CN113056019A

CN113056019A - Method and apparatus for scheduling resources

Info

Publication number: CN113056019A
Application number: CN201911361224.4A
Authority: CN
Inventors: 王丹墨; 龙彪; 尹珂; 王庆扬; 王波; 赵晔; 曹磊
Original assignee: China Telecom Corp Ltd
Current assignee: China Telecom Corp Ltd
Priority date: 2019-12-26
Filing date: 2019-12-26
Publication date: 2021-06-29
Anticipated expiration: 2039-12-26
Also published as: CN113056019B

Abstract

The present disclosure provides a method and apparatus for scheduling resources. After receiving an analysis request sent by network side equipment, the device for scheduling resources extracts an identifier of an application used by a user side from the analysis request, wherein the network side equipment is a network function NF or a network management OAM; acquiring resource information associated with the application through the big data; and feeding back the acquired resource information to the network side equipment so that the network side equipment can execute the application according to the resource information. According to the method and the device, the selection of the frequency and the network is carried out according to the application condition of the user service, so that the energy consumption of the 5G system is reduced and the bandwidth is saved under the condition of ensuring the user experience, and the energy-saving capability of the network is improved.

Description

Method and apparatus for scheduling resources

Technical Field

The present disclosure relates to the field of communications, and in particular, to a method and an apparatus for scheduling resources.

Background

In the existing 5G system, the energy consumption of the high frequency NR (New Radio, New air interface) is larger than that of the LTE (Long Term Evolution), and if a user always resides on the high frequency NR and only uses a low value service (P2P (Peer to Peer) download) instead of an emerging service (e.g., VR (Virtual Reality), AR (Augmented Reality), etc.), the bandwidth and energy may be wasted, and the experience of other users may be affected.

In addition, the NWDAF (Network Data analysis Function) in the 5G system has the following functions:

collect event subscription information provided by NFs (Network functions) and OAM (Operation, Maintenance and Administration) such as AMF (Access and Mobility Management Function), SMF (Session Management Function), PCF (Policy Control Function), and provide analysis information to the NFs and OAM, wherein the analysis information may be statistical analysis or predictive analysis of past events.

Retrieve information of the Data store (e.g., UDR (Unified Data Repository function) gets subscription related information through UDM (Unified Data Management function)).

Retrieve information about NF

Supply of information to NF on demand

Therefore, the current NWDAF cannot select the frequency and the network according to the application condition of the user service.

Disclosure of Invention

The present disclosure provides a scheme for frequency and network selection for user service application. Therefore, the energy-saving capability of the network can be improved under the condition of ensuring the user experience.

According to a first aspect of the embodiments of the present disclosure, there is provided a method for resource scheduling, including: after receiving an analysis request sent by network side equipment, extracting an identifier of an application used by a user side from the analysis request, wherein the network side equipment is a network function NF or a network management OAM; acquiring resource information associated with the application through big data; and feeding back the acquired resource information to the network side equipment so that the network side equipment can execute the application according to the resource information.

In some embodiments, parameters associated with the application are obtained through big data, the parameters including at least one of a service mean opinion value, MOS, corresponding energy consumption, user demand, network status, and quality of service; determining a corresponding significant value according to the acquired parameters; and determining an access network corresponding to the application according to the significance value.

In some embodiments, determining the respective saliency value from the acquired parameters comprises: and determining a corresponding significant value according to the service MOS and the corresponding energy consumption.

In some embodiments, obtaining resource information associated with the application through big data further comprises: acquiring the use preference of the user and the running requirement of the application through big data; determining an access network for running the application according to the use preference of the user; and determining the frequency band of the application in the determined access network according to the operation requirement of the application.

According to a second aspect of the embodiments of the present disclosure, there is provided an apparatus for resource scheduling, including: the extraction module is configured to extract an identifier of an application used by a user side from an analysis request after receiving the analysis request sent by a network side device, wherein the network side device is a network function NF or a network management OAM; an information acquisition module configured to acquire resource information associated with the application through big data; and the sending module is configured to feed back the acquired resource information to the network side equipment so that the network side equipment can execute the application according to the resource information.

In some embodiments, the information acquisition module is configured to acquire, through big data, parameters associated with the application, the parameters including at least one of a mean opinion value MOS of the service, a respective energy consumption, a user demand, a network status, and a quality of service, determine a respective saliency value from the acquired parameters, determine an access network corresponding to the application from the saliency value.

In some embodiments, the information acquisition module is configured to determine the respective significance value from the service MOS and the respective energy consumption.

In some embodiments, the information obtaining module is configured to obtain, through big data, usage preferences of the user and operation requirements of the application, determine an access network for operating the application according to the usage preferences of the user, and determine a frequency band of the application in the determined access network according to the operation requirements of the application.

According to a third aspect of the embodiments of the present disclosure, there is provided an apparatus for scheduling resources, including: a memory configured to store instructions; a processor coupled to the memory, the processor configured to perform a method implementing any of the embodiments described above based on instructions stored by the memory.

According to a fourth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer instructions are stored, and when executed by a processor, the computer-readable storage medium implements the method according to any of the embodiments described above.

Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.

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 present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:

FIG. 1 is a flow diagram of a method for resource scheduling according to one embodiment of the present disclosure;

fig. 2 is a schematic structural diagram of an apparatus for resource scheduling according to an embodiment of the present disclosure;

fig. 3 is a schematic structural diagram of an apparatus for resource scheduling according to another embodiment of the present disclosure;

fig. 4 is a schematic resource scheduling flow according to an embodiment of the present disclosure.

It should be understood that the dimensions of the various parts shown in the figures are not drawn to scale. Further, the same or similar reference numerals denote the same or similar components.

Detailed Description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the disclosure, its application, or uses. The present disclosure may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. It should be noted that: the relative arrangement of parts and steps, the composition of materials and values set forth in these embodiments are to be construed as illustrative only and not as limiting unless otherwise specifically stated.

The use of the word "comprising" or "comprises" and the like in this disclosure means that the elements listed before the word encompass the elements listed after the word and do not exclude the possibility that other elements may also be encompassed.

All terms (including technical or scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs unless specifically defined otherwise. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

Fig. 1 is a flow diagram of a method for resource scheduling according to one embodiment of the present disclosure. In some embodiments, the following method steps for resource scheduling are performed by an apparatus for resource scheduling.

In step 101, after receiving an analysis request sent by a network side device, an identifier of an application used by a user side is extracted from the analysis request.

The network side equipment is network function NF or network management OAM.

At step 102, resource information associated with an application is obtained through big data.

In some embodiments, parameters associated with an application are obtained through big data. The parameters include at least one of a Mean Opinion Score (MOS), corresponding energy consumption, user demand, network status, and service quality, and the corresponding significant Value (signifiance Value) is determined according to the obtained parameters, and an access network corresponding to the application is determined according to the significant Value.

In some embodiments, the respective significance value is determined from the service MOS and the respective energy consumption.

For example, the significance value is service MOS/power consumption.

After obtaining the significant value of the application, the optimal RAT (Radio Access Technology) is selected for the application by comprehensively considering the above parameters according to the priority of the application and the location or time information of the application, as shown in table 1. The specific access policy may be executed by the PCF or OAM.

Application ID	Position of	Preferred RAT	Significance value
				Application ID-1	Cell-1	NR-RAN	8
Application ID-2	TA-1	E-UTRAN	6
				Application ID-3	Cell-2	NR-RAN	9
Application ID-4	TA-1	E-UTRAN	5
				Application ID-1	Cell-3	E-UTRAN	7
…	…	…	…

TABLE 1

This is to be explained here. The priority of the application can be dynamically adjusted according to the network state, the application scene requirements and the like, and different operators can have different priorities for the application. For example, in Table 1, Application ID-1 corresponds to two locations, Cell-1 and Cell-3, but because of the difference in location and significance, the RATs selected by Application ID-1 for different locations will not be the same.

In other embodiments, the usage preference of the user and the operation requirement of the application are obtained through the big data, the access network for operating the application is determined according to the usage preference of the user, and the frequency band applied to the determined access network is determined according to the operation requirement of the application.

For example, it is determined that the user wishes to execute the application under the 5G network according to the user's usage preference. And then determining a corresponding significant value according to the operation requirement of the application, thereby determining a corresponding frequency band according to the significant value.

After the significant value of the application is obtained, the preferred frequency band is selected for the application by comprehensively considering the above parameters according to the priority of the application and the location or time information of the application, as shown in table 2. The specific access policy may be executed by the PCF or OAM.

Application ID	Position of	Preferred frequency band	Significance value
				Application ID-1	Cell-1	5G high frequency band	8
Application ID-2	TA-1	5G low frequency band	5
				Application ID-3	Cell-2	5G high frequency band	7
Application ID-4	TA-2	5G low frequency band	3
				Application ID-1	Cell-3	5G low frequency band	6
…	…	…	…

TABLE 2

It should be noted that the priority of the application may be dynamically adjusted according to the network status, the application scenario requirement, and the like, and different operators may also have different priorities for the application. For example, in Table 2, Application ID-1 corresponds to two locations, Cell-1 and Cell-3, but the frequency bands selected by Application ID-1 at different locations will not be the same due to the difference in location and significance.

In step 103, the acquired resource information is fed back to the network side device, so that the network side device executes the application according to the resource information.

In the method for resource scheduling provided by the above embodiment of the present disclosure, by selecting a frequency and a network according to an application situation of a user service, energy consumption of a 5G system (for example, high frequency NR) is reduced and a bandwidth is saved while ensuring user experience, thereby improving energy saving capability of the network.

Fig. 2 is a schematic structural diagram of an apparatus for resource scheduling according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus for resource scheduling includes an extracting module 21, an information obtaining module 22, and a transmitting module 23.

The extracting module 21 is configured to extract an identifier of an application used by a user side from an analysis request after receiving the analysis request sent by a network side device, where the network side device is a network function NF or a network management OAM.

The information acquisition module 22 is configured to acquire resource information associated with an application through big data.

In some embodiments, the information acquisition module 22 acquires parameters associated with the application through big data. The parameters comprise at least one of service MOS, corresponding energy consumption, user requirements, network state and service quality, corresponding significant values are determined according to the obtained parameters, and access networks corresponding to the applications are determined according to the significant values.

In some embodiments, the information acquisition module 22 determines the respective significance value from the service MOS and the respective energy consumption.

For example, the significance value is service MOS/power consumption.

After obtaining the significance of the application, the optimal RAT is selected for the application by comprehensively considering the above parameters according to the priority of the application and the location or time information of the application, as shown in table 1 above. In addition, the priority of the application may be dynamically adjusted according to the network status, the application scenario requirements, and the like, and different operators may also have different priorities for the application. For example, in Table 1, Application ID-1 corresponds to two locations, Cell-1 and Cell-3, but because of the difference in location and significance, the RATs selected by Application ID-1 for different locations will not be the same.

In other embodiments, the information obtaining module 22 obtains the usage preference of the user and the operation requirement of the application through the big data, determines an access network for operating the application according to the usage preference of the user, and determines the frequency band applied in the determined access network according to the operation requirement of the application.

After the significance value of the application is obtained, the preferred frequency band is selected for the application by comprehensively considering the above parameters according to the priority of the application and the location or time information of the application, as shown in table 2 above. In addition, the priority of the application may be dynamically adjusted according to the network status, the application scenario requirements, and the like, and different operators may also have different priorities for the application. For example, in Table 2, Application ID-1 corresponds to two locations, Cell-1 and Cell-3, but the frequency bands selected by Application ID-1 at different locations will not be the same due to the difference in location and significance.

The sending module 23 is configured to feed back the acquired resource information to the network side device, so that the network side device executes the application according to the resource information.

In the apparatus for resource scheduling provided in the foregoing embodiment of the present disclosure, by selecting a frequency and a network according to an application situation of a user service, energy consumption of a 5G system (e.g., high frequency NR) is reduced and a bandwidth is saved while ensuring user experience, so that an energy saving capability of the network is improved.

Fig. 3 is a schematic structural diagram of an apparatus for resource scheduling according to another embodiment of the present disclosure. As shown in fig. 3, the apparatus includes a memory 31 and a processor 32.

The memory 31 is used to store instructions. The processor 32 is coupled to the memory 31. The processor 32 is configured to perform a method as referred to in any of the embodiments of fig. 1 based on the instructions stored by the memory.

As shown in fig. 3, the apparatus further includes a communication interface 33 for information interaction with other devices. Meanwhile, the device also comprises a bus 34, and the processor 32, the communication interface 33 and the memory 31 are communicated with each other through the bus 34.

The Memory 31 may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM). Such as at least one disk storage. The memory 31 may also be a memory array. The storage 31 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules.

Further, the processor 32 may be a central processing unit, or may be an ASIC (Application Specific Integrated Circuit), or one or more Integrated circuits configured to implement embodiments of the present disclosure.

The present disclosure also provides a computer-readable storage medium. The computer-readable storage medium stores computer instructions, and the instructions, when executed by the processor, implement the method according to any one of the embodiments in fig. 1.

It should be noted that the apparatus for resource scheduling according to any of the embodiments in fig. 2 or fig. 3 may be an NWDAF, or may be a component of an NWDAF.

In step 401, the NF/OAM sends a subscription request to the NWDAF. The subscription request comprises an application identifier and auxiliary analysis information used for outputting energy saving correlation by the NWDAF.

At step 402, the NWDAF analyzes to determine the network accessed, or the frequency selected under the same network.

For example, in scenario 1, if a user is not active or low value services are applied (e.g., P2P download), network selection from NG-RAN to E-UTRAN is implemented for such user applications, thereby achieving the goal of power saving and not affecting the experience of other users. If the user's traffic has a high demand for low latency and high bandwidth (e.g., VR, AR, etc. traffic), no network selection is applied for this type of user and it is made to reside on the NR.

As another example, in scenario 2, if a user's traffic does not require ultra-high bandwidth support but needs to reside on an NR, then frequency selection from a high frequency NR to a low frequency NR applies for such user. If the user's traffic has a high demand for ultra-high bandwidth, frequency selection is not applied and is left to reside on the high frequency NR for such users.

In step 403, the NWDAF sends a subscription request response to the NF/OAM to provide a corresponding resource selection result.

In some embodiments, the functional modules may be implemented as a general purpose Processor, a Programmable Logic Controller (PLC), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), or other Programmable Logic device, discrete Gate or transistor Logic, discrete hardware components, or any suitable combination thereof, for performing the functions described in this disclosure.

So far, embodiments of the present disclosure have been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.

Although some specific embodiments of the present disclosure have been described in detail by way of example, it should be understood by those skilled in the art that the foregoing examples are for purposes of illustration only and are not intended to limit the scope of the present disclosure. It will be understood by those skilled in the art that various changes may be made in the above embodiments or equivalents may be substituted for elements thereof without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for resource scheduling, comprising:

after receiving an analysis request sent by network side equipment, extracting an identifier of an application used by a user side from the analysis request, wherein the network side equipment is a network function NF or a network management OAM;

acquiring resource information associated with the application through big data;

and feeding back the acquired resource information to the network side equipment so that the network side equipment can execute the application according to the resource information.

2. The method of claim 1, wherein obtaining resource information associated with the application through big data comprises:

acquiring parameters associated with the application through big data, wherein the parameters comprise at least one of a service mean opinion value (MOS), corresponding energy consumption, user requirements, network state and service quality;

determining a corresponding significant value according to the acquired parameters;

and determining an access network corresponding to the application according to the significance value.

3. The method of claim 2, wherein determining respective significance values from the acquired parameters comprises:

and determining a corresponding significant value according to the service MOS and the corresponding energy consumption.

4. The method of claim 1, wherein obtaining resource information associated with the application through big data further comprises:

acquiring the use preference of the user and the running requirement of the application through big data;

determining an access network for running the application according to the use preference of the user;

and determining the frequency band of the application in the determined access network according to the operation requirement of the application.

5. An apparatus for resource scheduling, comprising:

the extraction module is configured to extract an identifier of an application used by a user side from an analysis request after receiving the analysis request sent by a network side device, wherein the network side device is a network function NF or a network management OAM;

an information acquisition module configured to acquire resource information associated with the application through big data;

and the sending module is configured to feed back the acquired resource information to the network side equipment so that the network side equipment can execute the application according to the resource information.

6. The apparatus of claim 5, wherein,

the information acquisition module is configured to acquire parameters associated with the application through big data, the parameters including at least one of a service mean opinion value MOS, a corresponding energy consumption, a user demand, a network status and a service quality, determine a corresponding saliency value according to the acquired parameters, and determine an access network corresponding to the application according to the saliency value.

7. The apparatus of claim 6, wherein,

the information acquisition module is configured to determine a respective significance value from the service MOS and the respective energy consumption.

8. The apparatus of claim 5, wherein,

the information acquisition module is configured to acquire the use preference of the user and the operation requirement of the application through big data, determine an access network for operating the application according to the use preference of the user, and determine a frequency band of the application in the determined access network according to the operation requirement of the application.

9. An apparatus for scheduling resources, comprising:

a memory configured to store instructions;

a processor coupled to the memory, the processor configured to perform implementing the method of any of claims 1-4 based on instructions stored by the memory.

10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions which, when executed by a processor, implement the method of any one of claims 1-4.