Adjustment and optimization strategy determination method, electronic equipment and computer readable medium

Abstract

The invention provides a method for determining an adjusting and optimizing strategy. The method comprises the following steps: setting at least one preset adjusting and optimizing scene according to a target index parameter; obtaining an effective index value, corresponding to the target index parameter, of a service flow in each preset tuning scene; and determining a target scene according to the effective index value corresponding to each preset tuning scene to obtain a target tuning strategy. The invention further provides electronic equipment and a computer readable medium.

Description

Technical Field

[0001] The present disclosure relates to the field of communication technology, and in particular to a method for determining a tuning strategy, an electronic device, and a computer-readable medium. Background Art

[0002] At present, after the TCP (Transmission Control Protocol) optimization module is launched, R&D personnel need to cooperate in debugging and try various optimization parameters back and forth to adjust the optimization parameters to the optimal value, which requires a lot of manpower and time. In addition, the optimization parameters are different at different locations and during different time periods. Summary of the invention

[0003] The embodiments of the present disclosure provide a method for determining a tuning strategy, an electronic device, and a computer-readable medium.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for determining a tuning strategy, comprising: setting at least one preset tuning scenario according to target indicator parameters; obtaining effective indicator values ​​corresponding to the target indicator parameters for business flows under each preset tuning scenario; determining a target scenario according to the effective indicator values ​​corresponding to each preset tuning scenario, and obtaining a target tuning strategy.

[0005] In a second aspect, an embodiment of the present disclosure provides an electronic device, comprising: one or more processors; a memory on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the tuning strategy described in the first aspect of the embodiment of the present disclosure.

[0006] In a third aspect, an embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the method for determining the tuning strategy described in the first aspect of the embodiment of the present disclosure is implemented.

[0007] In the method for determining the tuning strategy provided in the embodiment of the present disclosure, adaptive transmission performance optimization is achieved, and various parameters are automatically adjusted to the optimal value, which saves manpower and time for manual tuning, while improving the accuracy of optimization and having a wide range of applicability scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a flow chart of a method for determining a tuning strategy in an embodiment of the present disclosure;

[0009] Figure 2 It is a schematic diagram of an application scenario of a method for determining a tuning strategy in an embodiment of the present disclosure;

[0010] Figure 3It is a flowchart of the first part of a method for determining a tuning strategy in an embodiment of the present disclosure;

[0011] Figure 4 It is a flowchart of the second part of a method for determining a tuning strategy in an embodiment of the present disclosure;

[0012] Figure 5 is a schematic diagram of the structure of a terminal in an embodiment of the present disclosure;

[0013] Figure 6 is a result schematic diagram of a computer-readable medium in an embodiment of the present disclosure;

[0014] Figure 7 It is a flowchart of a method for determining a tuning strategy for multiple virtual machines in an embodiment of the present disclosure;

[0015] Figure 8 It is a flowchart of another method for determining a tuning strategy for multiple virtual machines in an embodiment of the present disclosure;

[0016] Fig. 9 It is a flowchart of a method for determining a tuning strategy for adaptive tuning in an embodiment of the present disclosure;

[0017] Fig.10 is a schematic diagram of a process of determining a decision time point by a timer in an embodiment of the present disclosure;

[0018] Fig.11 It is a flowchart of a method for determining a tuning strategy for setting a timer in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, the method for determining a tuning strategy, an electronic device, and a computer-readable medium provided by the present disclosure are described in detail below in conjunction with the accompanying drawings.

[0020] Example embodiments will be described more fully below with reference to the accompanying drawings, but the example embodiments may be embodied in different forms and should not be construed as limited to the embodiments set forth herein. On the contrary, the purpose of providing these embodiments is to make the present disclosure thorough and complete and to enable those skilled in the art to fully understand the scope of the present disclosure.

[0021] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

[0022] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0023] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "comprising" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded.

[0024] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

[0025] First, refer to Figure 1 , the present disclosure provides a method for determining a tuning strategy, including:

[0026] S1. Setting at least one preset tuning scenario according to the target indicator parameters;

[0027] S2. Obtaining effective indicator values ​​corresponding to the target indicator parameters of the service flow in each preset optimization scenario;

[0028] S3. Determine the target scenario according to the effective indicator values ​​corresponding to each preset tuning scenario to obtain a target tuning strategy.

[0029] In the embodiments of the present disclosure, the transmission performance optimization involved is the performance optimization based on TCP (Transmission Control Protocol) transmission, so as to achieve effective data transmission, improve the throughput and response speed of the network, and reduce the packet loss rate and delay; Figure 2 , the exemplary application scenarios of the method for determining the tuning strategy disclosed in the present invention are:

[0030] The service data message (i.e., service flow) is received through XGW (XG-PON Gateway, a gateway device that supports 10G optical fiber access technology); after passing through VRF1 (Virtual Routing Forwarding 1, the first virtual router), the service data message is subjected to message load balancing operation through the DRLB (Dynamic Ratio Load Balancing, load balancing based on dynamic ratio) virtual machine; the processed service data message is sent to the xSF (eXtensible Service Functions, multi-service function) forwarding module; the xSF forwarding module determines whether to optimize the service data message according to the policy configuration, and processes the service data message that needs to be optimized through the TCPO (Transmission Control Protocol Optimization, transmission control protocol optimization) optimization module, which can execute the tuning strategy selection and optimize the service data message according to the selected target tuning strategy; the message is forwarded to other modules (such as firewall) for processing through VRF2 (Virtual Routing Forwarding 2, the second virtual router).

[0031] In some embodiments, the target indicator parameters include at least one of the effective confirmation rate, queue overflow ratio, and retransmission rate within a preset time period, but the embodiments of the present disclosure are not limited to this. The target indicator parameters may also include average delay time, bandwidth utilization, throughput, etc.

[0032] In the embodiments of the present disclosure, the target indicator parameter is a system-level parameter that affects the transmission performance, and the target indicator parameter can be adjusted to optimize the network performance; the effective confirmation rate within a preset time period is used to measure the data transmission efficiency; the queue overflow ratio, that is, the frequency of data overflow in the queue, is used to reflect whether the system's ability to process data can meet the speed of data input; the retransmission rate represents the proportion of data packets that need to be retransmitted in all sent data packets, which is used to reflect the reliability and stability of the network. The target indicator parameter is not limited to this, and can also include other parameters that affect network performance, which are not specifically limited in the embodiments of the present disclosure.

[0033] Accordingly, in some embodiments, setting at least one preset tuning scenario according to the target indicator parameter includes:

[0034] S101. According to the target indicator parameters, set multiple optimization scenarios and one optimization-free scenario; wherein the optimization scenario indicates that the business flow is transmitted after being optimized corresponding to the target indicator parameters, and the optimization-free scenario indicates that the business flow is directly transmitted.

[0035] In an embodiment of the present disclosure, the preset tuning scenario includes a specific network environment and application requirements during the transmission process, wherein the application requirements may be a window value, a queue percentage threshold or an algorithm, etc. The preset tuning scenario specifically includes a plurality of optimization scenarios and an optimization-free scenario; in some embodiments, in the optimization scenario, the service flow is optimized to improve network performance, transmission speed and reduce latency, and in the optimization-free scenario, the service flow is not optimized and is directly transmitted. In one example, the window value of one optimization scenario is a fixed window mode, an enablement restriction that the queue usage ratio exceeds a first value, and the algorithm used is a first algorithm, and the window value of another optimization scenario is a non-fixed window mode, an enablement restriction that the queue usage ratio is lower than a second value, and the algorithm used is a second algorithm. The embodiment of the present disclosure does not specifically limit the optimization scenario.

[0036] In some embodiments, before setting at least one preset tuning scenario according to the target indicator parameter, the method further includes:

[0037] S0. Obtain the on / off state of the adaptive tuning corresponding to the business flow; wherein the on / off state of the adaptive tuning is used to indicate the optimization method of the business flow.

[0038] In the embodiment of the present disclosure, the on / off state of the adaptive tuning is determined by the TCPO optimization module. When the on / off state is on, the business flow is optimized according to the target indicator parameters. When the on / off state is closed, the business flow is optimized according to the default configuration parameters.

[0039] Reference Figure 3 In some embodiments, obtaining the effective indicator value of the service flow corresponding to the target indicator parameter in each preset optimization scenario includes:

[0040] S201, optimizing the business flow under each preset optimization scenario corresponding to the target indicator parameter;

[0041] S202: Determine the effective indicator value of the business flow in each preset optimization scenario corresponding to the target indicator parameter.

[0042] In an embodiment of the present disclosure, the effective index value may be a KPI (Key Performance Indicator) value, which is used to measure the optimization effect of the scenario corresponding to the target index parameter, and the effective index value is related to at least one of the effective confirmation rate, queue overflow ratio, and retransmission rate within a preset time period. In one example, the KPI value is obtained by sorting and weighted calculation.

[0043] Reference Figure 4In some embodiments, obtaining the effective indicator value of the service flow corresponding to the target indicator parameter in each preset optimization scenario includes:

[0044] S203, receiving at least one service flow within a preset time period after a preset time;

[0045] S204. According to the order in which the service flows are received within the preset time period, determine in sequence the effective indicator values ​​of the service flows corresponding to the target indicator parameters in each preset optimization scenario.

[0046] In the embodiments of the present disclosure, multiple business flows are received within a preset time period, and each business flow determines the target tuning strategy in turn according to the order of reception, and synchronizes the corresponding target tuning strategy to the next business flow; the preset moment is a periodic time node, that is, the adaptive optimization of transmission performance is performed according to the cycle, and the target tuning strategy is determined according to the business flow of the preset time period in each cycle; in an example, the preset time period is within N minutes after the preset moment, and N is a positive integer. The preset cycle, combined with the on and off state of the adaptive tuning, will periodically collect and compare business data, automatically adjust various parameters to be optimized to the optimal value, and can record the tuning records, reducing the manpower and time costs of tuning.

[0047] The execution environment of the optimization processing of each preset tuning scenario is a multi-virtual machine environment, and each virtual machine is used to execute the processing of the business flow under a preset tuning scenario; in one example, when there are multiple SFU (Selective Forwarding Unit) virtual machines, the OMU (Operation Maintain Unit) obtains a special SC (Special Service component) according to the preset policy decision, and executes the processing of the preset tuning environment through the special SC.

[0048] In addition, it is worth mentioning that after each virtual machine executes the processing of the service flow in a preset tuning scenario, it synchronizes the target tuning strategy of the current service flow to other SCs (Service components) according to the order of receiving the service flow.

[0049] In some embodiments, the target scenario is determined according to the effective indicator values ​​corresponding to each preset tuning scenario to obtain the target tuning strategy, including:

[0050] S301. Compare the effective indicator values ​​of the service flow in each preset optimization scenario to determine the target optimization strategy.

[0051] In some embodiments, the effective index value includes: a first effective index value in each optimization scenario, and a second effective index value in an optimization-free scenario;

[0052] Comparing the effective indicator values ​​of the service flow in each preset optimization scenario to determine the target optimization strategy includes:

[0053] S3011. Compare the first effective index value of the service flow in each of the optimization scenarios with the second effective index value in the optimization-free scenario to determine an intermediate optimization scenario corresponding to a target effective index value, wherein the target effective index value indicates that the first effective index value is better than the second effective index value;

[0054] S3012. Compare all target effective indicator values, determine the target scenario whose performance meets the preset conditions, and obtain the target tuning strategy.

[0055] In the embodiment of the present disclosure, the first effective index value represents the performance index of the service flow after optimization in multiple optimization scenarios corresponding to the target index parameter, and the second effective index value represents the performance index of the service flow when it is directly transmitted without optimization. After comparing each first effective index value with the second effective index value, a target effective index value is obtained, and the performance index of the target effective index value is better than the second effective index value, so as to effectively eliminate the situation where the performance is degraded after the optimization process;

[0056] All target effective indicator values ​​are compared with each other, the target scenario whose performance meets the preset conditions is determined, and the target tuning strategy is obtained. Here, the performance meets the preset conditions when the performance indicators corresponding to all target effective indicator values ​​are optimal.

[0057] In some embodiments, after determining the target scenario according to the effective indicator values ​​corresponding to each preset tuning scenario and obtaining the target tuning strategy, the method further includes:

[0058] S4. Synchronize the target optimization strategy to at least one service component so that the service component transmits the service flow according to the target optimization strategy.

[0059] In an embodiment of the present disclosure, a target tuning strategy is determined in a special SC obtained by an OMU decision. The special SC is used to determine the target tuning strategy based on the service flow within a preset time period so that the performance of transmitting the service flow is optimized under the target scenario of the target tuning strategy. At the same time, multiple other SCs are also included, each of which is used for data forwarding and transmission. The special SC synchronizes the target tuning strategy to other service components, and each service component transmits the service flow according to the target tuning strategy under the target scenario so as to optimize the service flow transmission effect.

[0060] The method for determining the tuning strategy provided in the embodiment of the present disclosure has a simple implementation process, solves the inconvenience caused by installing other services or using other tools, realizes adaptive transmission performance optimization, automatically adjusts various parameters to the optimal value, saves manpower and manual tuning time, and at the same time improves the accuracy of optimization, and has a wide range of applicable scenarios.

[0061] Second, refer to Figure 5 , an embodiment of the present disclosure provides an electronic device, comprising:

[0062] One or more processors 501;

[0063] A memory 502 having one or more programs stored thereon, which, when the one or more programs are executed by one or more processors, enables the one or more processors to implement the method for determining the tuning strategy of any one of the first aspects above;

[0064] One or more I / O interfaces 503 are connected between the processor and the memory and are configured to implement information exchange between the processor and the memory.

[0065] Among them, the processor 501 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 502 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory (FLASH); the I / O interface (read-write interface) 503 is connected between the processor 501 and the memory 502, and can realize information interaction between the processor 501 and the memory 502, including but not limited to a data bus (Bus), etc.

[0066] In some embodiments, the processor 501 , the memory 502 , and the I / O interface 503 are connected to each other via a bus 504 , and further connected to other components of the computing device.

[0067] Thirdly, refer to Figure 6 An embodiment of the present disclosure provides a computer-readable medium having a computer program stored thereon, and when the program is executed by a processor, the method for determining any one of the tuning strategies of the first aspect described above is implemented.

[0068] In order to enable those skilled in the art to more clearly understand the technical solutions provided by the embodiments of the present disclosure, the technical solutions provided by the embodiments of the present disclosure are described in detail below through specific embodiments:

[0069] Embodiment 1

[0070] This embodiment performs adaptive optimization on the transmission of service data streams through multiple virtual machines based on TCP, wherein the cycle of adaptive optimization is every hour, and the preset time period in each cycle is the first M minutes of every hour;

[0071] Reference Figure 7 , OMU decides a special SC according to the preset strategy. A special SC and multiple other SCs are virtual machines. Each virtual machine corresponds to an SFU. Automatic tuning collection is performed on the special SC. The automatic tuning collection here refers to the target tuning strategy obtained after the SFU optimizes the service flow;

[0072] Reference Figure 8 , on the special SC virtual machine and within the preset time period, the received service flows are in order of flow 1, flow 2, ..., flow N, and the optimization scenarios configured according to the target indicator parameters include scenario 1, scenario 2, ..., scenario N, and the configured optimization-free scenario is the BYPASS scenario; wherein, flow 1 is optimized using scenario 1, flow 2 is optimized using scenario 2, and so on, until N scenarios have completed the optimization of N service flows respectively, and flow N+1 is directly transmitted in the BYPASS scenario; obtain the KPI values ​​corresponding to flows 1 to N+1 in their respective scenarios; make decisions on flows 1 to N+1 in turn to obtain the target tuning strategy; wherein, the target tuning strategy determined by the Special SC will be synchronized to other SCs. When the target tuning strategy is determined, other SCs optimize the service flows received in the period through the target tuning strategy, and when the target tuning strategy is not determined, the default configuration parameters are used for optimization. Similarly, the transmission performance is optimized in other periods by stream polling.

[0073] Embodiment 2

[0074] This embodiment optimizes the service flow based on the adaptive switch.

[0075] Reference Figure 2 and Fig. 9 ,The service data message is forwarded to the TCPO optimization module by xSF. The TCPO optimization module first determines whether the adaptive tuning switch is turned on;

[0076] When the adaptive tuning switch is turned on, different scenarios are set according to the pre-configured tuning parameters (i.e., preset indicator parameters), including tuning scenarios and no-tuning scenarios;

[0077] Through the flow within the preset time period of each cycle, polling optimization is performed in various scenarios;

[0078] Analyze the KPI values ​​of parameter statistics in each scenario;

[0079] According to the KPI value, the intermediate tuning strategy of the service flow in the first N minutes of each hour is counted. The intermediate tuning strategy here represents the intermediate tuning scenario of the performance optimization-free scenario.

[0080] Save the intermediate optimization strategies and compare them with each other according to the corresponding KPI values. Use the KPI sorting method and multiply it with the corresponding weight (i.e. weighted) to decide the optimal strategy.

[0081] The optimal strategy is used to optimize the transmission of subsequent messages within the cycle.

[0082] When the adaptive tuning switch is not turned on, the default configuration parameters are obtained and the service flow within the period is optimized by using the default configuration parameters.

[0083] Embodiment 3

[0084] In this embodiment, the transmission performance optimization start is determined according to a timer.

[0085] Reference Fig.10 , after setting Special SC and turning on the adaptive tuning switch, set the optimization decision timer; the optimization decision timer calculates the decision time point based on the collection time and days;

[0086] Reference Fig.11 ,When the decision time point of the optimization decision timer arrives, the Special SC summarizes the incremental data under the optimization configuration of each thread and each scenario, and re-determines the next decision time point;

[0087] Calculate the effective confirmation rate, queue overflow ratio, and retransmission rate of each optimized configuration ACK (Acknowledge character) in the first N minutes;

[0088] Traverse the KPI values ​​corresponding to each service flow after optimization in the optimization scenario. If the KPI value is worse than the KPI value of the bypass scenario (i.e., the optimization-free scenario), the KPI value is invalid; if the KPI value is better than the KPI value of the bypass scenario, save the KPI value corresponding to the current scenario;

[0089] After the business flow has polled all scenarios, it compares the saved KPI values ​​and determines the best optimization scenario strategy for subsequent message processing.

[0090] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically contain computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0091] Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and / or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and / or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims

1. A method for determining a tuning strategy, the method comprising: Setting at least one preset tuning scenario according to the target indicator parameters; Obtaining effective indicator values ​​corresponding to the target indicator parameters of the business flow in each preset tuning scenario; The target scenario is determined according to the effective indicator values ​​corresponding to each preset tuning scenario, and the target tuning strategy is obtained.

2. The method for determining the tuning strategy according to claim 1, wherein: The target indicator parameter includes at least one of an effective confirmation rate, a queue overflow ratio, and a retransmission rate within a preset time period.

3. The method for determining a tuning strategy according to claim 1, wherein: The step of setting at least one preset tuning scenario according to the target indicator parameters includes: According to the target indicator parameters, multiple optimization scenarios and one optimization-free scenario are set; wherein the optimization scenario indicates that the business flow is transmitted after being optimized corresponding to the target indicator parameters, and the optimization-free scenario indicates that the business flow is directly transmitted.

4. The method for determining a tuning strategy according to claim 2, wherein: The obtaining of the effective indicator value of the service flow corresponding to the target indicator parameter in each preset optimization scenario includes: Optimizing the business flow under each preset optimization scenario corresponding to the target indicator parameter; Determine the effective indicator value of the business flow in each preset optimization scenario corresponding to the target indicator parameter.

5. The method for determining a tuning strategy according to any one of claims 1 to 4, wherein: Determine the target scenario based on the effective indicator values ​​corresponding to each preset tuning scenario, and obtain the target tuning strategy, including: The effective indicator values ​​of the business flow in each preset optimization scenario are compared to determine the target optimization strategy.

6. The method for determining the tuning strategy according to claim 5, wherein: The effective index value includes: a first effective index value in each optimization scenario, and a second effective index value in an optimization-free scenario; Comparing the effective indicator values ​​of the service flow in each preset optimization scenario to determine the target optimization strategy includes: Comparing the first effective index value of the service flow in each of the optimization scenarios with the second effective index value in the optimization-free scenario, determining an intermediate optimization scenario corresponding to a target effective index value, wherein the target effective index value indicates that the first effective index value is better than the second effective index value; All target effective indicator values ​​are compared to determine the target scenario whose performance meets the preset conditions and obtain the target tuning strategy.

7. The method for determining a tuning strategy according to any one of claims 1 to 4, wherein: The obtaining of the effective indicator value of the service flow corresponding to the target indicator parameter in each preset optimization scenario includes: Receiving at least one service flow within a preset time period after a preset time; According to the order in which the business flows are received within the preset time period, effective indicator values ​​corresponding to the target indicator parameters of the business flows in each preset optimization scenario are determined in sequence.

8. The method for determining a tuning strategy according to any one of claims 1 to 4, wherein: Before setting at least one preset tuning scenario according to the target indicator parameters, the method further includes: Obtain the on / off state of the adaptive tuning corresponding to the business flow; wherein the on / off state of the adaptive tuning is used to indicate the optimization method of the business flow.

9. The method for determining a tuning strategy according to claim 1, wherein: After determining the target scenario according to the effective indicator values ​​corresponding to each preset optimization scenario and obtaining the target optimization strategy, the method further includes: The target tuning strategy is synchronized to at least one service component so that the service component transmits the business flow according to the target tuning strategy.

10. An electronic device, comprising: one or more processors; A memory having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method for determining the tuning strategy according to any one of claims 1 to 9.

11. A computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for determining a tuning strategy according to any one of claims 1 to 9 is implemented.

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