CN109873709B - Platform scheduling method and device and multi-platform service system - Google Patents
Platform scheduling method and device and multi-platform service system Download PDFInfo
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- CN109873709B CN109873709B CN201711263229.4A CN201711263229A CN109873709B CN 109873709 B CN109873709 B CN 109873709B CN 201711263229 A CN201711263229 A CN 201711263229A CN 109873709 B CN109873709 B CN 109873709B
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Abstract
The disclosure provides a platform scheduling method, a platform scheduling device and a multi-platform service system, and relates to the technical field of big data. The platform scheduling method comprises the following steps: acquiring monitoring data of a service platform; determining the current state of the service platform according to the monitoring data based on the platform state model; and if the current state is the high-risk state, sending a switching message to the backup platform to switch to the service provided by the backup platform. By the method, the abnormity of the service platform can be timely found and actively switched to the backup platform to provide service, so that service interruption caused by passive switching when a fault occurs is avoided, and the service continuity and reliability of the service platform are improved.
Description
Technical Field
The disclosure relates to the technical field of big data, in particular to a platform scheduling method, a platform scheduling device and a multi-platform service system.
Background
The multi-system platform is composed of a plurality of independent peer-to-peer systems, and each system independently bears services and flow. Because each system of the multi-system platform independently bears respective service and is not communicated with each other, the scheduling among the systems can not be realized in a load balancing mode.
When a system fails and needs to perform service scheduling, the currently used method is as follows: and after the system fault is found, switching the service of the fault platform to a backup platform.
Disclosure of Invention
The inventor finds that switching the service platform after the system failure will cause service interruption during the failure and switching, and affect the normal use of the service object.
One object of the present disclosure is to improve the business continuity and reliability of a service platform.
According to an aspect of the present disclosure, a method for scheduling a platform is provided, including: acquiring monitoring data of a service platform; determining the current state of the service platform according to the monitoring data based on the platform state model; and if the current state is the high-risk state, sending a switching message to the backup platform to switch to the service provided by the backup platform.
Optionally, the monitoring data includes platform hardware parameters, service flow parameters, and service form parameters.
Optionally, the platform state model includes an association relationship between at least one of a hardware parameter, a service traffic parameter, or a service form parameter and the current state.
Optionally, the platform state model is generated based on a machine learning algorithm according to past monitoring data and platform performance parameters.
Optionally, the method further comprises: receiving a switching message from a service platform; reporting a switching message to the global equipment so that the global equipment can change the service configuration; and issuing a service switching instruction to the service object according to the confirmation information fed back by the global device.
By the method, the abnormity of the service platform can be timely found and actively switched to the backup platform to provide service, so that service interruption caused by passive switching when a fault occurs is avoided, and the service continuity and reliability of the service platform are improved.
According to another aspect of the present disclosure, a platform scheduling apparatus is provided, including: the service monitoring unit is configured to acquire monitoring data of the service platform; and the data analysis unit is configured to determine the current state of the service platform according to the monitoring data based on the platform state model, and send a switching message to the backup platform to switch to the backup platform to provide services if the current state is a high-risk state.
Optionally, the method further comprises: the platform monitoring unit is configured to receive a switching message sent to the backup platform by the data analysis unit; and the state issuing unit is configured to report a switching message to the global device so that the global device changes the service configuration, and issue a service switching instruction to the service object according to the confirmation information fed back by the global device.
Optionally, the method further comprises: and the state receiving unit is configured to send the switching message to the global device by the state publishing unit so that the global device changes the service configuration.
Optionally, the service monitoring unit and the data analysis unit are located on the service platform or in signal connection with the service platform; the platform monitoring unit and the state issuing unit are positioned on the backup platform or are in signal connection with the backup platform; and the state receiving unit is positioned in the global equipment or is in signal connection with the global equipment.
According to another aspect of the present disclosure, a platform scheduling apparatus is provided, including: a memory; and a processor coupled to the memory, the processor configured to perform the platform scheduling method above based on instructions stored in the memory.
The platform scheduling device can find the abnormity of the service platform in time and actively switch to the backup platform to provide service, thereby avoiding service interruption caused by passive switching when a fault occurs and improving the service continuity and reliability of the service platform.
According to one aspect of the present disclosure, a multi-platform service system is provided, including: the system comprises more than one service platform, a backup platform and a service platform, wherein the service platforms are configured to acquire monitoring data of the service platforms, determine the current state of the service platforms according to the monitoring data based on a platform state model, and send a switching message to the backup platform if the current state is a high-risk state; and the backup platform is connected with the service platform and is configured to provide services for the service objects of the service platform according to the switching messages from the service platform.
Optionally, the method further comprises: a global device configured to change a service configuration according to a handover message from a service platform; the backup platform is also configured to issue a service switching instruction to the service object according to the confirmation information fed back after the global device completes the change of the service configuration.
The multi-platform service system can timely find the abnormity of each service platform and actively switch to the backup platform to provide service, thereby avoiding service interruption caused by passive switching when a fault occurs and improving the service continuity and reliability of the service platform.
Further, according to an aspect of the present disclosure, a computer-readable storage medium is proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of any of the above platform scheduling methods.
By executing the instructions on the computer-readable storage medium, the abnormity of the service platform can be timely found and the service platform is actively switched to the backup platform to provide service, so that the service interruption caused by passive switching when a fault occurs is avoided, and the service continuity and the reliability of the service platform are improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a flowchart of one embodiment of a platform scheduling method of the present disclosure.
Fig. 2 is a flowchart of another embodiment of a platform scheduling method according to the present disclosure.
Fig. 3 is a schematic diagram of an embodiment of a platform scheduling apparatus according to the present disclosure.
Fig. 4 is a schematic diagram of another embodiment of a platform scheduling apparatus according to the present disclosure.
Fig. 5 is a schematic diagram of a platform scheduling apparatus according to another embodiment of the disclosure.
FIG. 6 is a schematic diagram of one embodiment of a multi-platform services system of the present disclosure.
Detailed Description
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
A flowchart of one embodiment of a platform scheduling method of the present disclosure is shown in fig. 1.
In step 101, service platform monitoring data is obtained. In one embodiment, the monitoring data includes platform hardware parameters, service traffic parameters, service configuration parameters, and other data, such as platform hardware parameters, such as CPU occupancy, service traffic parameters, such as current traffic size, and service configuration parameters, such as the type of service to be carried, the service characteristics of the type of service, and the load on the platform.
In step 102, the current state of the service platform is determined from the monitoring data based on the platform state model. In one embodiment, the state of the service platform may include a health state, and a high risk state is determined to be entered when various indicators in the monitoring data reach a certain threshold. The high-risk state can be considered as a critical state of the stability of the service platform, and the service platform reaching the high-risk state can maintain the currently provided service, but is likely to fail.
In step 103, if it is determined that the current state is the high risk state, a switching message is sent to the backup platform to switch to the service provided by the backup platform.
By the method, the abnormity of the service platform can be timely found and actively switched to the backup platform to provide service, so that service interruption caused by passive switching when a fault occurs is avoided, and the service continuity and reliability of the service platform are improved.
In one embodiment, the platform state model includes an association relationship between at least one of a hardware parameter, a service traffic parameter, or a service configuration parameter and a current state, for example, when an occupancy rate of the CPU reaches 90% or more, a number of currently-loaded calls reaches 90% or more of a maximum load capacity, or a majority of current services are services with a large data volume requirement, it may be determined that a corresponding platform state is a high-risk state.
By the method, the corresponding relation between the monitoring data and the platform state can be recorded by quantitative indexes, so that the platform state can be quickly determined according to the monitoring data.
In one embodiment, machine learning can be performed based on the operating data and the performance parameters of the service platforms themselves to form and update the platform state model, so that the confirmation of the platform state can better conform to the self condition of each service platform, and unnecessary or untimely platform switching can be avoided.
A flow diagram of another embodiment of a platform scheduling method of the present disclosure is shown in fig. 2.
In step 201, service platform monitoring data is obtained.
In step 202, the current state of the service platform is determined from the monitoring data based on the platform state model.
In step 203, if it is determined that the current state is the high risk state, a switching message is sent to the backup platform.
In step 204, after receiving the switching message, the backup platform reports the switching message to the global device so that the global device changes the service configuration.
In step 205, a service switching instruction is issued to the service object according to the confirmation information fed back by the global device.
By the method, after the service platform is confirmed to be in a high-risk state, the service platform can inform the backup platform of receiving the service task in time, the service platform can inform the global device of changing the service configuration in time so that the backup platform can meet the service bearing requirement, and the backup platform issues the service switching instruction to the service object of the service platform after the global device completes the configuration change, so that each service object is actively switched to the backup platform, thereby avoiding the service object from passively learning service interruption caused by the service platform fault, avoiding the repeated searching and attempted connection of the service object, and improving the service continuity and reliability of the service platform.
A schematic diagram of one embodiment of a platform scheduling apparatus of the present disclosure is shown in fig. 3. The service monitoring unit 301 can acquire service platform monitoring data. In one embodiment, the monitoring data includes platform hardware parameters, traffic flow parameters, and traffic shape parameters. The data analysis unit 302 can determine the current state of the service platform from the monitoring data based on the platform state model. In one embodiment, the platform state model includes an association of at least one of a hardware parameter, a traffic flow parameter, or a traffic morphology parameter with the current state. When determining that the current state is a high-risk state, the data analysis unit 302 can send a switch message to the backup platform to switch to the backup platform to provide services. The service monitoring unit 301 and the data analysis unit 302 may be located at the service platform or in signal connection with the service platform in order to collect monitoring data of the service platform and determine the status of the service platform in time.
The platform scheduling device can find the abnormity of the service platform in time and actively switch to the backup platform to provide service, thereby avoiding service interruption caused by passive switching when a fault occurs and improving the service continuity and reliability of the service platform.
In one embodiment, as shown in fig. 3, the platform scheduling apparatus may further include a platform monitoring unit 303 and a status issue unit 304. The platform monitoring unit 303 can receive the switching message sent by the data analysis unit, and the state issue unit 304 can report the switching message to the global device so that the global device changes the service configuration, and issue a service switching instruction to the service object according to the confirmation information fed back by the global device. The platform monitoring unit 303 and the status issue unit 304 may be located on or in signal connection with the backup platform.
The platform scheduling device can timely inform each service object of actively switching to the backup platform according to the switching message of the service platform, thereby avoiding the service object from passively acquiring service interruption caused by the service platform fault, avoiding the service object from repeatedly searching and trying to establish connection, and improving the service continuity and reliability of the service platform.
In one embodiment, as shown in fig. 3, the platform scheduling apparatus may further include a status receiving unit 305, which is capable of receiving a switching message sent by the status issuing unit to the global device, so that the global device changes the service configuration. The state receiving unit 305 may be located at a global device or signal-connected to a global device.
The platform scheduling device can change service configuration in time, so that the backup platform has the capability of carrying out service bearing by replacing the service platform, and the service bearing requirement is met.
Fig. 4 shows a schematic structural diagram of an embodiment of the platform scheduling apparatus according to the present disclosure. The platform scheduling means comprises a memory 401 and a processor 402. Wherein: the memory 401 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is for storing instructions in the corresponding embodiments of the platform scheduling method above. The processor 402 is coupled to the memory 401 and may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 402 is configured to execute instructions stored in the memory, and can discover an exception of the service platform in time and actively switch to the backup platform to provide a service, thereby improving service continuity and reliability of the service platform.
In one embodiment, as also shown in fig. 5, the platform scheduling apparatus 500 includes a memory 501 and a processor 502. The processor 502 is coupled to the memory 501 by a BUS 503. The platform scheduling device 500 may be further connected to an external storage device 505 through a storage interface 504 to call external data, and may be further connected to a network or another computer system (not shown) through a network interface 506. And will not be described in detail herein.
In the embodiment, the data instruction is stored in the memory, and the processor processes the instruction, so that the abnormity of the service platform can be found in time and the service platform is actively switched to the backup platform to provide service, and the service continuity and reliability of the service platform are improved. .
In another embodiment, 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 corresponding embodiment of the platform scheduling method. 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.
A schematic diagram of one embodiment of the multi-platform services system of the present disclosure is shown in fig. 6. Each of the service platforms 611-61 n includes a service monitoring unit and a data analysis unit. The backup platform 62 also has a plurality of devices capable of carrying services. The backup platform 62 includes a platform monitoring unit and a platform status issuing unit.
In one embodiment, a global device, such as the global configuration management device 63, may be further included, or a global voice access device 64 may be further included, each having a platform state receiving unit and a respective adjusting unit, respectively.
In 601, the service monitoring unit located in the service platform 611 acquires monitoring data of each device of the same platform, and determines the current state of the service platform through the data analysis unit.
In 602, when the current state of the service platform 611 is the high-risk state, the data analysis unit sends a handover message to the platform detection unit of the backup platform 62.
In 603, the platform monitoring unit activates the respective devices of the present platform in preparation for starting providing services.
In 604, the platform state issuing unit of the backup platform notifies the global device that the backup platform provides services, and the notified information may include which platform is switched with the backup platform, the switching direction, whether the switching is successful, and the like. Global devices such as the global configuration management device 63 perform configuration adjustment within the domain, and the voice access device 64 performs automatic adjustment of access settings, etc.
At 605, the global device feeds back a message to the backup platform 62 after completing the adjustment, informing the backup platform 62 that the preparation for switching is completed.
In 606, the platform state publication unit of the backup platform 62 switches to the agent serviced by the service platform 611 to service the backup platform 62. After receiving the switching information, the seat automatically logs in again according to the switching information, and the switching is completed without influencing the service.
The multi-platform service system can timely find the abnormity of each service platform and actively switch to the backup platform to provide service, thereby avoiding service interruption caused by passive switching when a fault occurs and improving the service continuity and reliability of the service platform.
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.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Finally, it should be noted that: the above examples are intended only to illustrate the technical solutions of the present disclosure and not to limit them; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will understand that: modifications to the specific embodiments of the disclosure or equivalent substitutions for parts of the technical features may still be made; all such modifications are intended to be included within the scope of the claims of this disclosure without departing from the spirit thereof.
Claims (4)
1. A multi-platform service system comprising:
the system comprises more than one service platform, a backup platform and a service platform, wherein the service platforms are configured to acquire monitoring data of the service platforms, determine the current state of the service platforms according to the monitoring data based on a platform state model, and send a switching message to the backup platform if the current state is a high-risk state;
a global device configured to change a service configuration according to a handover message from the service platform;
the backup platform connected with the service platform is configured to notify the global device of the service provided by the backup platform according to a switching message from the service platform, and the notified information includes which platform is switched with the backup platform; and issuing a service switching instruction to the service object of the service platform according to the confirmation information fed back after the global device finishes changing the service configuration, and providing service for the service object of the service platform.
2. The system of claim 1, wherein the monitoring data includes platform hardware parameters, traffic flow parameters, and traffic morphology parameters.
3. The system of claim 2, wherein the platform state model includes an association of at least one of the hardware parameters, the traffic flow parameters, or the traffic shape parameters with the current state.
4. The system of claim 1, 2 or 3, wherein the platform state model is generated based on past monitoring data and platform performance parameters based on a machine learning algorithm.
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| CN101136900A (en) * | 2006-10-16 | 2008-03-05 | 中兴通讯股份有限公司 | Fast transparent fault shift device and implementing method facing to service |
| EP2866410A1 (en) * | 2013-10-22 | 2015-04-29 | Canon Denshi Kabushiki Kaisha | Apparatus for switching between multiple servers in a web-based system |
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