CN111511028B - Multi-user resource allocation method, device, system and storage medium - Google Patents
Multi-user resource allocation method, device, system and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-user resource allocation method, a device, a system and a storage medium, wherein the method comprises the following steps: determining the task amount of the user equipment and the task amount of the edge server according to the tasks of the user equipment and the edge server; calculating to obtain average total power consumption according to the user equipment and the edge server; calculating according to the user equipment task quantity, the edge server task quantity and the base station cooperation proportion to obtain average execution time delay; and calculating to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay. The invention discloses a method, a device, a system and a storage medium for multi-user resource allocation based on 5G D-RAN. The algorithm realizes the balance of task power consumption and execution time delay under the constraints of the stability of a task buffer area and the like.
Description
Technical Field
The invention relates to the technical field of 5G distributed wireless Access networks, in particular to a distributed-Radio Access Network (RDRAN) multi-user resource allocation method, device, system and storage medium.
Background
In the past years, smart phones are penetrating people's daily life, and meanwhile, with the continuous development of technologies, computing-intensive applications such as virtual reality, interactive online games and the like are increased sharply, and current surveys show that in the next few years, mobile data traffic will show explosive growth, which will also bring infinite pressure to mobile network operators. Furthermore, the concept of moving edge calculation is introduced because the quality of information calculation is currently limited by processing power, memory and battery capacity. As a promising new technology, the edge server can allocate computing resources in the wireless access node accordingly, thereby reducing the lengthy delay and power consumption for data exchange from the mobile device to the cloud.
In edge computing, in addition to resource allocation issues in edge nodes, another challenge is the reliability of data transmission between mobile devices and edge nodes. However, in the existing resource allocation method based on edge computing, a task is first unloaded to a micro base station, then written to a macro base station, and finally reaches an edge server for processing, so that the time delay required by task unloading is increased, and therefore, the task processing capability is poor.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method, an apparatus, a system, and a storage medium for multi-user resource allocation, so as to solve the technical problem that the task processing capability of the resource allocation method in the prior art is poor.
The technical scheme provided by the invention is as follows:
the first aspect of the embodiments of the present invention provides a method for allocating multi-user resources based on a 5G D-RAN, where the allocation method is based on a 5G D-RAN technology, and includes the following steps: determining the task quantity of the user equipment and the task quantity of the edge server according to the tasks of the user equipment and the edge server; calculating to obtain average total power consumption according to the user equipment and the edge server, and calculating to obtain average execution time delay according to the user equipment task quantity, the edge server task quantity and the base station cooperation proportion; and calculating to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay.
Optionally, the obtaining of the channel allocation and the computing resource allocation with the minimum energy consumption according to the average total power consumption and the average execution delay includes: determining a limiting condition of resource allocation according to the CPU frequency, the channel, the user equipment task quantity and the edge server task quantity of the user equipment and the edge server; determining an optimization problem of resource allocation according to the average total power consumption; and calculating the optimization problem according to the limiting conditions to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum.
Optionally, calculating the optimization problem according to the limiting condition to obtain channel allocation when energy consumption of the user equipment and the edge server is minimum, including: calculating according to the Lyapunov function to obtain the total amount of tasks of the user equipment and the edge server; calculating the optimization problem according to the total task amount, the average total power consumption and the Lyapunov drift penalty function to obtain an optimization equation; and calculating the optimization equation according to the limiting conditions and the Lyapunov algorithm to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum.
Optionally, calculating the optimization equation according to the limiting condition and the lyapunov algorithm to obtain channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum, including: determining dynamic expressions of CPU frequencies and channel transmission energy consumption of the user equipment and the edge server according to Gaussian Seld iteration; calculating the dynamic expression according to a Lagrange multiplier method and a binary search algorithm to obtain a channel allocation and calculation resource allocation dynamic expression; and determining the channel allocation and the computing resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the dynamic expression of the channel allocation and the computing resource allocation.
Optionally, the user equipment task amount includes: the task amount of the task buffer area and the task amount processed by the user equipment, wherein the task amount of the edge server comprises the task amount unloaded to the edge server and the task amount received from other edge servers.
A second aspect of the embodiments of the present invention provides a multi-user resource allocation apparatus, where the allocation apparatus is based on a 5G D-RAN technology, and the resource allocation apparatus includes: a task amount determination module: the method comprises the steps of determining the task quantity of the user equipment and the task quantity of the edge server according to the tasks of the user equipment and the edge server; a power consumption calculation module: the method comprises the steps of calculating to obtain average total power consumption according to user equipment and an edge server; the time delay calculation module is used for calculating and obtaining average execution time delay according to the user equipment task quantity, the edge server task quantity and the base station cooperation proportion; and the resource allocation module is used for calculating to obtain the minimum time channel allocation and the calculation resource allocation of the energy consumption user equipment and the edge server according to the average total power consumption and the average execution time delay.
A third aspect of the embodiments of the present invention provides a multi-user resource allocation system, including: a plurality of user equipment; a plurality of edge servers; a microprocessor, configured to allocate the signals of the user equipment and the edge server according to the multi-user resource allocation method in any one of the first aspect and the first aspect of the embodiments of the present invention.
A fourth aspect of the embodiments of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to cause the computer to execute the method for allocating multi-user resources according to the first aspect and any one of the first aspect of the embodiments of the present invention.
A fifth aspect of the embodiments of the present invention provides a multi-user resource allocation terminal, including: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the multi-user resource allocation method according to the first aspect and any one of the first aspect of the embodiments of the present invention.
The technical scheme provided by the invention has the following effects:
the 5G D-RAN-based multi-user resource allocation method, the device, the system and the storage medium provided by the embodiment of the invention introduce a 5G D-RAN model on the basis of an original single-user multi-edge server model, and expand the model into a multi-user multi-edge server scene. In the 5G D-RAN, the base station has the cooperative characteristic, so that the edge server can receive data from the mobile equipment and also can receive data from other edge servers, and reliable transmission of information is effectively guaranteed.
The 5G D-RAN-based multi-user resource allocation method provided by the embodiment of the invention utilizes the Lyapunov optimization algorithm to determine the resource allocation strategy in the system, the allocation strategy is jointly determined by corresponding energy consumption and time delay, and in the decision process of the optimal solution, the energy consumption and the time delay need to achieve relative balance. Meanwhile, in the process of calculating the optimal solution, gauss Seidel iteration is used to obtain the optimal local CPU frequency and the optimal edge server frequency, and then the Lagrange multiplier method is used to realize the optimal distribution of channel resources and transmission energy consumption.
The 5G D-RAN multi-user resource allocation method provided by the embodiment of the invention is based on a Leiponov optimized joint bandwidth and a calculation resource allocation algorithm, and takes the weighted energy consumption and the calculation time delay of the system as the evaluation indexes of the system performance. The algorithm realizes the balance of task power consumption and execution time delay under the constraints of the stability of a task buffer area and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of an application scenario of a 5G D-RAN multi-user resource allocation method according to an embodiment of the present invention;
fig. 2 is a flow chart of a 5G D-RAN based multi-user resource allocation method according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating simulation results of a 5G D-RAN-based multi-user resource allocation method according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating another simulation result of a 5G D-RAN-based multi-user resource allocation method according to an embodiment of the present invention;
fig. 5 is a block diagram of a 5G D-RAN-based multi-user resource allocation apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a hardware structure of a 5G D-RAN-based multi-user resource allocation terminal according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic view of an application scenario according to an embodiment of the present invention. In the edge network corresponding to the 5G D-RAN-based multi-user resource allocation method provided by the embodiment of the present invention, N user equipments are processing compute-intensive tasks, and the tasks may be partially offloaded to M edge servers for processing, and these edge servers are regarded as small data centers installed in a wireless access point. In the 5G D-RAN, data exchange between base stations is possible, and now the edge server can receive not only the data offloaded from the mobile device but also the cooperation data from the base stations, in this way, the user equipment can have more computational resources and radio resources.
An embodiment of the present invention provides a method for allocating multi-user resources based on a 5G D-RAN, as shown in fig. 2, the method for allocating resources includes the following steps:
step S101: and determining the task quantity of the user equipment and the task quantity of the edge server according to the tasks of the user equipment and the edge server. Where the user device may be a mobile device, the mobile device may be local.
In particular, in the resource allocation method, a task queuing model can be adopted to represent the amount of tasks to be processed in the system. Suppose that the ue is receiving independent and fine-grained task, and when the t-th timeslot starts, it is assumed that the task arrives randomly, and there is a i The (t) bits arrive at the ith user equipment. Where all tasks can be split into two parts, the first part being tasks that arrive but have not yet been processed, which will be queued in a task buffer, while the task buffer is assumed to have infinite capacity, and the second part being tasks that are processed by the user equipment and the edge server.
The queue length at the local next time can be expressed by formula (1):
Q i (t+1)＝max{Q i (t)-D l,i (t)-D r,i (t),0}+A i (t) formula (1)
Wherein D is l,i (t) represents the local processing task amount, D r,i (t) represents the amount of tasks offloaded to the edge server, Q i (t) represents the queue length at local time t.
The cooperative queue length at the next time of the edge server can be expressed by formula (2):
T i,j,cop (t+1)＝max{T i,j,incop (t)-D s,i,j (t),0}+min{max{Q i (t)-D l,i (t),D r,i,j (t)}}+G j (t) formula (2)
Wherein, G j (t) represents the amount of tasks received from other servers.
The length of the uncooperative queue at the next time of the edge server can be expressed by formula (3):
T i,j,incop (t+1)
＝max{T i,j,incop (t)-D s,i,j (t),0}
+min{max{Q i (t)-D l,i (t),D r,i,j (t) } formula (3)
Step S102: calculating to obtain average total power consumption according to the user equipment and the edge server; in order to simplify the optimization problem during resource allocation, the embodiment of the invention researches task processing and energy consumption unloading, and ignores the energy required by maintaining system operation. The average total power consumption of the system is used as one of the indexes for evaluating the performance of the system, and is represented by formula (4):
where T denotes the number of time slots, E denotes the average value, P loc,i,cop (t) denotes local cooperative energy consumption, P loc,incop,i (t) denotes local uncooperative energy consumption, P ser,cop,j (t) represents Server cooperative energy consumption, P ser,incop,j (t) represents server uncooperative energy consumption.
Step S103: calculating according to the user equipment task quantity, the edge server task quantity and the base station cooperation proportion to obtain average execution time delay; specifically, according to Little theory, the average execution delay is represented by the average task amount waiting in the task buffer of the user equipment and the MEC server, so the average sum of the queue lengths is regarded as a performance index for measuring the execution delay, and therefore, the average execution delay can be represented by formula (5), where μ is the cooperation ratio of the base station.
Step S104: and calculating according to the average total power consumption and the average execution time delay to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum. Specifically, on the basis of determining the average execution delay, the power consumption and resource allocation of the multi-user device and the multi-edge server may be considered jointly, and determined as an optimization problem, that is, the optimization problem of resource allocation is to minimize the energy consumption of the system.
When solving the optimization problem, the resource allocation limiting conditions can be determined according to the CPU frequency, the channel, the user equipment task amount, and the edge server task amount of the user equipment and the edge server, that is, the optimization problem is solved under the constraint of the equipment performance (the constraint of the CPU frequency) and the stability constraint of the task buffer. Specifically, the corresponding restriction conditions may be expressed by the following formulas (6) to (12).
0 ≤ μ ≤ 1 formula (6)
∑ i∈N α i,j (t)≤1,α i,j (t) ≥ 0 formula (7)
0≤f i (t)≤f i,max Formula (8)
0≤f ser,j (t)≤f ser,max Formula (9)
0≤p tx,i,j (t)≤p tx,max Formula (10)
Wherein, the formula (7) represents the proportion of the channels allocated to i users by j base stations in the total channels, and the proportion is greater than or equal to 0. Meanwhile, for the j base station, the sum of the occupied channel ratios of all the users should be less than 1. Equation (8), equation (9) and equation (10) represent the limit of the local CPU frequency, the limit of the edge server CPU and the limit of the transmission power consumption, respectively. Equations (11) and (12) represent stability constraints of the task buffer, indicating that all arriving computational tasks can be completed within a finite time delay.
In one embodiment, a Lyapunov optimization algorithm may be employed in solving the optimization problem. The lyapunov optimization algorithm can be used for controlling a dynamic system, and the task quantity of the user equipment and the task quantity of the edge server are also dynamically changed in the embodiment of the invention, so the lyapunov algorithm is used for the dynamically changed task to represent the total queue backlog quantity in the network. Specifically, it can be expressed by formula (13).
Evolving from the basic quadratic Lyapunov function, the optimization problem can be represented in the form as follows.
Equation (14) may be expressed as a lyapunov drift penalty function for minimizing the penalty function while stabilizing the network. Where V is a non-negative weight, the variation of V may affect the tradeoff of energy consumption and latency.
Thus, the above optimization problem can be transformed according to the lyapunov optimization algorithm into an upper bound minimization problem that optimizes equation formula (14), which is jointly constrained by the frequency of the CPU and the channel allocation, while there is a coupling between the parameters, so to decouple the problem, the optimization problem can be decomposed into sub-problems. During solving, determining a dynamic expression of the CPU frequency and the channel transmission energy consumption of the user equipment and the edge server according to Gaussian iterative; then, calculating the dynamic expression according to a Lagrange multiplier method and a binary search algorithm to obtain a channel allocation dynamic expression; and determining the channel allocation when the energy consumption is minimized according to the dynamic expression of the channel allocation.
The 5G D-RAN-based multi-user resource allocation method provided by the embodiment of the invention introduces a 5G D-RAN model on the basis of the original single-user multi-edge server model, and expands the model into a multi-user multi-edge server scene. In the 5G D-RAN, the base station has the cooperative characteristic, so that the edge server can receive data from the mobile equipment and also can receive data from other edge servers, and reliable transmission of information is effectively guaranteed.
The 5G D-RAN based multi-user resource allocation method provided by the embodiment of the invention utilizes a Lyapunov optimization algorithm to determine a resource allocation strategy in the system, the allocation strategy is determined by corresponding energy consumption and time delay together, and in the decision process of an optimal solution, the energy consumption and the time delay need to achieve relative balance. Meanwhile, in the process of calculating the optimal solution, gauss Seidel iteration is used to obtain the optimal local CPU frequency and the optimal edge server frequency, and then the Lagrange multiplier method is used to realize the optimal distribution of channel resources and transmission energy consumption.
The 5G D-RAN multi-user resource allocation method provided by the embodiment of the invention is based on the joint bandwidth and the calculation resource allocation algorithm optimized by Lyapunov, and the weighted energy consumption and the calculation delay of the system are used as the evaluation indexes of the system performance. The algorithm realizes the balance of task power consumption and execution time delay under the constraints of the stability of a task buffer area and the like.
In one embodiment, the lyapunov optimization algorithm may be represented using the following code.
In an embodiment, existing software, such as MATLAB and the like, may be used to perform a simulation experiment on the 5G D-RAN-based multi-user resource allocation method provided in the embodiment of the present invention, and specific experiment parameters may be represented by the following table.
TABLE 1
By performing simulation experiments on the 5G D-RAN-based multi-user resource allocation method provided by the embodiment of the present invention, results shown in fig. 3 and fig. 4 can be obtained.
Fig. 3 shows the average queue length and the average energy consumption of each device in relation to V under different channel resource allocations. As can be seen from fig. 3, the optimized channel allocation obtained by the lyapunov optimization algorithm has lower energy consumption and time delay than the uniform channel resource allocation, and V can also be used as a control parameter to control the energy consumption and time delay of the system. For example, in a delay-sensitive system, a smaller control parameter may be used, and in an energy-sensitive system, a larger control parameter may be used, thereby finally achieving a balance between energy consumption and delay.
Fig. 4 shows the average energy consumption and the average queue length of each device with respect to V for different degrees of cooperation of the base stations. As can be seen from fig. 4, compared with the conventional non-cooperative non-authoring method, the cooperative base station can have lower energy consumption and lower time delay, and as the degree of cooperation increases, the processing capability of the edge server also increases, resulting in a decrease in the average queue length. Therefore, the 5G D-RAN-based multi-user resource allocation method provided by the embodiment of the invention obviously improves the processing capacity of the system.
An embodiment of the present invention further provides a multi-user resource allocation apparatus based on a 5G D-RAN, and as shown in fig. 5, the resource allocation apparatus includes:
task amount determination module 1: the method comprises the steps of determining the task quantity of the user equipment and the task quantity of the edge server according to the tasks of the user equipment and the edge server; for details, refer to the related description of step S101 in the above method embodiment.
The power consumption calculation module 2: the method comprises the steps of calculating to obtain average total power consumption according to user equipment and an edge server; for details, refer to the related description of step S102 in the above method embodiment.
The time delay calculation module 3 is used for calculating and obtaining the average execution time delay according to the user equipment task amount, the edge server task amount and the base station cooperation proportion; for details, refer to the related description of step S103 in the above method embodiment.
And the resource allocation module 4 is used for calculating and obtaining the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay. For details, refer to the related description of step S104 in the above method embodiment.
The detailed description of the functions of the 5G D-RAN-based multi-user resource allocation apparatus according to the embodiment of the present invention refers to the description of the 5G D-RAN-based multi-user resource allocation method in the above embodiment.
An embodiment of the present invention further provides a terminal for allocating multi-user resources based on 5G D-RAN, as shown in fig. 6, the terminal for allocating multi-user resources based on 5G D-RAN may include a processor 51 and a memory 52, where the processor 51 and the memory 52 may be connected by a bus or in another manner, and fig. 6 takes the connection by a bus as an example.
The processor 51 may be a Central Processing Unit (CPU). The Processor 51 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 52, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions/modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing of the processor by running non-transitory software programs, instructions and modules stored in the memory 52, namely, implements the 5G D-RAN-based multi-user resource allocation method in the above method embodiment.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 52 and when executed by the processor 51 perform a 5G D-RAN based multi-user resource allocation method as in the embodiment shown in fig. 2.
The specific details of the multi-user resource allocation terminal based on the 5G D-RAN may be understood by referring to the corresponding related description and effects in the embodiment shown in fig. 2, which are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined by the appended claims.
Claims (6)
1. A multi-user resource allocation method is characterized in that the allocation method is based on a 5G D-RAN technology, and comprises the following steps:
determining the task quantity of the user equipment and the task quantity of the edge server according to the tasks of the user equipment and the edge server;
calculating to obtain average total power consumption according to the user equipment and the edge server, and calculating to obtain average execution time delay according to the user equipment task quantity, the edge server task quantity and the base station cooperation proportion;
calculating according to the average total power consumption and the average execution time delay to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum;
obtaining the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay calculation, wherein the channel allocation and the calculation resource allocation comprise the following steps:
determining a limiting condition of resource allocation according to the CPU frequency, the channel, the user equipment task quantity and the edge server task quantity of the user equipment and the edge server;
determining an optimization problem of resource allocation according to the average total power consumption and the average execution time delay;
calculating the optimization problem according to the limiting conditions to obtain channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum;
the edge server task amount comprises the length of a cooperative queue at the next moment of the edge server and the length of an uncooperative queue at the next moment of the edge server;
the cooperative queue length at the next time of the edge server is represented by equation (2):
T i，j，cop (t+1)＝max{T i，j，incop (t)-D s，i，j (t)，0}+min{max{Q i (t)-D l，i (t)，D r，i，j (t)}}+G j (t) formula (2)
Wherein, G j (t) represents the amount of tasks received from other servers, Q i (t) represents the queue length at local time t, D l，i (t) represents the amount of local processing tasks;
the length of the uncooperative queue at the next time of the edge server is represented by equation (3):
T i，j，incop (t+1)＝max{T i，j，incop (t)-D s，i，j (t)，0}+min{max{Q i (t)-D l，i (t)，D r，i，j (t) } formula (3)
The average total power consumption of the system is represented by the following formula:
where T denotes the number of time slots, E denotes the average value, P loc，cop，i (t) denotes local cooperative energy consumption, P loc，incop，i (t) denotes local uncooperative energy consumption, P ser，cop，j (t) represents Server cooperative energy consumption, P ser，incop，j (t) represents server uncooperative energy consumption;
the limitation is expressed by the following equations (6) to (12):
0 ≤ μ ≤ 1 formula (6)
∑ i∈N α i，j (t)≤1，α i，j (t) ≥ 0 formula (7)
0≤f i (t)≤f i，max Formula (8)
0≤f ser，j (t)≤f ser，max Formula (9)
0≤p tx，i，j (t)≤p tx，max Formula (10)
Wherein μ is the cooperation ratio of the base stations, formula (7) represents the ratio of the channel allocated to i user by j base station to the total channel, the ratio is greater than or equal to 0, and the sum of the channel ratios occupied by all users is less than 1 for j base station; the formula (8), the formula (9) and the formula (10) respectively represent the limit of the local CPU frequency, the limit of the edge server CPU and the limit of the transmission energy consumption, and the formula (11) and the formula (12) represent the stability limit of the task buffer area, which represents that all the arriving calculation tasks can be completed within limited time delay;
calculating the optimization problem according to the limiting conditions to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum, wherein the method comprises the following steps:
calculating according to the Lyapunov function to obtain the total amount of tasks of the user equipment and the edge server;
calculating the optimization problem according to the total task amount, the average total power consumption and the Lyapunov penalty function to obtain an optimization equation;
calculating the optimization equation according to the limiting conditions and the Lyapunov algorithm to obtain channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum;
calculating the optimization equation according to the limiting conditions and the Lyapunov algorithm to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimized, wherein the method comprises the following steps:
determining dynamic expressions of CPU frequencies and channel transmission energy consumption of the user equipment and the edge server according to Gausser iteration;
calculating the dynamic expression according to a Lagrange multiplier method and a binary search algorithm to obtain a channel allocation and calculation resource allocation dynamic expression;
and determining the channel allocation and the computing resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the dynamic expression of the channel allocation and the computing resource allocation.
2. The method of claim 1, wherein the ue task volume comprises: the task amount of the task buffer area and the task amount processed by the user equipment, wherein the task amount of the edge server comprises the task amount unloaded to the edge server and the task amount received from other edge servers.
3. A multi-user resource allocation apparatus, wherein the allocation apparatus is based on 5G D-RAN technology, comprising:
a task amount determination module: the system comprises a task management module, a task scheduling module and a task scheduling module, wherein the task management module is used for determining the task amount of the user equipment and the task amount of the edge server according to the tasks of the user equipment and the edge server;
a power consumption calculation module: the method comprises the steps of calculating to obtain average total power consumption according to user equipment and an edge server;
the time delay calculation module is used for calculating and obtaining the average execution time delay according to the user equipment task amount, the edge server task amount and the base station cooperation proportion;
the resource allocation module is used for calculating and obtaining channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay;
calculating to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the average total power consumption and the average execution time delay, wherein the method comprises the following steps of:
determining a limiting condition of resource allocation according to the CPU frequency, the channel, the user equipment task quantity and the edge server task quantity of the user equipment and the edge server;
determining an optimization problem of resource allocation according to the average total power consumption and the average execution time delay;
calculating the optimization problem according to the limiting conditions to obtain channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum;
the task amount of the edge server comprises the length of a cooperative queue at the next moment of the edge server and the length of an uncooperative queue at the next moment of the edge server;
the cooperative queue length at the next time of the edge server is represented by equation (2):
T i，j，cop (t+1)＝max{T i，j，incop (t)-D s，i，j (t)，0}+min{max{Q i (t)-D l，i (t)，D r，i，j (t)}}+G j (t) formula (2)
Wherein G is j (t) represents the amount of tasks received from other servers, Q i (t) represents the queue length at local time t, D l，i (t) represents the amount of local processing tasks;
the length of the uncooperative queue at the next time of the edge server is represented by equation (3):
T i，j，incop (t+1)＝max{T i，j，incop (t)-D s，i，j (t)，0}+min{max{Q i (t)-D l，i (t)，D r，i，j (t) } formula (3)
The average total power consumption of the system is represented by the following formula:
where T denotes the number of time slots, E denotes the average value, P loc，cop，i (t) denotes local cooperative energy consumption, P loc，incop，i (t) denotes local uncooperative energy consumption, P ser，cop，j (t) represents Server cooperative energy consumption, P ser，incop，j (t) represents server uncooperative energy consumption;
the limitation is expressed by the following equations (6) to (12):
0 ≤ μ ≤ 1 formula (6)
∑ i∈N α i，j (t)≤1，α i，j (t) ≥ 0 formula (7)
0≤f i (t)≤f i，max Formula (8)
0≤f ser，j (t)≤f ser，max Formula (9)
0≤p tx，i，j (t)p tx，max Formula (10)
Mu is the cooperation proportion of the base station, formula (7) represents the proportion of the channels allocated to i users by j base stations in the total channels, the proportion is more than or equal to 0, and the sum of the channel proportions occupied by all users is less than 1 for j base stations; the formula (8), the formula (9) and the formula (10) respectively represent the limitation of the local CPU frequency, the limitation of the edge server CPU and the limitation of the transmission energy consumption, and the formula (11) and the formula (12) represent the stability limitation of the task buffer area, and represent that all the arriving calculation tasks can be completed within limited time delay;
calculating the optimization problem according to the limiting conditions to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum, wherein the method comprises the following steps:
calculating to obtain the total amount of tasks of the user equipment and the edge server according to the Lyapunov function;
calculating the optimization problem according to the total task amount, the average total power consumption and the Lyapunov penalty function to obtain an optimization equation;
calculating the optimization equation according to the limiting conditions and the Lyapunov algorithm to obtain channel allocation and calculation resource allocation when the energy consumption of the user equipment and the edge server is minimum;
calculating the optimization equation according to the limiting conditions and the Lyapunov algorithm to obtain the channel allocation and the calculation resource allocation when the energy consumption of the user equipment and the edge server is minimized, wherein the method comprises the following steps:
determining dynamic expressions of CPU frequencies and channel transmission energy consumption of the user equipment and the edge server according to Gausser iteration;
calculating the dynamic expression according to a Lagrange multiplier method and a binary search algorithm to obtain a channel allocation and calculation resource allocation dynamic expression;
and determining the channel allocation and the computing resource allocation when the energy consumption of the user equipment and the edge server is minimum according to the dynamic expression of the channel allocation and the computing resource allocation.
4. A multi-user resource allocation system, comprising:
a plurality of user equipment;
a plurality of edge servers;
a microprocessor for allocating signals of the user equipment and the edge server according to the multi-user resource allocation method of claim 1 or 2.
5. A computer-readable storage medium having stored thereon computer instructions for causing a computer to perform the multi-user resource allocation method of claim 1 or 2.
6. A multi-user resource allocation terminal, comprising: a memory and a processor communicatively coupled to each other, the memory storing computer instructions, and the processor performing the multi-user resource allocation method of claim 1 or 2 by executing the computer instructions.
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