CN111949110A - Processing method and device for minimizing energy consumption in mobile edge calculation - Google Patents

Abstract

Embodiments of the present invention provide a processing method and device for minimizing energy consumption in mobile edge computing, which can obtain a first functional relationship between total energy consumption and compression ratio, where total energy consumption represents the ability to compress original data to obtain target data energy consumption, and the energy consumption of sending the target data to the edge server; determine the compression ratio corresponding to the minimum total energy consumption in the first functional relationship as the first compression ratio; based on the first compression ratio and the first compression ratio interval, A target compression ratio is determined, and the first compression ratio interval includes all compression ratios supported by the compression of the original data; the original data is compressed according to the target compression ratio to obtain the target data. Based on the above processing, since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, based on the first compression ratio and the first compression ratio interval, a target compression ratio with a smaller total energy consumption can be determined, and further, based on the target compression ratio Compressing the original data can reduce the total energy consumption of the client.

Description

A processing method and device for minimizing energy consumption in mobile edge computing

technical field

The present invention relates to the field of computer technology, and in particular, to a processing method and device for minimizing energy consumption in mobile edge computing.

Background technique

With the development of computer technology, the functions provided by the client to users are increasingly rich. For example, the client can recommend movies to the user. The client can obtain relevant data (which may be referred to as raw data) of the movies that the user has watched, and process the raw data to determine the movie that the user is interested in, and then the client can recommend the determined movie to the user.

However, due to the limitation of hardware configuration, when there is a lot of raw data, the client may not be able to complete the processing of the raw data. Therefore, the client can compress the raw data based on the preset compression ratio to obtain the target data and send it to the edge server. target data. Correspondingly, the edge server can decompress the target data, obtain the original data, and process the original data, and then the edge server can send the processing result to the client. The client compresses the original data and sends the target data to the edge server, which consumes the client's energy (for example, electricity).

In the prior art, the client can select a compression ratio (which may be referred to as a target compression ratio) from a plurality of preset compression ratios, and compress the original data according to the target compression ratio to obtain the target data and send it to the edge server. target data. However, the energy consumed to compress the original data with different compression ratios is different, and the energy consumed to send the target data obtained based on the different compression ratios to the edge server is also different. Randomly selecting a compression ratio among the compression ratios and compressing the original data according to the compression ratio may result in high energy consumption for compressing the original data.

SUMMARY OF THE INVENTION

The purpose of the embodiments of the present invention is to provide a processing method and apparatus for minimizing energy consumption in mobile edge computing, which can reduce the total energy consumption of the client. The specific technical solutions are as follows:

In the first aspect, in order to achieve the above object, an embodiment of the present invention provides a processing method for minimizing energy consumption in mobile edge computing, and the method includes:

Obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the sum of the energy consumption of sending the target data to the edge server, so The compression ratio represents the ratio of the size of the target data to the size of the original data;

determining the compression ratio corresponding to the smallest total energy consumption in the first functional relationship as the first compression ratio;

determining a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data;

The original data is compressed according to the target compression ratio to obtain the target data.

Optionally, after determining the compression ratio corresponding to the minimum total energy consumption in the first functional relationship as the first compression ratio, the method further includes:

obtaining a second functional relationship between the total processing duration and the compression ratio, wherein the total processing duration represents the total duration from compressing the original data to the completion of processing the original data by the edge server;

Based on the second functional relationship, each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold is determined to obtain a second compression ratio interval;

The determining a target compression ratio based on the first compression ratio and the first compression ratio interval includes:

determining the intersection of the first compression ratio interval and the second compression ratio interval as a third compression ratio interval;

determining whether the first compression ratio belongs to the third compression ratio interval;

If the first compression ratio belongs to the third compression ratio interval, determining the first compression ratio as a target compression ratio;

If the first compression ratio does not belong to the third compression ratio interval, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding compression ratio with lower energy consumption as the target compression ratio.

Optionally, the second functional relationship is expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, and B represents the target channel used by the client to send the target data to the edge server. Maximum bandwidth, σ ² represents the noise power of the target channel, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the central processing unit CPU of the client, F ₂ represents The frequency of the CPU of the edge server, β ₂ represents the second preset coefficient.

Optionally, the first functional relationship is expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, and B represents the target channel used by the client to send the target data to the edge server The maximum bandwidth of , σ ² represents the noise power of the target channel, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running one cycle when the original data is compressed, β ₁ represents the first preset coefficient.

Optionally, determining the target compression ratio based on the first compression ratio and the first compression ratio interval includes:

determining whether the first compression ratio belongs to the first compression ratio interval;

If the first compression ratio belongs to the first compression ratio interval, the first compression ratio is determined as a target compression ratio.

In a second aspect, in order to achieve the above object, an embodiment of the present invention provides a processing device for minimizing energy consumption in mobile edge computing, the device comprising:

an obtaining module, configured to obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the energy consumption of sending the target data to the edge server and the compression ratio represents the ratio of the size of the target data to the size of the original data;

a first determining module, configured to determine a compression ratio corresponding to the smallest total energy consumption in the first functional relationship, as a first compression ratio;

a second determining module, configured to determine a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data;

A compression module, configured to compress the original data according to the target compression ratio to obtain the target data.

Optionally, the device further includes:

A processing module, configured to obtain a second functional relationship between the total processing duration and the compression ratio, where the total processing duration represents the total duration from compressing the original data to the completion of processing the original data by the edge server ;

Based on the second functional relationship, each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold is determined to obtain a second compression ratio interval;

The second determination module is specifically configured to determine the intersection of the first compression ratio interval and the second compression ratio interval as a third compression ratio interval;

determining whether the first compression ratio belongs to the third compression ratio interval;

If the first compression ratio belongs to the third compression ratio interval, determining the first compression ratio as a target compression ratio;

If the first compression ratio does not belong to the third compression ratio interval, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding compression ratio with lower energy consumption as the target compression ratio.

Optionally, the second functional relationship is expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, and B represents the target channel used by the client to send the target data to the edge server. Maximum bandwidth, σ ² represents the noise power of the target channel, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the central processing unit CPU of the client, F ₂ represents The frequency of the CPU of the edge server, β ₂ represents the second preset coefficient.

Optionally, the first functional relationship is expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, and B represents the target channel used by the client to send the target data to the edge server The maximum bandwidth of , σ ² represents the noise power of the target channel, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running one cycle when the original data is compressed, β ₁ represents the first preset coefficient.

Optionally, a second determination module, specifically configured to determine whether the first compression ratio belongs to the first compression ratio interval;

If the first compression ratio belongs to the first compression ratio interval, the first compression ratio is determined as a target compression ratio.

An embodiment of the present invention further provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus;

memory for storing computer programs;

The processor is configured to implement the steps of any of the foregoing processing methods for minimizing energy consumption in mobile edge computing when executing the program stored in the memory.

Embodiments of the present invention further provide a computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the energy consumption in any of the above-mentioned mobile edge computing is minimized. processing method.

Embodiments of the present invention also provide a computer program product including instructions, which, when running on a computer, enables the computer to execute any of the above-described processing methods for minimizing energy consumption in mobile edge computing.

The embodiment of the present invention provides a processing method for minimizing energy consumption in mobile edge computing, which can obtain a first functional relationship between total energy consumption and a compression ratio, where the total energy consumption represents the energy consumption of compressing original data to obtain target data, The sum value of the energy consumption of sending the target data to the edge server; the compression ratio corresponding to the minimum total energy consumption in the first functional relationship is determined as the first compression ratio; the target is determined based on the first compression ratio and the first compression ratio interval Compression ratio, the first compression ratio interval includes all compression ratios supported by the compression of the original data; the original data is compressed according to the target compression ratio to obtain the target data.

Based on the above processing, since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, based on the first compression ratio and the first compression ratio interval, a target compression ratio with a smaller total energy consumption can be determined, and further, based on the target compression ratio Compressing the original data can reduce the total energy consumption of the client.

Of course, it is not necessary for any product or method of the present invention to achieve all of the advantages described above at the same time.

Description of drawings

In order to explain the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can also be obtained according to these drawings without creative efforts.

1 is a flowchart of a processing method for minimizing energy consumption in mobile edge computing provided by an embodiment of the present invention;

2 is a flowchart of a processing method for minimizing energy consumption in mobile edge computing provided by an embodiment of the present invention;

3 is a graph of a functional relationship between total energy consumption and compression ratio according to an embodiment of the present invention:

4 is a graph showing a functional relationship between a target compression ratio and the size of original data according to an embodiment of the present invention;

5 is a graph of a functional relationship between total energy consumption and the size of original data according to an embodiment of the present invention;

6 is a graph of a functional relationship between a target compression ratio and the size of original data according to an embodiment of the present invention;

7 is a graph of a functional relationship between total energy consumption and the size of original data according to an embodiment of the present invention;

FIG. 8 is a structural diagram of a mobile edge computing system according to an embodiment of the present invention;

9 is a structural diagram of a processing device for minimizing energy consumption in mobile edge computing according to an embodiment of the present invention;

FIG. 10 is a structural diagram of an electronic device according to an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Referring to FIG. 1, FIG. 1 is a flowchart of a processing method for minimizing energy consumption in mobile edge computing provided by an embodiment of the present invention, and the method can be applied to a client in an MEC (Mobile Edge Computing, mobile edge computing) system .

The method may include the following steps:

S101: Obtain a first functional relationship between the total energy consumption and the compression ratio.

Among them, the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the energy consumption of sending the target data to the edge server, and the compression ratio represents the ratio of the size of the target data to the size of the original data.

S102: Determine the compression ratio corresponding to the smallest total energy consumption in the first functional relationship as the first compression ratio.

S103: Determine a target compression ratio based on the first compression ratio and the first compression ratio interval.

The first compression ratio interval includes all compression ratios supported by compression of the original data, and the target compression ratio is the compression ratio corresponding to the smallest energy consumption among the energy consumptions corresponding to the first compression ratio interval.

S104: Compress the original data according to the target compression ratio to obtain target data.

Based on the processing method for minimizing energy consumption in mobile edge computing provided by the embodiment of the present invention, since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, it can be determined based on the first compression ratio and the first compression ratio interval. A target compression ratio with a smaller total energy consumption, and further, compressing the original data based on the target compression ratio can reduce the total energy consumption of the client.

In step S101, the total energy consumption of the client may include the energy consumption of compressing the original data, the energy consumption of sending the target data to the edge server, and the energy consumption of receiving the processing result for the original data. Since the data volume of the processing result for the original data is small, the energy consumption of the client to receive the processing result for the original data is also small and can be ignored.

For example, when the client recommends a movie to the user, the client can compress the relevant data (ie, original data) of the movie that the user has watched to obtain the target data. The client can then send the target data to the edge server. The edge server can decompress the target data to obtain the original data, and process the original data to determine the movie that the user is interested in, and then the edge server can send the identified movie identifier to the client (that is, the processing result for the original data) . The identification of the movie may be the name of the movie (for example, Movie A), and the client consumes less energy to receive the determined identification of the movie.

In an embodiment of the present invention, the functional relationship between the energy consumption E ₁ for compressing the original data and the compression ratio for compressing the original data, and the energy consumption E ₂ for sending the target data to the edge server can be determined, which is related to the compression ratio of the original data. A function of the compression ratio with which the original data is compressed. Furthermore, by calculating the sum of E ₁ and E ₂ , the first functional relationship between the total energy consumption and the compression ratio can be obtained.

In an implementation manner, the functional relationship between the energy consumption E ₁ when compressing the original data and the compression ratio for compressing the original data can be determined according to the following formulas (1) and (2).

E ₁ =P ₂ C ₁ (2)

C ₁ represents the number of CPU (Central Processing Unit, central processing unit) cycles of the client required to compress the original data, β ₁ represents the first preset coefficient, D represents the size of the original data, and δ represents the compression of the original data compression ratio. δ belongs to (0, 1]. The first preset coefficient can be set by a technician according to experience, for example, the first preset coefficient can be 80, or the first preset coefficient can also be 75, but not limited thereto.

E ₁ represents the energy consumption when compressing the original data, and P ₂ represents the energy consumption of the client's CPU running one cycle when compressing the original data.

Then, according to the following equations (3), (4), and (5), the functional relationship between the energy consumption E ₂ of the client sending the target data to the edge server and the compression ratio for compressing the original data can be determined.

E ₂ =P ₁ t ₁ (5)

R represents the transmission rate of data sent by the client to the edge server through the target channel, ω represents the bandwidth of the target channel used by the client to send the target data to the edge server, and ω belongs to [0, B], where B represents the maximum value of the target channel Bandwidth, P ₁ represents the transmit power of the client, h represents the channel gain of the target channel, σ ² represents the noise power of the target channel, t ₁ represents the time required for the target data to be transmitted from the client to the edge server, and δ represents the processing of the original data. The compression ratio of compression, D represents the size of the original data, and E ₂ represents the energy consumption of the client to send the target data to the edge server.

Furthermore, the energy consumption of compressing the original data to obtain the target data and the energy consumption of sending the target data to the edge server can be calculated according to the following formula (6) (ie, the total energy consumption).

E=E ₁ +E ₂ (6)

E represents the total energy consumption, E ₁ represents the energy consumption of the client compressing the original data to obtain the target data, and E ₂ represents the energy consumption of sending the target data to the edge server.

In an implementation manner, the channel gain h of the target channel in the above formula (3) may be set to "1". Furthermore, according to the above formula (1), formula (2), formula (3), formula (4), formula (5) and formula (6), the total energy consumption can be expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, ω represents the bandwidth of the target channel used by the client to send the target data to the edge server, and σ ² represents the noise of the target channel Power, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running for one cycle when compressing the original data, and β ₁ represents the first preset coefficient.

The partial derivative function of E with respect to ω in the above formula (7) can be expressed as:

即

因此，

可以确定算式(7)中E关于ω单调递减，即ω取值越大，对应的E越小。可见，当目标信道的带宽为最大带宽时，可以使得对应的能耗较小，因此，ω可以取目标信道的最大带宽B。The above parameters δ, D, P ₁ are all positive numbers, and

which is

therefore,

It can be determined that E in equation (7) decreases monotonically with respect to ω, that is, the larger the value of ω is, the smaller the corresponding E is. It can be seen that when the bandwidth of the target channel is the maximum bandwidth, the corresponding energy consumption can be made smaller. Therefore, ω can take the maximum bandwidth B of the target channel.

Furthermore, the first functional relationship between the total energy consumption and the compression ratio can be obtained as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, B represents the maximum bandwidth of the target channel used by the client to send the target data to the edge server, σ ² represents the Noise power, D represents the size of the original data, P ₂ represents the energy consumption of the client's CPU running for one cycle when compressing the original data, and β ₁ represents the first preset coefficient.

In step S102, the compression ratio (ie, the first compression ratio) corresponding to the smallest total energy consumption in the first functional relationship may be determined.

In an implementation manner, the partial derivative function of E with respect to δ in the above formula (9) can be expressed as:

时，可以得到

δ₀即为总能耗与压缩比的第一函数关系中最小的总能耗对应的第一压缩比。when

, you can get

δ ₀ is the first compression ratio corresponding to the smallest total energy consumption in the first functional relationship between the total energy consumption and the compression ratio.

In step S103, after the first compression ratio is determined, a target compression ratio may be determined based on the first compression ratio and the first compression ratio interval.

In an embodiment of the present invention, step S103 may include the following steps:

Step 1, determine whether the first compression ratio belongs to the first compression ratio interval, and if so, execute Step 2.

Step 2, determining the first compression ratio as the target compression ratio.

The first compression ratio interval includes all compression ratios supported by the compression of the original data. Therefore, the first compression ratio interval can be (0, 1]. When the compression ratio is 1, it means that the original data will not be compressed, and the compression ratio takes The smaller the value, the more compression the original data is compressed.

Furthermore, it can be determined whether the first compression ratio belongs to the first compression ratio interval, and if the first compression ratio belongs to the first compression ratio interval, the first compression ratio can be directly determined as the target compression ratio. Since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, the subsequent compression of the original data based on the first compression ratio can reduce the total energy consumption of the client.

In step S104, after the target compression ratio is determined, the original data may be compressed according to the target compression ratio, and the target data may be obtained. In addition, the obtained target data can also be sent to the edge server.

In an embodiment of the present invention, referring to FIG. 2, after step S102, the method may further include the following steps:

S105: Obtain a second functional relationship between the total processing time and the compression ratio.

The total processing time represents the total time from compressing the original data to the completion of processing the original data by the edge server.

S106: Based on the second functional relationship, determine each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold, and obtain a second compression ratio interval.

Correspondingly, step S103 may include the following steps:

S1031: Determine the intersection of the first compression ratio interval and the second compression ratio interval as the third compression ratio interval.

S1032: Determine whether the first compression ratio belongs to the third compression ratio interval, and if so, execute step S1033, and if not, execute step S1034.

S1033: Determine the first compression ratio as the target compression ratio.

S1034: From the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine a corresponding compression ratio with lower energy consumption as a target compression ratio.

In step S105, the total processing time may include the time required for the client to compress the original data, the time required for the target data to be transmitted from the client to the edge server, the time required for the edge server to decompress the target data, and the time required for the edge server to decompress the target data. How long it takes to process the raw data, and how long it takes for the results of the processing on the raw data to travel from the edge server to the client. Since the data volume of the processing result for the original data is small, the time required for the processing result of the original data to be transmitted from the edge server to the client is short and can be ignored.

In an embodiment of the present invention, a functional relationship between the time duration t ₂ required for compressing the original data and the compression ratio for compressing the original data can be determined. The time duration t ₁ required for the target data to be transmitted from the client to the edge server is a function of the compression ratio for compressing the original data. The time duration t ₃ required for the edge server to decompress the target data is a function of the compression ratio for compressing the original data. The time duration t ₄ required by the edge server to process the original data is a function of the compression ratio for compressing the original data. Furthermore, by calculating the sum of t ₁ , t ₂ , t ₃ and t ₄ , the second functional relationship between the total processing time and the compression ratio can be obtained.

In an implementation manner, according to the above formula (3) and the above formula (4), the functional relationship between the time length t ₁ required for the target data to be transmitted from the client to the edge server and the compression ratio for compressing the original data can be determined.

According to the following formula (11), the functional relationship between the time duration t ₂ required for compressing the original data and the compression ratio for compressing the original data is determined.

t ₂ represents the time required to compress the original data, C ₁ represents the number of CPU cycles of the client required to compress the original data, and F ₁ represents the frequency of the client's CPU.

According to the following formula (12), determine the functional relationship between the time length t ₃ required for the edge server to decompress the target data and the compression ratio for compressing the original data.

t ₃ represents the time required for the edge server to decompress the target data, C ₂ represents the number of CPU cycles of the edge server required to decompress the original data, and F ₂ represents the frequency of the CPU of the edge server. The CPU cycle of the client is the same as the CPU cycle of the edge server. Furthermore, the edge server decompresses the target data to obtain the original data, and the number of CPU cycles required for the client to compress the original data to obtain the target data is the same. Therefore, the formula (12 ) in C ₂ may be equal to the number C ₁ of the client's CPU cycles required to compress the original data.

The functional relationship between the time duration t ₄ required by the edge server to process the original data and the compression ratio for compressing the original data can also be determined according to the following equations (13) and (14).

H=β ₂ D (13)

H represents the number of CPU cycles of the edge server required by the edge server to process the raw data, β ₂ represents the second preset coefficient, and D represents the size of the raw data. t ₄ represents the time required for the edge server to process the raw data, and F ₂ represents the frequency of the edge server's CPU.

The second preset coefficient can be set by technicians based on experience. In one implementation, the second preset coefficient can be determined according to the complexity of the algorithm used by the edge server to process the original data, and the second preset coefficient can be determined when the edge server processes the original data. When the complexity of the algorithm used is relatively high, the second preset coefficient may be set to a relatively large value. When the complexity of the algorithm used by the edge server to process the raw data is low, the second preset coefficient may be set to a small value.

For example, when the algorithm used by the edge server to process the original data belongs to the first preset algorithm, the second preset coefficient may be 1000, and when the algorithm used by the edge server to process the original data belongs to the second preset algorithm, the second preset coefficient The second preset coefficient may be 500, and the complexity of the first preset algorithm is higher than that of the second preset algorithm, but is not limited thereto.

Furthermore, according to the following formula (15), the time required to compress the original data, the time required to transmit the target data from the client to the edge server, the time required for the edge server to decompress the target data, and the processing time of the edge server can be determined. The sum of the durations required for the raw data (i.e. the total processing duration).

t=t ₁ +t ₂ +t ₃ +t ₄ (15)

t represents the total processing time, t ₁ represents the time required for the target data to be transmitted from the client to the edge server, t ₂ represents the time required to compress the original data, and t ₃ represents the time required for the edge server to decompress the target data , t ₄ represents the time required for the edge server to process the raw data.

In an implementation manner, the channel gain h of the target channel in the above formula (3) may be set to "1". According to the above formula (3), formula (4), formula (11), formula (12), formula (13), formula (14) and formula (15), the total processing time can be expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, ω represents the bandwidth of the target channel used by the client to send the target data to the edge server, and σ ² represents the noise power of the target channel , P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the CPU of the client, F ₂ represents the frequency of the CPU of the edge server, and β ₂ represents the second preset coefficient.

The partial derivative function of t with respect to ω in the above formula (16) can be expressed as:

即

因此，

可以确定算式(17)中t关于ω单调递减，即ω取值越大，对应的t越小。可见，当目标信道的带宽为最大带宽时，可以使得对应的总处理时长较小，因此，ω可以取目标信道的最大带宽B。The above parameters δ, D, P ₁ are all positive numbers, and

which is

therefore,

It can be determined that t in equation (17) decreases monotonically with respect to ω, that is, the larger the value of ω, the smaller the corresponding t. It can be seen that when the bandwidth of the target channel is the maximum bandwidth, the corresponding total processing time can be made smaller. Therefore, ω can take the maximum bandwidth B of the target channel.

Furthermore, the second functional relationship between the total processing time and the compression ratio can be obtained as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, B represents the maximum bandwidth of the target channel used by the client to send the target data to the edge server, and σ ² represents the noise of the target channel power, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the CPU of the client, F ₂ represents the frequency of the CPU of the edge server, and β ₂ represents the second preset coefficient.

In step S106, after obtaining the second functional relationship between the total processing duration and the compression ratio, each compression ratio whose corresponding total processing duration is not greater than the preset duration threshold may be determined to obtain a second compression ratio interval.

Among them, the preset duration threshold can be set according to the business requirements of the client. When the client needs to obtain the processing results for the raw data in real time, the preset duration threshold can be set to a smaller value. When the client does not need to obtain the raw data in real time When the processing result of , the preset duration threshold can be set to a larger value.

For example, when the client needs to obtain the processing results of raw data in real time, the preset duration threshold can be 1 second; when the client does not need to obtain the processing results of raw data in real time, the preset duration threshold can be 30 seconds, but It is not limited to this.

In an implementation manner, the second functional relationship is the above formula (18). When the total processing duration is not greater than the preset duration threshold, the following formula (19) can be obtained.

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, B represents the maximum bandwidth of the target channel used by the client to send the target data to the edge server, and σ ² represents the noise of the target channel Power, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the CPU of the client, F ₂ represents the frequency of the CPU of the edge server, β ₂ represents the second preset coefficient, and T represents the preset coefficient. Set the duration threshold.

The above equation (19) can be expressed as the following equation (20).

aδ ² +bδ+c≤0 (20)

where, a=DF ₁ F ₂ , b=β ₂ DRF ₁ -TRF ₁ F ₂ , c=β ₁ DR(F ₁ +F ₂ ),

进而，可以得到对应的总处理时长不大于预设时长阈值的第二压缩比区间为：[δ₁，δ₂]。Then, for the above formula (20), when aδ ² +bδ+c=0, it can be calculated as:

Furthermore, it can be obtained that the corresponding second compression ratio interval in which the total processing duration is not greater than the preset duration threshold is: [δ ₁ , δ ₂ ].

Since c=β ₁ DR(F ₁ +F ₂ ), the parameters β ₁ , D, R, F ₁ , and F ₂ are not equal to 0, and F ₁ +F ₂ ≠0, therefore, c≠0, and further, δ ₁ ≠0, and δ ₂ ≠0.

Correspondingly, in step S1031, the intersection of the first compression ratio interval and the second compression ratio interval may be determined, and a third compression ratio interval may be obtained, and the third compression ratio interval may be expressed as: [δ ₃ , δ ₄ ].

The total energy consumption corresponding to the compression ratio included in the third compression ratio interval is small, and the corresponding total processing time is also small. That is to say, in the future, according to the compression ratio included in the third compression ratio interval, when compressing the original data, the total energy consumption can be reduced, and the total processing time can be reduced at the same time, that is, the total amount of the client can be reduced. Energy consumption, and can shorten the time for the client to obtain the processing result for the original data.

For steps S1032 and S1033, it can be determined whether the first compression ratio belongs to the third compression ratio interval, and if the first compression ratio belongs to the third compression ratio interval, it indicates that the total energy consumption corresponding to the first compression ratio is the third compression ratio interval The minimum total energy consumption among the corresponding total energy consumption, that is, the total energy consumption corresponding to the first compression ratio is small, and the total processing time corresponding to the first compression ratio is also small, and further, the first compression ratio can be determined as the target compression ratio. Compare.

In step S1034, if the first compression ratio does not belong to the third compression ratio interval, it indicates that the total processing duration corresponding to the first compression ratio is greater than the preset duration threshold, and further, in order to ensure that the total energy consumption and the total processing duration are both small, the Calculate the total energy consumption corresponding to the maximum compression ratio and the total energy consumption corresponding to the minimum compression ratio included in the third compression ratio interval, and then, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding The compression ratio with smaller total energy consumption is regarded as the target compression ratio, the total energy consumption corresponding to the target compression ratio is smaller, and the total processing time corresponding to the target compression ratio is also smaller.

Exemplarily, if the first compression ratio does not belong to the third compression ratio interval, the third compression ratio interval is: [δ ₃ , δ ₄ ]. Then, the total energy consumption E(δ ₃ ) corresponding to δ ₃ and the total energy consumption E(δ ₄ ) corresponding to δ ₄ can be calculated based on the above formula (9). Furthermore, when E(δ ₃ ) is less than E(δ ₄ ), δ ₃ can be determined as the target compression ratio, and when E(δ ₃ ) is greater than E(δ ₄ ), δ ₄ can be determined as the target compression ratio.

In an embodiment of the present invention, referring to FIG. 3 , FIG. 3 is a graph of a functional relationship between total energy consumption and compression ratio according to an embodiment of the present invention. In Fig. 3, the abscissa represents the compression ratio for compressing the original data, the ordinate represents the total energy consumption for compressing the original data based on the compression ratio, and the unit of the total energy consumption is J.

Figure 3 shows the energy consumption of the client CPU running one cycle when the original data size is 0.5Mb, the maximum bandwidth of the target channel is 1MHz, the transmit power of the client is 40dBm, the noise power of the target channel is -70dBm, and the original data is compressed. When it is 1×10 ^-27 J/cycle, the CPU frequency of the client is 600M cycles/s, the CPU frequency of the edge server is 1500M cycles/s, the first preset coefficient is 80, and the second preset coefficient is 1000, the total Energy consumption as a function of compression ratio.

The solid line in Figure 3 represents the total energy consumption as a function of the compression ratio. It can be seen that as the compression ratio increases, the total energy consumption first decreases and then increases, and the compression ratio corresponding to the smallest total energy consumption belongs to [0.5×10 ^-9 , 0.75×10 ^-9 ].

Referring to FIG. 4 , FIG. 4 is a graph showing a functional relationship between a target compression ratio and the size of original data according to an embodiment of the present invention. In FIG. 4 , the abscissa represents the size of the original data, the unit of the size of the original data is Mb, and the ordinate represents the target compression ratio.

Figure 4 shows the energy consumption of the client CPU running one cycle when the preset duration threshold is 0.5 seconds, the maximum bandwidth of the target channel is 1MHz, the transmit power of the client is 40dBm, the noise power of the target channel is -70dBm, and the original data is compressed. It is 1×10 ^-27 J/cycle, the frequency of the client's CPU is 600M cycles/s, the CPU frequency of the edge server is 1500M cycles/s, the first preset coefficient is 80, the second preset coefficient is 1000, the original data When the size is [0.5Mb, 1.0Mb], the target compression ratio is a function of the size of the original data.

The solid line in FIG. 4 represents the functional relationship between the target compression ratio and the size of the original data when the preset duration threshold is 0.5 seconds. It can be seen that the target compression ratio is positively related to the size of the original data. When the data volume of the original data is larger, the target compression ratio is larger.

Referring to FIG. 5, FIG. 5 is a graph of a functional relationship between total energy consumption and the size of original data according to an embodiment of the present invention. In Fig. 5, the abscissa represents the size of the original data, the unit of the size of the original data is Mb, the ordinate represents the total energy consumption, and the unit of the total energy consumption is J.

Figure 5 shows the energy consumption of the client CPU running one cycle when the preset duration threshold is 0.5 seconds, the maximum bandwidth of the target channel is 1MHz, the transmit power of the client is 40dBm, the noise power of the target channel is -70dBm, and the original data is compressed. It is 1×10 ^-27 J/cycle, the frequency of the client's CPU is 600M cycles/s, the CPU frequency of the edge server is 1500M cycles/s, the first preset coefficient is 80, the second preset coefficient is 1000, the original data When the size is [0.5Mb, 1.0Mb], the total energy consumption is a function of the size of the original data.

The solid line with a circle in FIG. 5 represents the functional relationship between the total energy consumption and the size of the original data when the original data is compressed according to the target compression ratio when the preset duration threshold is 0.5 seconds. The solid line with squares represents the functional relationship between the total energy consumption and the size of the original data when the preset duration threshold is 0.5 seconds and the original data is not compressed. It can be seen that when the size of the original data is the same, the total energy consumption of compressing the original data according to the target compression ratio is less than the total energy consumption of not compressing the original data.

Referring to FIG. 6 , FIG. 6 is a graph showing a function relationship between a target compression ratio and a size of original data according to an embodiment of the present invention. In FIG. 6 , the abscissa represents the size of the original data, the unit of the size of the original data is Mb, and the ordinate represents the target compression ratio.

Figure 6 shows that the maximum bandwidth of the target channel is 1MHz, the transmit power of the client is 40dBm, the noise power of the target channel is -70dBm, and the energy consumption of the client's CPU running one cycle when compressing the original data is 1×10 ^-27 J/ When the cycle, the frequency of the client's CPU is 600M cycles/s, the CPU frequency of the edge server is 1500M cycles/s, the first preset coefficient is 80, and the second preset coefficient is 1000, the target compression ratio is a function of the size of the original data relation.

Different preset duration thresholds can be set for original data of different sizes. When the amount of original data is large, a larger preset duration threshold can be set. When the amount of original data is small, a smaller preset duration threshold can be set. Preset duration threshold. Further, according to the size of the original data and the preset duration threshold, the corresponding target compression ratio is determined.

For example, when the size of the original data is 1.5Mb, the preset duration threshold can be 0.5 seconds. According to the size of the original data (ie 1.5Mb) and the preset duration threshold (ie 0.5 seconds), the size of the original data can be calculated as 1.5 The corresponding target compression ratio in Mb. When the size of the original data is 2.0Mb, the preset duration threshold may be 0.8 seconds. According to the size of the original data (ie 2.0Mb) and the preset duration threshold (ie 0.8 seconds), the corresponding target compression ratio when the size of the original data is 1.5Mb can be calculated, and so on.

Furthermore, the graph of the functional relationship between the target compression ratio and the size of the original data shown in FIG. 6 can be obtained. The solid line in Figure 6 represents the target compression ratio as a function of the size of the original data. It can be seen that with the increase of the data volume of the original data, the target compression ratio gradually tends to a stable state.

Referring to FIG. 7 , FIG. 7 is a graph showing a functional relationship between total energy consumption and the size of original data according to an embodiment of the present invention. In FIG. 7 , the abscissa represents the size of the original data, the unit of the size of the original data is Mb, the ordinate represents the total energy consumption, and the unit of the total energy consumption is J.

Figure 7 shows that the maximum bandwidth of the target channel is 1MHz, the transmit power of the client is 40dBm, the noise power of the target channel is -70dBm, and the energy consumption of the client's CPU running one cycle when compressing the original data is 1×10 ^-27 J/ cycle, the frequency of the client's CPU is 600M cycles/s, the CPU frequency of the edge server is 1500M cycles/s, the first preset coefficient is 80, and the second preset coefficient is 1000, the total energy consumption and the size of the original data Functional relationship.

Different preset duration thresholds can be set for original data of different sizes. When the amount of original data is large, a larger preset duration threshold can be set. When the amount of original data is small, a smaller preset duration threshold can be set. Preset duration threshold. Further, a corresponding target compression ratio is determined according to the size of the original data and a preset duration threshold, and the total energy consumption is calculated according to the target compression ratio.

For example, when the size of the original data is 1.5Mb, the preset duration threshold can be 0.5 seconds. According to the size of the original data (ie 1.5Mb) and the preset duration threshold (ie 0.5 seconds), the size of the original data can be calculated as 1.5 When the target compression ratio is Mb, the corresponding total energy consumption is calculated according to the target compression ratio. When the size of the original data is 2.0Mb, the preset duration threshold may be 0.8 seconds. According to the size of the original data (ie 2.0Mb) and the preset duration threshold (ie 0.8 seconds), the corresponding target compression ratio when the size of the original data is 1.5Mb can be calculated, and then the corresponding total energy consumption can be calculated according to the target compression ratio , and so on.

Furthermore, the graph of the functional relationship between the total energy consumption and the size of the original data shown in FIG. 7 can be obtained. The solid line with squares in FIG. 7 indicates that when the original data is compressed according to the target compression ratio, the total energy consumption and the original data function of the size. The solid line with circles represents the total energy consumption as a function of the size of the original data when the original data is not compressed.

It can be seen that when the size of the original data is the same, the total energy consumption of compressing the original data according to the target compression ratio is less than the total energy consumption of not compressing the original data. And the larger the data volume of the original data, the larger the difference between the total energy consumption of not compressing the original data and the total energy consumption of compressing the original data.

Referring to FIG. 8 , FIG. 8 is a structural diagram of a mobile edge computing system according to an embodiment of the present invention. The mobile edge computing system may include: a central server, an edge server, and a client.

Based on the method provided by the embodiment of the present application, the client can determine a target compression ratio that makes both the total energy consumption and the total processing time smaller, and then compress the original data according to the target compression ratio to obtain the target data, and send it to the edge server. target data.

Correspondingly, after receiving the target data, the edge server can decompress the target data to obtain the original data. Then, the edge server can process the original data to obtain the processing result for the original data, and then the edge server can send the data to the client. The result of processing the raw data.

The central server can monitor the working status of the edge server (for example, whether the edge server is currently processing raw data), the computing pressure when the edge server processes the raw data, and the effectiveness of the algorithm used by the edge server to process the raw data.

In the mobile edge computing system, the client sends the raw data to the edge server for processing. Due to the small physical distance between the edge server and the client, compared with cloud computing, the client directly sends the raw data to the central server for processing. The large physical distance between the central server and the client leads to a longer time for the client to obtain the processing result for the original data. Mobile edge computing can shorten the time required for the client to obtain the processing result for the original data.

Corresponding to the method embodiment of FIG. 1 , see FIG. 9 . FIG. 9 is a structural diagram of a processing device for minimizing energy consumption in mobile edge computing according to an embodiment of the present invention, where the device includes:

The obtaining module 901 is configured to obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the energy consumption of sending the target data to the edge server. The sum value of the consumption, the compression ratio represents the ratio of the size of the target data to the size of the original data;

a first determining module 902, configured to determine the compression ratio corresponding to the smallest total energy consumption in the first functional relationship, as the first compression ratio;

A second determining module 903, configured to determine a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data;

The compression module 904 is configured to compress the original data according to the target compression ratio to obtain the target data.

Optionally, the device further includes:

A processing module, configured to obtain a second functional relationship between the total processing duration and the compression ratio, where the total processing duration represents the total duration from compressing the original data to the completion of processing the original data by the edge server ;

Based on the second functional relationship, each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold is determined to obtain a second compression ratio interval;

The second determination module 903 is specifically configured to determine the intersection of the first compression ratio interval and the second compression ratio interval as a third compression ratio interval;

determining whether the first compression ratio belongs to the third compression ratio interval;

If the first compression ratio belongs to the third compression ratio interval, determining the first compression ratio as a target compression ratio;

If the first compression ratio does not belong to the third compression ratio interval, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding compression ratio with lower energy consumption as the target compression ratio.

Optionally, the second functional relationship is expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, and B represents the target channel used by the client to send the target data to the edge server. Maximum bandwidth, σ ² represents the noise power of the target channel, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the central processing unit CPU of the client, F ₂ represents The frequency of the CPU of the edge server, β ₂ represents the second preset coefficient.

Optionally, the first functional relationship is expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, and B represents the target channel used by the client to send the target data to the edge server The maximum bandwidth of , σ ² represents the noise power of the target channel, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running one cycle when the original data is compressed, β ₁ represents the first preset coefficient.

Optionally, the second determination module 903 is specifically configured to determine whether the first compression ratio belongs to the first compression ratio interval;

If the first compression ratio belongs to the first compression ratio interval, the first compression ratio is determined as a target compression ratio.

Based on the processing device for minimizing energy consumption in mobile edge computing provided by the embodiment of the present invention, since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, based on the first compression ratio and the first compression ratio interval, it is possible to determine A target compression ratio with a smaller total energy consumption, and further, compressing the original data based on the target compression ratio can reduce the total energy consumption of the client.

An embodiment of the present invention further provides an electronic device, as shown in FIG. 10 , including a processor 1001 , a communication interface 1002 , a memory 1003 and a communication bus 1004 , wherein the processor 1001 , the communication interface 1002 , and the memory 1003 pass through the communication bus 1004 complete communication with each other,

a memory 1003 for storing computer programs;

When the processor 1001 is used to execute the program stored in the memory 1003, the following steps are implemented:

Obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the sum of the energy consumption of sending the target data to the edge server, so The compression ratio represents the ratio of the size of the target data to the size of the original data;

determining the compression ratio corresponding to the smallest total energy consumption in the first functional relationship as the first compression ratio;

determining a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data;

The original data is compressed according to the target compression ratio to obtain the target data.

The communication bus mentioned in the above electronic device may be a peripheral component interconnect standard (Peripheral Component Interconnect, PCI) bus or an Extended Industry Standard Architecture (Extended Industry Standard Architecture, EISA) bus or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

The communication interface is used for communication between the above electronic device and other devices.

The memory may include random access memory (Random Access Memory, RAM), and may also include non-volatile memory (Non-Volatile Memory, NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

The above-mentioned processor may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; may also be a digital signal processor (Digital Signal Processing, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

Based on the electronic device provided by the embodiment of the present invention, since the first compression ratio is the compression ratio corresponding to the minimum total energy consumption, based on the first compression ratio and the first compression ratio interval, a target compression ratio with a smaller total energy consumption can be determined , and further, compressing the original data based on the target compression ratio can reduce the total energy consumption of the client.

In yet another embodiment provided by the present invention, a computer-readable storage medium is also provided, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the above mobile edge computing is implemented The steps of the treatment method to minimize energy consumption in the medium.

In yet another embodiment provided by the present invention, there is also provided a computer program product containing instructions, when running on a computer, the computer enables the computer to perform the process of minimizing energy consumption in mobile edge computing in any of the above embodiments method.

In the above-mentioned embodiments, it may be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented in software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer may be a general purpose computer, special purpose computer, computer network, or other programmable device. The computer instructions may be stored in or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be downloaded from a website site, computer, server or data center Transmission to another website site, computer, server, or data center is by wire (eg, coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (eg, infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that includes an integration of one or more available media. The usable media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVD), or semiconductor media (eg, Solid State Disk (SSD)), among others.

It should be noted that, in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any relationship between these entities or operations. any such actual relationship or sequence exists. Moreover, the terms "comprising", "comprising" or any other variation thereof are intended to encompass a non-exclusive inclusion such that a process, method, article or device that includes a list of elements includes not only those elements, but also includes not explicitly listed or other elements inherent to such a process, method, article or apparatus. Without further limitation, an element qualified by the phrase "comprising a..." does not preclude the presence of additional identical elements in a process, method, article or apparatus that includes the element.

Each embodiment in this specification is described in a related manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. Especially, for the apparatus, electronic device, computer-readable storage medium, and computer program product embodiments, since they are basically similar to the method embodiments, the description is relatively simple.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims (10)

1. A processing method for minimizing energy consumption in mobile edge computing, wherein the method comprises: Obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the sum of the energy consumption of sending the target data to the edge server, so The compression ratio represents the ratio of the size of the target data to the size of the original data; determining the compression ratio corresponding to the smallest total energy consumption in the first functional relationship as the first compression ratio; determining a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data; The original data is compressed according to the target compression ratio to obtain the target data. 2 . The method according to claim 1 , wherein after determining the compression ratio corresponding to the minimum total energy consumption in the first functional relationship as the first compression ratio, the method further comprises: 2 . obtaining a second functional relationship between the total processing duration and the compression ratio, wherein the total processing duration represents the total duration from compressing the original data to the completion of processing the original data by the edge server; Based on the second functional relationship, each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold is determined to obtain a second compression ratio interval; The determining a target compression ratio based on the first compression ratio and the first compression ratio interval includes: determining the intersection of the first compression ratio interval and the second compression ratio interval as a third compression ratio interval; determining whether the first compression ratio belongs to the third compression ratio interval; If the first compression ratio belongs to the third compression ratio interval, determining the first compression ratio as a target compression ratio; If the first compression ratio does not belong to the third compression ratio interval, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding compression ratio with lower energy consumption as the target compression ratio. 3. The method according to claim 2, wherein the second functional relationship is expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, and B represents the target channel used by the client to send the target data to the edge server. Maximum bandwidth, σ ² represents the noise power of the target channel, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the central processing unit CPU of the client, F ₂ represents The frequency of the CPU of the edge server, β ₂ represents the second preset coefficient. 4. The method according to claim 1, wherein the first functional relationship is expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, and B represents the target channel used by the client to send the target data to the edge server The maximum bandwidth of , σ ² represents the noise power of the target channel, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running one cycle when the original data is compressed, β ₁ represents the first preset coefficient. 5. The method according to claim 1, wherein the determining a target compression ratio based on the first compression ratio and the first compression ratio interval comprises: determining whether the first compression ratio belongs to the first compression ratio interval; If the first compression ratio belongs to the first compression ratio interval, the first compression ratio is determined as a target compression ratio. 6. A processing device for minimizing energy consumption in mobile edge computing, wherein the device comprises: an obtaining module, configured to obtain the first functional relationship between the total energy consumption and the compression ratio, wherein the total energy consumption represents the energy consumption of compressing the original data to obtain the target data, and the energy consumption of sending the target data to the edge server and the compression ratio represents the ratio of the size of the target data to the size of the original data; a first determining module, configured to determine a compression ratio corresponding to the smallest total energy consumption in the first functional relationship, as a first compression ratio; a second determining module, configured to determine a target compression ratio based on the first compression ratio and a first compression ratio interval, wherein the first compression ratio interval includes all compression ratios supported by compression of the original data; A compression module, configured to compress the original data according to the target compression ratio to obtain the target data. 7. The apparatus of claim 6, wherein the apparatus further comprises: A processing module, configured to obtain a second functional relationship between the total processing duration and the compression ratio, where the total processing duration represents the total duration from compressing the original data to the completion of processing the original data by the edge server ; Based on the second functional relationship, each compression ratio whose corresponding total processing duration is not greater than a preset duration threshold is determined to obtain a second compression ratio interval; The second determination module is specifically configured to determine the intersection of the first compression ratio interval and the second compression ratio interval as a third compression ratio interval; determining whether the first compression ratio belongs to the third compression ratio interval; If the first compression ratio belongs to the third compression ratio interval, determining the first compression ratio as a target compression ratio; If the first compression ratio does not belong to the third compression ratio interval, from the maximum compression ratio and the minimum compression ratio included in the third compression ratio interval, determine the corresponding compression ratio with lower energy consumption as the target compression ratio. 8. The device according to claim 7, wherein the second functional relationship is expressed as:

t represents the total processing time, δ represents the compression ratio for compressing the original data, D represents the size of the original data, and B represents the target channel used by the client to send the target data to the edge server. Maximum bandwidth, σ ² represents the noise power of the target channel, P ₁ represents the transmit power of the client, β ₁ represents the first preset coefficient, F ₁ represents the frequency of the central processing unit CPU of the client, F ₂ represents The frequency of the CPU of the edge server, β ₂ represents the second preset coefficient. 9. The device according to claim 6, wherein the first functional relationship is expressed as:

E represents the total energy consumption, δ represents the compression ratio for compressing the original data, P ₁ represents the transmit power of the client, and B represents the target channel used by the client to send the target data to the edge server The maximum bandwidth of , σ ² represents the noise power of the target channel, D represents the size of the original data, P ₂ represents the energy consumption of the client CPU running one cycle when the original data is compressed, β ₁ represents the first preset coefficient. 10. An electronic device, comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; memory for storing computer programs; The processor is configured to implement the method steps described in any one of claims 1-5 when executing the program stored in the memory.

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