configured to determine a network delay parameter and health degree data between each monitoring node in the monitoring cluster and each monitoring target in the service application cluster, and a network bandwidth parameter of each monitoring node;
the weight management module is configured to set a weight coefficient of each monitoring target according to the requirement of each monitoring target on monitoring;
the cost analysis calculation module is configured to calculate the monitoring cost of each monitoring node on each monitoring target according to the network delay parameter, the health degree data, the network bandwidth parameter and the weight coefficient of each monitoring target; and
and the task scheduling module is configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node on each monitoring target.
8. The monitor task scheduling system of claim 7,
the task scheduling module is further configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node on each monitoring target and the task saturation of each monitoring node.
9. The monitor task scheduling system according to claim 7 or 8, wherein the weight coefficient includes:
the first weight coefficient is determined according to the real-time requirement of the monitoring target on the monitoring data;
the second weight coefficient is determined according to the requirement of the monitoring target on the loss rate of the monitoring data; and
and the third weight coefficient is determined according to the monitoring target requirement on the monitoring data scale.
10. The monitor task scheduling system of claim 9,
the cost analysis calculation module is further configured to form a global monitoring cost matrix according to the monitoring cost of each monitoring node on each monitoring target.
11. The supervisory task scheduling system of claim 10,
the cost analysis calculation module is further configured to determine a first generation value of the ith monitoring node to the jth monitoring target according to the network delay between the ith monitoring node and the jth monitoring target and a first weight coefficient of the jth monitoring target; determining a second generation value of the ith monitoring node to the jth monitoring target according to the health degree between the ith monitoring node and the jth monitoring target and a second weight coefficient of the jth monitoring target; determining a third generation value of the ith monitoring node to the jth monitoring target according to the network bandwidth of the ith monitoring node and a third weight coefficient of the jth monitoring target; and constructing a global monitoring cost matrix according to the sum of the first generation value, the second generation value and the third generation value of the ith monitoring node to the jth monitoring target, wherein elements in the global monitoring cost matrix represent the monitoring cost of the ith monitoring node to the jth monitoring target, i is a positive integer less than or equal to the number of monitoring nodes in the monitoring cluster, and j is a positive integer less than or equal to the number of monitoring targets in the service application cluster.
12. A monitor task scheduling system according to claim 7 or 8,
the task scheduling module is also configured to generate a task allocation strategy meeting the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node on each monitoring target; generating a task allocation file of each monitoring node according to the task allocation strategy; and configuring the monitoring cluster according to the task allocation file.
13. A supervisory task scheduling system comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the monitor task scheduling method of any of claims 1 to 6 based on instructions stored in the memory.
14. A non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the monitoring task scheduling method of any one of claims 1 to 6.
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