CN115473834B - Monitoring task scheduling method and system - Google Patents
Monitoring task scheduling method and system Download PDFInfo
- Publication number
- CN115473834B CN115473834B CN202211114813.4A CN202211114813A CN115473834B CN 115473834 B CN115473834 B CN 115473834B CN 202211114813 A CN202211114813 A CN 202211114813A CN 115473834 B CN115473834 B CN 115473834B
- Authority
- CN
- China
- Prior art keywords
- monitoring
- target
- node
- cost
- monitoring target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012544 monitoring process Methods 0.000 title claims abstract description 533
- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000036541 health Effects 0.000 claims abstract description 33
- 239000011159 matrix material Substances 0.000 claims description 35
- 238000007728 cost analysis Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000004590 computer program Methods 0.000 claims description 10
- 230000037221 weight management Effects 0.000 claims description 5
- 238000010586 diagram Methods 0.000 description 14
- 230000006870 function Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012217 deletion Methods 0.000 description 1
- 230000037430 deletion Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0852—Delays
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/08—Monitoring or testing based on specific metrics, e.g. QoS, energy consumption or environmental parameters
- H04L43/0876—Network utilisation, e.g. volume of load or congestion level
- H04L43/0894—Packet rate
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/12—Network monitoring probes
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The disclosure provides a monitoring task scheduling method and a monitoring task scheduling system, and relates to the technical field of emerging information. The method comprises the following steps: determining network delay parameters and health 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; according to the network delay parameter, the health data, the network bandwidth parameter and the weight coefficient of each monitoring target, calculating the monitoring cost of each monitoring node to each monitoring target; and carrying out monitoring task scheduling according to the monitoring cost of each monitoring node to each monitoring target. According to the method and the device, the monitoring tasks of the business applications with different monitoring requirements can be reasonably scheduled in a large-scale distributed scene, and the risk that the monitoring data are inconsistent with the actual state is reduced.
Description
Technical Field
The present disclosure relates to the field of emerging information technologies, and in particular, to a method and system for scheduling a monitoring task.
Background
With the application of the cloud native technology in cluster management, the cluster of monitoring applications and the cluster of service applications are gradually characterized by large scale and distributed, and in general, in order to ensure the reliability of cluster monitoring, the monitoring applications and the service applications are deployed in diversity clusters, the clusters may be distributed in a cross-regional data center, and the monitoring of the instances of different nodes of the clusters has different performance when monitoring data is acquired. The monitoring performance requirements of different service applications are different, so that reasonable monitoring task scheduling is required.
Disclosure of Invention
The technical problem to be solved by the present disclosure is to provide a monitoring task scheduling method and system, which can improve the rationality of monitoring task scheduling.
According to an aspect of the present disclosure, a monitoring task scheduling method is provided, including: determining network delay parameters and health 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; according to the network delay parameter, the health data, the network bandwidth parameter and the weight coefficient of each monitoring target, calculating the monitoring cost of each monitoring node to each monitoring target; and carrying out monitoring task scheduling according to the monitoring cost of each monitoring node to each monitoring target.
In some embodiments, the monitoring task scheduling is performed according to the monitoring cost of each monitoring node to each monitoring target and the task saturation of each monitoring node.
In some embodiments, the weight coefficients 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 requirement of the monitoring target on the loss rate of the monitoring data; and a third weight coefficient is determined according to the monitoring data scale requirement of the monitoring target.
In some embodiments, a global monitoring cost matrix is formed according to the monitoring cost of each monitoring node to each monitoring target.
In some embodiments, constructing the global monitoring cost matrix includes: 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 to the jth monitoring target; determining a second cost 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; according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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 schedule includes: generating a task allocation strategy meeting the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node to 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, there is also provided a monitoring task scheduling system, including: the monitoring agent module is configured to determine network delay parameters and health 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; 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 to each monitoring target according to the network delay parameter, the health 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 to each monitoring target.
In some embodiments, the task scheduling module is further configured to monitor task scheduling according to a monitoring cost of each monitoring node for each monitoring target, and a task saturation of each monitoring node.
In some embodiments, the weight coefficients 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 requirement of the monitoring target on the loss rate of the monitoring data; and a third weight coefficient is determined according to the monitoring data scale requirement of the monitoring target.
In some embodiments, the cost analysis calculation module is further configured to construct a global monitoring cost matrix from 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 generation value of the ith monitoring node for the jth monitoring target based on 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 cost 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; according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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, the task scheduling module is further configured to generate a task allocation policy meeting a minimum cost of monitoring requirements according to the monitoring cost of each monitoring node to 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, there is also provided a monitoring task scheduling system, including: a memory; and a processor coupled to the memory, the processor configured to perform the above-described monitor task scheduling method based on instructions stored in the memory.
According to another aspect of the disclosure, there is also provided a non-transitory computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a monitoring task scheduling method as described above.
In the embodiment of the disclosure, the monitoring cost of each monitoring node to each monitoring target is calculated by measuring and calculating the network delay, the health data and the network bandwidth of each monitoring node of the monitoring cluster and the service application cluster 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 of inconsistent monitoring data and actual states is reduced.
Other features of the present disclosure and its advantages will become apparent from the following detailed description of exemplary embodiments of the disclosure, which proceeds with reference to the accompanying drawings.
Drawings
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 disclosure may be more clearly understood from the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow diagram of some embodiments of a monitoring task scheduling method of the present disclosure;
FIG. 2 is a flow diagram of further embodiments of a monitoring task scheduling method of the present disclosure;
FIG. 3 is a flow diagram of further embodiments of a monitoring task scheduling method of the present disclosure;
FIG. 4 is a schematic diagram of some embodiments of a monitoring task scheduling system of the present disclosure; and
FIG. 5 is a schematic diagram of other embodiments of a monitoring task scheduling system of 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, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless it is specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective parts shown in the drawings are not drawn in actual scale for 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 specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
Fig. 1 is a flow diagram of some embodiments of a monitoring task scheduling method of the present disclosure.
In 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, as well as network bandwidth parameters of each monitoring node, are determined.
Examples of different nodes of the monitoring cluster are affected by factors such as application health, physical topology distance, network bandwidth and the like, and performance is different when monitoring data are collected. In addition, different service applications have different requirements on real-time performance and accuracy of monitoring data, and under a large-scale cluster, problems such as delay of monitoring data update, data deletion, missing of alarm information and the like are caused by 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 larger difference due to network delay and the like, so that operation and maintenance personnel can misjudge the actual service conditions. Thus, in this embodiment, in one monitoring period, the monitoring agent detects the network delay parameters, the health data and the network bandwidth parameters of the monitoring cluster nodes of the monitoring cluster and the target cluster, so as to perform the subsequent large-scale distributed application cluster monitoring task scheduling.
In some embodiments, a business application cluster contains multiple business applications, one business application typically being a cluster containing multiple pods (container instances), when dividing the monitoring, typically the individual sub-modules of the business application will be divided into one monitoring target, which may include one or more pods.
In step 120, a weight coefficient of each monitoring target is set according to the requirement 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 health tolerance of the monitoring nodes and the scale 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 loss rate requirement of the monitoring target on the monitoring data, and the third weight coefficient is determined according to the scale requirement of the monitoring target on 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 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 to each monitoring target.
For example, determining a first generation value of the ith monitoring node for 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 cost 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; according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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, monitoring task scheduling is performed 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 to each monitoring target and the task saturation of each monitoring node.
In some embodiments, generating a task allocation strategy meeting the minimum cost of monitoring requirements according to the monitoring cost of each monitoring node to 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, three monitoring targets p1, p2 and p3, the costs of monitoring p1, p2 and p3 by the monitoring node a are 1, 1.5 and 2 respectively, the costs of monitoring p1, p2 and p3 by the monitoring node b are 1.5, 1 and 1 respectively, so that the minimum cost meeting the monitoring requirement is produced as a task allocation strategy, and then the most reasonable allocation strategy is that the monitoring node a monitors p1, and the monitoring node b monitors p2 and p3. If the monitoring node b can only monitor 1 monitoring target, otherwise, exceeding the task saturation of the monitoring node, the most reasonable allocation 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 data and the network bandwidth of each monitoring node of the monitoring cluster and the service application cluster 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 of inconsistent monitoring data and actual states is reduced.
FIG. 2 is a flow chart of other embodiments of a monitoring task scheduling method of the present 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 the monitoring target information to the monitoring agent module and the cost analysis calculation module. As shown in fig. 3.
In step 220, the monitoring agent module in the monitoring cluster calculates data such as network delay, health, network bandwidth, etc. of each application in the service application cluster, and reports the result to the cost analysis calculation module.
In step 230, the weight management module assigns weights to all monitoring targets, wherein the weight values are set according to the requirements of the specific business application for monitoring.
In some embodiments, the user inputs information such as weights through WebUI (portal of user interaction).
In step 240, the cost analysis computation module computes a global cost matrix from the cost computation model.
In some embodiments, the formula COST is utilized ij ＝N ij ×W1+H ij ×W2+B i X W3 to obtain global COST matrix COST ij The method comprises the steps of carrying out a first treatment on the surface of the Wherein COST is as follows ij The element of (2) represents the cost of monitoring target j by monitoring node i; n (N) ij The element of the network delay matrix is used for representing the network delay between the monitoring node i and the monitoring target j; w1 is a weight matrix of the monitoring target on network delay, wherein 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 The health degree matrix is a health degree matrix, and the elements of the health degree matrix represent health degrees between the monitoring node i and the monitoring target j; w2 is a weight matrix of the health requirements of the monitoring target, and the weight value depends on the requirements of the target application on the monitored data loss rate; b (B) i A network bandwidth matrix for the monitoring node, the value of which depends on the network bandwidth condition of the monitoring node; w3 is a weight matrix of the monitoring target on the network bandwidth requirement, and the weight value depends on the monitoring index scale of the monitoring target.
In step 250, the task scheduling module generates a task allocation policy 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 module configures a monitoring system instance according to the task allocation file to begin executing the monitoring task.
In the embodiment, different weights are set according to the monitoring target on the monitoring requirement, so that the monitoring resources are reasonably scheduled, the monitoring requirement of the application with the higher weight coefficient is preferentially met, the real-time performance and the effectiveness of the application monitoring data can be improved, and the risk of misjudgment of the application state by operation and maintenance personnel is reduced. According to the global cost matrix and the monitoring task of each node of the monitoring cluster, the monitoring task of each node of the monitoring cluster is reasonably planned, each node can be effectively distributed to execute the appropriate monitoring task, and the operation efficiency of the monitoring system is improved.
Fig. 4 is a schematic diagram of some embodiments of a monitoring task scheduling system of the present disclosure, including 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, and transmits monitoring target information to the monitoring agent module, and the monitoring agent module calculates and calculates data such as network delay, health, 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 of each monitoring target according to a requirement 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 health tolerance of the monitoring nodes and the scale 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 loss rate requirement of the monitoring target on the monitoring data, and the third weight coefficient is determined according to the scale requirement of the monitoring target on 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 computation module 430 constructs a global monitoring cost matrix according to the monitoring cost of each monitoring node for each monitoring target.
For example, determining a first generation value of the ith monitoring node for 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 cost 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; according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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.
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 to 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 monitoring requirements according to the monitored cost of each monitoring node to 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 allocated to execute a proper monitoring task, and the operation efficiency of the monitoring system is improved.
FIG. 5 is a schematic diagram of other embodiments of a monitoring task scheduling system of the present disclosure. The system 500 includes a memory 510 and a processor 520. Wherein: memory 510 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is used to store 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 BUS 530. The system 500 may also be connected to an external storage system 550 via a storage interface 540 for invoking external data, and may also be connected 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 by the memory, and then the instruction is processed by the processor, so that the problem of reasonable scheduling of the monitoring task of the business application which meets different monitoring requirements in a large-scale distributed scene can be solved, and the risk of inconsistent monitoring data and actual states is reduced.
In other embodiments, a computer readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the methods of the above embodiments. It will be apparent to those skilled in the art that 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, etc.) 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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. In order to avoid obscuring the concepts of the present disclosure, some details known in the art are not described. How to implement the solutions disclosed herein will be fully apparent to those skilled in the art from the above description.
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 above examples are for 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 disclosure. The scope of the present disclosure is defined by the appended claims.
Claims (14)
1. A monitoring task scheduling method, comprising:
determining network delay parameters and health data between each monitoring node in a monitoring cluster and each monitoring target in a 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 to 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 carrying out monitoring task scheduling according to the monitoring cost of each monitoring node to each monitoring target.
2. The monitoring task scheduling method of claim 1, further comprising:
and carrying out monitoring task scheduling according to the monitoring cost of each monitoring node to 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 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 data scale requirement of the monitoring target.
4. A monitoring 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 to each monitoring target.
5. The monitoring task scheduling method of claim 4, wherein constructing a global monitoring cost matrix comprises:
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 to the jth monitoring target;
determining a second cost 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;
according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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 the monitoring nodes in the monitoring cluster, and j is a positive integer less than or equal to the number of the 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 meeting the minimum cost of the monitoring requirement according to the monitoring cost of each monitoring node to 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 monitoring task scheduling system, comprising:
the monitoring agent module is configured to determine network delay parameters and health 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;
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 to each monitoring target according to the network delay parameter, the health 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 to each monitoring target.
8. The monitoring task scheduling system of claim 7, wherein,
the task scheduling module is further configured to perform monitoring task scheduling according to the monitoring cost of each monitoring node to each monitoring target and the task saturation of each monitoring node.
9. A monitoring task scheduling system according to claim 7 or 8 wherein the weight coefficients comprise:
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 data scale requirement of the monitoring target.
10. The monitoring task scheduling system of claim 9, wherein,
the cost analysis and calculation module is further configured to form a global monitoring cost matrix according to the monitoring cost of each monitoring node to each monitoring target.
11. The monitoring task scheduling system of claim 10, wherein,
the cost analysis calculation module is further configured to determine a first generation value of the ith monitoring node for 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 cost 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; according to the network bandwidth of the ith monitoring node and the third weight coefficient of the jth monitoring target, determining the third generation value of the ith monitoring node to 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 monitoring task scheduling system according to claim 7 or 8, wherein,
the task scheduling module is further configured to generate a task allocation strategy meeting the minimum cost of monitoring requirements according to the monitoring cost of each monitoring node to 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 monitoring task scheduling system, comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the monitoring task scheduling method of any one 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211114813.4A CN115473834B (en) | 2022-09-14 | 2022-09-14 | Monitoring task scheduling method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211114813.4A CN115473834B (en) | 2022-09-14 | 2022-09-14 | Monitoring task scheduling method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115473834A CN115473834A (en) | 2022-12-13 |
| CN115473834B true CN115473834B (en) | 2024-04-02 |
Family
ID=84333535
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211114813.4A Active CN115473834B (en) | 2022-09-14 | 2022-09-14 | Monitoring task scheduling method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115473834B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111625337A (en) * | 2020-05-28 | 2020-09-04 | 浪潮电子信息产业股份有限公司 | Task scheduling method and device, electronic equipment and readable storage medium |
| CN112162907A (en) * | 2020-09-30 | 2021-01-01 | 上海新炬网络信息技术股份有限公司 | Health degree evaluation method based on monitoring index data |
| WO2022016845A1 (en) * | 2020-07-21 | 2022-01-27 | 国云科技股份有限公司 | Multi-node monitoring method and apparatus, electronic device, and storage medium |
| CN114143226A (en) * | 2021-12-06 | 2022-03-04 | 浪潮云信息技术股份公司 | Dynamic cost calibration method and system for network delay of distributed database |
| WO2022160886A1 (en) * | 2021-01-29 | 2022-08-04 | Zhejiang Dahua Technology Co., Ltd. | Task allocation method, apparatus, storage medium, and electronic device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9934071B2 (en) * | 2015-12-30 | 2018-04-03 | Palo Alto Research Center Incorporated | Job scheduler for distributed systems using pervasive state estimation with modeling of capabilities of compute nodes |
-
2022
- 2022-09-14 CN CN202211114813.4A patent/CN115473834B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111625337A (en) * | 2020-05-28 | 2020-09-04 | 浪潮电子信息产业股份有限公司 | Task scheduling method and device, electronic equipment and readable storage medium |
| WO2022016845A1 (en) * | 2020-07-21 | 2022-01-27 | 国云科技股份有限公司 | Multi-node monitoring method and apparatus, electronic device, and storage medium |
| CN112162907A (en) * | 2020-09-30 | 2021-01-01 | 上海新炬网络信息技术股份有限公司 | Health degree evaluation method based on monitoring index data |
| WO2022160886A1 (en) * | 2021-01-29 | 2022-08-04 | Zhejiang Dahua Technology Co., Ltd. | Task allocation method, apparatus, storage medium, and electronic device |
| CN114143226A (en) * | 2021-12-06 | 2022-03-04 | 浪潮云信息技术股份公司 | Dynamic cost calibration method and system for network delay of distributed database |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115473834A (en) | 2022-12-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Jiang et al. | Distributed dynamic scheduling for cyber-physical production systems based on a multi-agent system | |
| US10613909B2 (en) | Method and apparatus for generating an optimized streaming graph using an adjacency operator combination on at least one streaming subgraph | |
| Babukartik et al. | Hybrid Algorithm using the advantage of ACO and Cuckoo Search for Job Scheduling | |
| CN103164261B (en) | Multicenter data task disposal route, Apparatus and system | |
| CN102063336B (en) | Distributed computing multiple application function asynchronous concurrent scheduling method | |
| WO2017036244A1 (en) | Digital simulation system of power distribution network | |
| CN103309946B (en) | Multimedia file processing method, Apparatus and system | |
| CN110162388A (en) | A kind of method for scheduling task, system and terminal device | |
| CN113918314A (en) | Task processing method, edge computing device, computer device, and medium | |
| CN110134507B (en) | A kind of cooperative computing method under edge calculations system | |
| CN109389518A (en) | Association analysis method and device | |
| CN103209096A (en) | Method and device for alarm processing | |
| CN109409746A (en) | A kind of production scheduling method and device | |
| CN113094246A (en) | Edge heterogeneous computing environment simulation system | |
| Qayyum et al. | IoT-Orchestration based nanogrid energy management system and optimal time-aware scheduling for efficient energy usage in nanogrid | |
| CN107995026B (en) | Management and control method, management node, managed node and system based on middleware | |
| CN115473834B (en) | Monitoring task scheduling method and system | |
| CN109298989A (en) | Operational indicator threshold value acquisition methods and device | |
| CN103617083A (en) | Storage scheduling method and system, job scheduling method and system and management node | |
| Raju et al. | Hybrid ant colony optimization and cuckoo search algorithm for job scheduling | |
| CN114138452B (en) | High-energy-efficiency computing node selection method and device in edge computing | |
| CN109684051A (en) | A kind of method and system of the hybrid asynchronous submission of big data task | |
| CN111415261B (en) | Control method, system and device for dynamically updating flow control threshold of bank system | |
| CN105119741A (en) | Cloud network reliability measuring method | |
| CN115618532A (en) | Network system simulation method and related device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |