CN110740473A - management method for mobile edge calculation and edge server - Google Patents
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Abstract
Especially under the condition that the size of the downlink data of scenes caused by the technical development at present is large and cannot be ignored, the invention can minimize the total time delay of all tasks corresponding to the task execution request, and for the condition that the size of the downlink data is small, the invention can also minimize the total time delay of all tasks corresponding to the task execution request, and can efficiently meet the requirement of low time delay of users under different scenes, thereby improving the user experience.
Description
Technical Field
The present invention relates to the field of wireless communication, and in particular, to joint optimization of computation offload and resource allocation in mobile edge computing, and more particularly, to management methods and edge servers for mobile edge computing.
Background
With the rapid development of mobile communication and the rapid popularization of intelligent mobile terminals, many new applications such as virtual reality, augmented reality, automatic driving, etc. are generated, and such applications with low latency and high reliable communication requirements put high demands on the computing power of the mobile terminal, because the mobile terminal with limited computing power will generate higher application processing latency and affect the service experience of the terminal user when processing such applications, how to reduce the application processing latency and improve the service experience of the terminal user is which is a key problem that needs to be solved at present.
In view of the above problems, Mobile Cloud Computing (MCC) technology has been proposed. The MCC aims to extend rich computing resources at the cloud to mobile terminals with limited resources, thereby enhancing potential computing capabilities of the mobile terminals and reducing application processing delay. To achieve this goal, the mobile terminal needs to migrate the computationally intensive task to the cloud server by way of wireless access. Although the method can reduce the load of the mobile terminal, the method also has the obvious defect that the longer distance between the mobile terminal and the cloud server and the large number of terminal service requests cause the increase of network delay, thereby reducing the service experience of terminal users.
The European Telecommunications Standards Institute (ETSI) proposed new technologies, Mobile Edge Computing (MEC), in which a server with fixed location and powerful computing power is disposed at the edge of a network (e.g., a base station) to reduce the communication load and network delay of users in its coverage area.
Currently, there are three main methods for reducing the task delay of a mobile terminal in an MEC network:
the method comprises the steps that a base station can acquire basic information of a user, such as transmission distance, a requested task type, the number of users in a current cell, position information of the user in the cell in a cellular network, computing capacity of an edge server and the like, the mobile user selects unloading by comparing execution delay of the task in the edge server with execution delay in the local, and accordingly optimization of an unloading strategy is completed, and meanwhile, the terminal user obtains the lowest delay.
Compared with the method, the method limits the computing capacity and system communication resources of the edge server in step , when there is a task request of a user, the edge server performs optimal allocation of uplink resources and computing resources for the user through the obtained task request, and then the time delay obtained by the optimal allocation is compared with the local computing time delay to obtain an optimal unloading strategy and a minimum time delay.
However, the third method has the disadvantages that the distance is long, the communication delay of the user is increased, and when a plurality of users make service requests, network blockage is easily caused, and the application processing delay is increased by , and the third method does not consider the influence of downlink transmission delay on the total delay of the task.
It can be seen that the task delay of the mobile terminal is affected by being the transmission delay of the uplink, which is very important how to allocate the optimal bandwidth resources to the user to meet the user's requirements after the user inputs data, by task delay which is also very important how to allocate the optimal computing resources to tasks to meet the user's requirements for delay, and by downlink transmission delay, which is very important how to allocate the optimal bandwidth resources to the user to meet the user's requirements for transmission of the output data when the input data is calculated and the output data is output to the user.
For example, a random offload algorithm (ROC) randomly allocates offload decisions for each user when the user has a task request, so that tasks of a part of users are randomly executed locally, and tasks of another part of users are offloaded to an edge server for execution.
However, in practical buffer communication application scenarios, cases in which downlink data is large, such as AR, VR, remote monitoring, etc., downlink data will seriously affect user delay and thus cannot be ignored, and especially in special application scenarios, there is a great demand for low user delay.
In summary, the existing solutions have limited applicable scenarios, and cannot efficiently meet the requirement of low latency of users in different scenarios under the situation that the size of the downlink data of scenarios is large and cannot be ignored due to the technical development at present.
Disclosure of Invention
Therefore, an object of the present invention is to overcome the above-mentioned drawbacks of the prior art, and provide management methods and edge servers for mobile edge calculation, which comprehensively consider the effect of uplink transmission delay, computation delay, and downlink transmission delay on the total delay of a task for executing a user task.
According to aspect of the invention, the invention provides management methods for mobile edge calculation, which are used for edge auxiliary calculation and system resource optimal allocation before auxiliary calculation of a system composed of a base station, a mobile terminal and an edge server within the coverage area of the base station, and the following steps are executed for each base station coverage areas:
s1, responding to task requests of all mobile terminals at the current moment, initializing unloading decisions of each mobile terminal, and randomly setting the initial unloading decisions of each mobile terminal as a th decision or a second decision;
s2, based on the information of the mobile terminal, the current resources of the system and the initial unloading decision set in the step S1, calculating the total time delay including the uplink transmission time delay, the calculation time delay and the downlink transmission time delay corresponding to the execution of the tasks of all the mobile terminals according to the initial decision;
s3, sequentially calculating a total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision, wherein if the total time delay is reduced after a certain mobile terminal adjusts the current offload decision, the offload decision of the mobile terminal is adjusted, otherwise, the offload decision of the mobile terminal is not adjusted;
s4, after rounds of calculation are carried out on each mobile terminal, it is determined that no mobile terminals adjust the unloading decision in the round of adjustment process, the adjustment process is ended, and the final unloading decision and the final system resource allocation scheme of each mobile terminal are obtained;
s5, independently allocating the uplink and downlink communication resources of the time and the computing resources of the edge server needed by the task of the auxiliary computing of the time for the mobile terminal with the final unloading decision of according to the system resource allocation scheme;
s6, executing the task request according to the final unloading decision of each mobile terminal; and/or
S7, entering next task cycles after completing the task requests of all the mobile terminals at the current moment, and re-executing the steps S1-S7;
the th decision indicates that the mobile terminal offloads the task corresponding to the task request to the edge server for calculation, the second decision indicates that the mobile terminal places the task corresponding to the task request in local calculation, the uplink or downlink communication resources are allocated to the mobile terminal whose final offloading decision is the th decision in a percentage manner, the computing resources of the edge server are allocated to the mobile terminal whose final offloading decision is the th decision in a percentage manner, and the percentage of the resources occupied by every mobile terminals whose final offloading decisions are the th decisions is the percentage of the task request requirements in the total task request requirements corresponding to all the mobile terminals whose final offloading decisions are the th decisions.
Wherein the step S3 includes:
s31, sequentially calculating the total time delay when the unloading decision of each mobile terminal is adjusted to a decision opposite to the current unloading decision, wherein the total time delay is calculated each time based on the system resource allocation scheme adapted to the unloading decisions of all the mobile terminals after the current mobile terminal is adjusted; and/or
S32, if the total delay is reduced after a certain mobile terminal adjusts the current offload decision, adjusting the offload decision and the resource allocation scheme of the mobile terminal, otherwise, not adjusting.
It is preferable that the downlink transmission delay is a ratio of a size of the calculation result data to a downlink transmission rate, and in calculating the downlink transmission delay, the corresponding size of the calculation result data may be provided according to out of the following manners, in a case where the same task is calculated in the previous stage of the edge server, the size of the calculation result data obtained in the previous stage is provided as the size of the calculation result data of the corresponding task according to the history data of the edge server, in a case where the same task is not calculated in the previous stage of the edge server but the same type of task is calculated, a reference range is formed according to the history data of the edge server and a th random value is randomly provided within the reference range as the size of the calculation result of the corresponding task, and in a case where the same task is not calculated in the previous stage of the edge server and the same type of task is not calculated, second random values smaller than or equal to the size of the task are randomly provided as the size of the corresponding calculation result by the edge server.
According to another aspect of the invention, the invention provides edge servers for providing services including at least assistance calculations for mobile terminals in an area covered by a base station associated therewith, the edge servers comprising or more processors and a memory, wherein the memory is configured to store executable instructions, the or more processors being configured to perform the aforementioned methods via execution of the executable instructions.
Compared with the prior art, the method has the advantages that the method fully considers the influence of the uplink transmission delay, the calculation delay and the downlink transmission delay for executing all user tasks on the total delay of the tasks, comprehensively considers the influence of the uplink transmission delay, the calculation delay and the downlink transmission delay on the total delay before the edge server provides auxiliary calculation, and optimally allocates the currently limited resources of the system for each mobile terminal so as to reduce the total delay for executing all user tasks.
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Embodiments of the present invention are further described with reference to the accompanying drawings, in which:
fig. 1 is a diagram illustrating a conventional architecture of a base station system according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating a comparison of total delays corresponding to a management method for mobile edge calculation and three existing methods in different numbers of mobile terminals according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a comparison of total delays corresponding to a management method for mobile edge calculation and three existing methods under different input data sizes according to an embodiment of the present invention;
fig. 4 is a schematic diagram illustrating comparison of total delays corresponding to a management method for mobile edge calculation and three existing methods under different calculation result data sizes according to an embodiment of the present invention.
Detailed Description
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference is made to the following detailed description taken in conjunction with the accompanying drawings.
First, the background of the invention is introduced .
In the optimization process, because the computing capacities of different users in the area covered by a single base station are different, and in addition, different user execution tasks have different characteristics, namely the size of input data, the size of output data and the number of CPU cycles required by the tasks are different, namely the uplink network bandwidth resources, the downlink network bandwidth resources and the edge server computing resources required by different user task requests are different.
Next, some of the terms used in the present invention are defined as follows:
an edge server refers to a server deployed near the edge of a network, such as a base station, to serve corresponding users. In the present application, the service includes at least an auxiliary computing service, and the user may refer to a mobile terminal.
CPU cycles refer to the number of times an instruction is executed per second, and are referred to by the English name CPU cycles.
Fig. 1 shows a schematic diagram of a conventional architecture of a base station system according to embodiments of the present invention, where the system includes a base station, an edge server and mobile terminals in a coverage area of the base station, the edge server is deployed at the base station at the edge of the network to provide services for the mobile terminals in the coverage area of the base station, in this application, each mobile terminal corresponds to users of the base station, the base station is responsible for providing communication services for the mobile terminals, and the edge server is responsible for providing computing services for the mobile terminals.
According to embodiments of the present invention, the present invention provides management methods for mobile edge calculation, which are used for edge assisted calculation and system resource optimized allocation before assisted calculation of a system composed of a base station, a mobile terminal and an edge server within the coverage of the base station, and for each coverage areas of the base station, the following main steps are performed:
s1, responding to task requests of all mobile terminals at the current moment, initializing unloading decisions of each mobile terminal, and randomly setting the initial unloading decisions of each mobile terminal as a th decision or a second decision;
s2, based on the information of the mobile terminal, the current resources of the system and the initial unloading decision set in the step S1, calculating the total time delay including the uplink transmission time delay, the calculation time delay and the downlink transmission time delay corresponding to the execution of the tasks of all the mobile terminals according to the initial decision;
s3, sequentially calculating a total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision, wherein if the total time delay is reduced after a certain mobile terminal adjusts the current offload decision, the offload decision of the mobile terminal is adjusted, otherwise, the offload decision of the mobile terminal is not adjusted;
s4, after rounds of calculation are carried out on each mobile terminal, it is determined that no mobile terminals adjust the unloading decision in the round of adjustment process, the adjustment process is ended, and the final unloading decision and the final system resource allocation scheme of each mobile terminal are obtained;
s5, independently allocating the uplink and downlink communication resources of the time and the computing resources of the edge server needed by the task of the auxiliary computing of the time for the mobile terminal with the final unloading decision of according to the system resource allocation scheme;
s6, executing the task request according to the final unloading decision of each mobile terminal; and/or
S7, entering next task cycles after completing the task requests of all the mobile terminals at the current moment, and re-executing the steps S1-S7;
the th decision indicates that the mobile terminal offloads the task corresponding to the task request to the edge server for calculation, the second decision indicates that the mobile terminal places the task corresponding to the task request in local calculation, the uplink or downlink communication resources are allocated to the mobile terminal whose final offloading decision is the th decision in a percentage manner, the computing resources of the edge server are allocated to the mobile terminal whose final offloading decision is the th decision in a percentage manner, and the percentage of the resources occupied by every mobile terminals whose final offloading decisions are the th decisions is the percentage of the task request requirements in the total task request requirements corresponding to all the mobile terminals whose final offloading decisions are the th decisions.
Preferably, the method may further comprise: before executing step S1, each time, the mobile terminal information of all mobile terminals in the coverage area of the base station is acquired and the user information table in which the mobile terminal information is recorded is refreshed, so that the total time delay of this time is calculated based on the mobile terminal information updated in the user information table each time. The mobile terminal information includes location information of the mobile terminal, computing power, and requested specific task information. The mobile terminal information may also include the transmit power of the mobile terminal.
Preferably, for step S1, the edge server may complete the task request of rounds of users in the coverage area, send the calculation result obtained by performing the auxiliary calculation on the tasks unloaded by the received mobile terminals to the corresponding mobile terminals, and then start to respond to the task requests of all the mobile terminals in the current rounds.
For a better understanding of the present invention, the following detailed description is made for each steps with reference to specific examples.
S1, responding to task requests of all mobile terminals at the current time, initializing an offloading decision of each mobile terminal, and randomly setting an initial offloading decision of each mobile terminal to be a th decision or a second decision, for example, assuming that mobile terminals of three users in a coverage area of a current base station send task requests, at this time, the task requests of all mobile terminals at the current time are task requests sent by the three users, and the edge server responds to the task requests of all mobile terminals at the current time, and randomly sets the initial offloading decisions corresponding to the three users to be 0 or 1, for example, the initial offloading decisions corresponding to the three users are randomly set to be 0,1, and 1 represents a th decision, and 0 represents the second decision.
S2, based on the information of the mobile terminals, the current resources of the system and the initial unloading decision set in the step S1, calculating the total time delay including uplink transmission time delay, calculation time delay and downlink transmission time delay corresponding to the tasks of all the mobile terminals when being executed according to the initial decision, after the initial unloading decision of each user is randomly set, correspondingly distributing the system resources according to the initial unloading decision of the user to form initial system resource distribution schemes corresponding to the initial unloading decisions, so as to calculate the corresponding total time delay obtained according to the initial system resource distribution schemes of the initial unloading decisions, according to embodiments of the invention, it is assumed that each mobile terminal only requests to execute tasks wi＝{si,di,ciIn which s isiIndicating the size of the input data corresponding to the task, diIndicating the size of the calculation result data, ciRepresents the total amount of CPU cycles required for tasks, all movesAnd the set of the unloading decision of the mobile terminal is an unloading strategy.
Thus, the uplink transmission delay can be expressed as
Wherein,
the rate of uplink data transmission can be expressed as:
wherein k isiRepresents the percentage of the uplink bandwidth occupied by the task uploaded by the mobile terminal i, Pi,sDenotes the transmission power, h, of the mobile terminal ii,BDenotes the channel fading coefficient from the mobile terminal i to the base station, d denotes the distance between the mobile terminal i and the base station, r denotes the path loss, σ2Representing the noise power of the channel.
The downlink transmission delay can be expressed as
Wherein,
the rate of downlink transmission data can be expressed as:
wherein, ξiRepresents the bandwidth percentage h occupied by the mobile terminal i when receiving the downlink dataB,iRepresenting the channel fading coefficient, P, from the base station to the mobile terminal iBRepresenting the transmit power of the base station.
The computation delay is divided into two types, types are the computation delay when the task is executed on the edge server, and types are the computation delay when the task is executed locally.
The computational latency of a task executing on an edge server can be expressed as：fi,mIndicating the computing resources allocated by the edge server for mobile terminal i.
The computational latency of a task executing locally can be expressed as:fi,lrepresenting the computing power local to the mobile terminal i.
Therefore, the time delay caused by the task of the mobile terminal i being offloaded to the edge server for execution can be expressed as: u shapei,m＝ti,u+ti,d+ti,m. The time delay caused by the local execution of the task of the mobile terminal i can be expressed as: u shapei,l＝ti,l. As can be seen, the time delay for the mobile terminal to offload to the edge server to execute the task includes uplink transmission time delay, calculation time delay, and downlink transmission time delay. The time delay of the mobile terminal for executing the task locally comprises calculation time delay, and the time delay of the mobile terminal for executing the task locally does not have uplink transmission time delay and downlink transmission time delay because the processes of uploading the task and receiving the calculation result data do not exist.
The delay problem model proposed by the embodiment of the present invention is a model when multi-user task requests are performed in an area covered by a single base station, in the delay problem basic model, the computing power of each mobile terminal can be different, each task can be different, and the bandwidth resources can be designed according to a percentage, each mobile terminal in the delay problem basic model can request or more tasks at a time.
Preferably, the basic model of the delay problem can be expressed as:
where a denotes an offload policy, a ═ ai}i∈L，aiRepresents an offload decision for mobile terminal i, aiE {0,1}, when aiWhen the task request is equal to 1, the unloading decision of the mobile terminal i is represented as the th decision, the mobile terminal i unloads the task corresponding to the task request to the edge server for calculation, and when a is equal toiWhen the current task is equal to 0, the offloading decision of the mobile terminal i is represented as a second decision, the mobile terminal i places the task corresponding to the task request in local computation, L represents the set of all the mobile terminals, and L is { i: i ═ 1, 2.. N },
fi,mrepresenting computing resources allocated by an edge server for a mobile terminal iSource, fmIndicating the total computing power that the edge server can provide,
kirepresenting the percentage of uplink bandwidth occupied by the mobile terminal i uploading its task,
ξirepresents the percentage of bandwidth occupied by the mobile terminal i receiving the downlink data, a represents a specific sets of offloading decisions, a ═ a { (a)1,a2,...an}，anN in (1) is the current actual total number of users, i.e. the actual total number of mobile terminals, Ui,mRepresents the total time delay, U, calculated by the mobile terminal i offloading its tasks to the edge serveri,lThe computation delay caused by the task placed by the mobile terminal i in the local computation is represented, the constraint C1 represents that the sum of the computation resources allocated to at least two mobile terminals is less than or equal to the computation capacity of the server, the constraint C2 represents that the sum of the uplink spectrum resources allocated to at least two mobile terminals is less than or equal to the total uplink bandwidth of the system, the constraint C3 represents that the sum of the downlink spectrum resources allocated to at least two mobile terminals is less than or equal to the total downlink bandwidth of the system, the constraint C4 represents that the local computation resources of the mobile terminal i are non-negative, and the constraint C5 represents that the offloading decision of the mobile terminal i is the th decision or the second decision.
S3, sequentially calculating a total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision, wherein if the total time delay is reduced after a certain mobile terminal adjusts the current offload decision, the offload decision of the mobile terminal is adjusted, otherwise, the offload decision of the mobile terminal is not adjusted;
and S4, calculating rounds of calculation for each mobile terminal, determining that no mobile terminals adjust the unloading decision in the round of adjustment process, and ending the adjustment process to obtain the final unloading decision and the final system resource allocation scheme of each mobile terminal.
According to embodiments of the present invention, in step S3, in the adjustment process of sequentially calculating the total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision, the number of the offload decisions adjusted at each time may be or more, for example, the total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision may be calculated by adjusting only the offload decision of a single mobile terminal and not adjusting the offload decisions of other mobile terminals at this time, specifically, if the current offload decision of a certain mobile terminal is adjusted to a decision opposite to the current offload decision, the current offload decision of the mobile terminal is adjusted to 1, the offload decisions of the other mobile terminals are not adjusted at this time, so the adjusted total time delay is calculated, or, if the total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision is calculated, the total time delay when the offload decision of two mobile terminals is adjusted at each time and not adjusted to the current offload decision of the other mobile terminals is adjusted to 1, and the current offload decision is calculated as two offload decisions, and the total time delay of the current offload decision is calculated as the offload decision of the current offload decision of the other mobile terminals is not adjusted to 1.
According to embodiments of the present invention, for steps S3 to S4, the final offloading decision and system resource allocation scheme can be analyzed and determined by constructing a delay problem solution model, which uses a game model, but the solution to the problem is not limited to the game model.
Preferably, the delay problem solution model can be expressed as:
G＝{L，(Ai)i∈L，ui}；
wherein L represents the set of all mobile terminals, AiIndicates the offload policy, u, of the mobile terminal iiThe time delay utility function of the mobile terminal i is represented, and the time delay of the mobile terminal i is represented as
Indicating the offloading decision of the other mobile terminal j of the at least two mobile terminals than mobile terminal i.
According to embodiments of the present invention, the method of the present application can obtain a final resource allocation scheme containing an optimal solution through a time delay problem solution model by gaming for each mobile terminal, which is expressed as:
optimal solution of computing resources allocated by edge server for mobile terminal i
Mobile terminal i uploads the optimal solution of the percentage of the uplink bandwidth occupied by the task
Optimal solution of bandwidth percentage occupied by mobile terminal i when receiving downlink data
Wherein,
b represents the total spectral bandwidth, hi,BRepresenting the channel fading coefficient, P, from the mobile terminal i to the base stationi,sExpressed as the transmission power of the mobile terminal i, d the distance between the mobile terminal i and the base station, r the path loss, σ2Representing the noise power of the channel, hB,iRepresenting the channel fading coefficient, P, from the base station to the mobile terminal iBThe optimization factor mu, the second optimization factor lambda and the third optimization factor theta are correspondingly optimized and updated each time the unloading decision of the corresponding mobile terminal is adjusted in the iterative adjustment process, and formulas adopted by the optimization and update are respectively as follows:
wherein,
for the iteration step length, the value range of the iteration step length can be 10-5～10-7T is the number of iterations and t is not less than 0, μ (t +1), λ (t +1) and θ (t +1) are the values of μ, λ and θ, respectively, after this adjustment, μ (t), λ (t) and λ (t) are the values of μ, λ and θ, respectively, before this adjustment, in cases, although μ (t) - μ (t +1) is not more than 10-6、λ(t)-λ(t+1)≤10-6And theta (t) -theta (t +1) is less than or equal to 10-6The situation is not completely satisfied, but in the adjusting process, in the whole process of starting from a certain mobile terminals and going through rounds of adjusting processes and returning to the mobile terminals again, none of mobile terminals adjust their unloading decisions in the round of adjusting processes, and the adjusting process is ended, so that the final unloading decision and the final system resource of each mobile terminal corresponding to the minimum total delay are obtainedIn another cases, the offload decision for the respective mobile terminal is adjusted at a time and after this adjustment, μ (t) - μ (t +1) ≦ 10-6、λ(t)-λ(t+1)≤10-6And theta (t) -theta (t +1) is less than or equal to 10-6The iteration number may be set to a value of 10000 to 20000, for example, the iteration number refers to the maximum number of iterations, in an actual case, the maximum number may not be reached, and a final unloading decision and a final system resource allocation scheme may be obtained, for example, the iteration number is set to 15000, in an actual adjustment process, of the two cases may occur at 8000, and the adjustment process is ended.
It can be seen from the above that the system resource allocation scheme includes , second and third optimal solutions, wherein the optimal solution is a calculation resource allocated to the corresponding mobile terminal, the second optimal solution is a percentage of uplink bandwidth occupied by the task of uploading the resource allocated to the corresponding mobile terminal, the third optimal solution is a percentage of bandwidth occupied by receiving downlink data allocated to the corresponding mobile terminal, the , second and third optimal solutions correspond to the , second and third optimization factors, respectively, preferably, in order to prevent the iteration time from being too long, according to embodiments of the present invention, the method further includes ending the adjustment process if occurs during the execution of steps S3 to S4, the reduction values of the T5, the for optimizing the system resource allocation scheme, the second and third optimization factors are all smaller than the set threshold, and the T2, the number of actual iteration adjustment times reaches the set maximum iteration times, the final adjustment times of the system resource allocation process reaches the set maximum iteration times, the final adjustment process is obtained by using the final reduction values of the mobile terminal (T + T) and the final user allocation time after the final adjustment process is finished, and the final decision of the final user distribution of the final distribution of the mobile terminal (no final distribution time) is equal to the final distribution of the user.
Then, the base station and the edge server receive the task unloaded by the mobile terminal with the final unloading decision being th decision under the condition of the uplink communication resource distributed at this time and complete the auxiliary calculation of the received task, and transmit the calculation result corresponding to the task to the corresponding mobile terminal under the condition of the downlink communication resource distributed at this time.
Preferably, in step S4, the requirement for ending the adjustment process is that, in round of adjustment processes from a certain mobile terminals, none of mobile terminals in all mobile terminals adjust their offload decisions in the round of adjustment processes, for example, assuming that 5 mobile terminals all send task requests, and when the initial offload decision of five mobile terminals is randomly set as the th decision or the second decision, the initial offload decisions of the five mobile terminals are 1, 0, respectively, and the offload decisions of the five mobile terminals after multiple adjustments become 0,1, 0, respectively, and after the previous adjustment, in the next round, starting from the th mobile terminal to the fifth mobile terminal, their offload decisions are not adjusted, and after the round of adjustment, the offload decisions of the five mobile terminals are still 0,1, 0, and then the adjustment process is ended.
Therefore, preferably, according to embodiments of the present invention, the method may further include, for the calculated task, associatively maintaining information of the task and a size of the corresponding calculation result data so as to provide a reference for the size of the calculation result data of the corresponding task based on the historical data.
According to embodiments of the present invention, in order to perform downlink communication resource allocation in advance using the size of calculation result data, the corresponding size of calculation result data may be provided according to one of of the following ways:
type, in case that the edge server has calculated the same task earlier, it provides the size of the calculation result data earlier obtained as the size of the calculation result data of the corresponding task according to the history data of the edge server, for example, a certain task edge server has calculated earlier, the edge server has kept the history data, the history data has recorded the information of the task and the size of the calculation result data corresponding to the task, so that the size of the calculation result data obtained earlier can be used as the size of the calculation result data of the corresponding task.
In the second mode, in the case that the same task is not calculated in the previous period of the edge server, and a th random value is randomly provided in the reference range as the size of the calculation result of the corresponding task, for example, for tasks which are a certain data format of the AR domain, the same task is not calculated in the previous period of the edge server, but times of the same data format of the task are calculated in the previous period, and the historical data indicates that the ratio of the size of the calculation result data of the data format to the size of the task is 20%, then reference ranges are formed by multiplying the size of the task by 10% -30%, if the previous period is calculated twice, the historical data indicates that the ratio of the size of the calculation result data of the data format to the size of the task is 20% and 40%, respectively, then reference ranges are formed by multiplying the size of the task by 20% -40%, if the previous period is calculated twice, the historical data indicates that the ratio of the size of the calculation result data of the data format to the size of the task is 20% and 40%, respectively, then % of the corresponding task is calculated in the previous period, and then % of the corresponding task is calculated in the random value.
In the third mode, in the case that neither the same nor the same type of task has been calculated in the previous stage of the edge server, second random values smaller than or equal to the size of the task are randomly provided by the edge server as the size of the corresponding calculation result.
Preferably, the mobile terminal can comprise at least of a mobile phone, a tablet computer, a notebook computer, a vehicle-mounted computer, VR glasses, AR glasses and a smart watch.
According to examples of the present invention, the proposed method of the present invention was simulation verified by the following simulation parameters, which are given in Table 1.
TABLE 1 simulation parameters
In fig. 2, 3 and 4, the CORAG curve corresponds to the method adopted in the present application, which is an abbreviation of a computing offloading and resource allocation algorithm (CORAG) based on the game theory. The LOC curve corresponds to a Local offload algorithm (LOC) in which all tasks are not offloaded to the edge server for execution, but are executed locally at each mobile terminal. The ROC curve corresponds to a random offload algorithm (ROC). The COURG curve corresponds to a computation offloading and uplink resource allocation (COURG), and the second method of the background art uses the algorithm.
FIG. 2 shows a comparison of total time delays corresponding to embodiments of the method for assisting edge calculation and three prior art methods for different mobile terminals, respectively, and it can be seen from FIG. 2 that the performance of the proposed method is better than that of LOC, ROC and COURG algorithms, the advantage of the CORAG algorithm is more obvious as the number of mobile terminals increases, because the CORAG algorithm can better allocate optimal calculation and communication resources to different mobile terminals under the condition of limited calculation and communication resources, so that the total time delay of all mobile terminals is the lowest, the input parameters of FIG. 2 adopt the parameters listed in Table 1, wherein part of the input parameters are changed, for example, the last four parameters in Table 1 are random values randomly given by a computer in the above normal distribution range.
Fig. 3 is a schematic diagram illustrating a total delay comparison corresponding to a management method for mobile edge calculation and three existing methods under different input data sizes according to an embodiment of the present invention. As can be seen from fig. 3, as the upstream input data increases, the total delay of the CORAG algorithm is lower than that of the LOC, ROC, and COURG algorithms. This is because, as the input data increases, the CORAG algorithm may allocate optimal uplink and downlink bandwidth resources and calculation resources according to the size of the input data, thereby minimizing the total delay of all terminal devices.
Fig. 4 is a schematic diagram illustrating a total delay comparison corresponding to a management method for mobile edge calculation and three existing methods under different calculation result data sizes according to an embodiment of the present invention. As can be seen from fig. 4, with the increasing of the calculation result data, the total delay of the CORAG algorithm is lower than that of the three algorithms of LOC, ROC, and COURG. The performance of the CORAG algorithm is obviously superior to that of the other three algorithms, and the performance advantage of the CORAG algorithm is more obvious along with the gradual increase of calculation result data.
In yet another embodiments of the present invention, there are also provided edge servers operable to provide services including at least assisted computing for mobile terminals in an area covered by a base station associated therewith, the edge server comprising or more processors and a memory, wherein the memory is configured to store executable instructions, and wherein the or more processors are configured to perform the aspects of the present invention and/or the methods of any of embodiments described above via execution of the executable instructions.
In yet another embodiments of the present invention, there is also provided electronic devices, more specifically, communication devices, comprising an edge server and a base station communicatively connected to each other, the edge server capable of providing services including at least auxiliary computing to mobile terminals in an area covered by the base station, the edge server comprising or more processors and a memory, wherein the memory is configured to store executable instructions, and or more processors are configured to execute the technical solution introduced in any of the aforementioned embodiments and/or the method of the present application via execution of the executable instructions.
It should be noted that although the steps are described in a specific order, it is not meant that the steps must be performed in the specific order described, and indeed, of the steps may be performed concurrently or even in a different order, as long as the desired functionality is achieved.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may include, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1, management method for mobile edge calculation, used for edge auxiliary calculation and system resource optimization allocation before auxiliary calculation of a system composed of base stations, mobile terminals and edge servers within the coverage of the base stations, characterized in that, for each coverage areas of the base stations, the following steps are executed:
s1, responding to task requests of all mobile terminals at the current moment, initializing unloading decisions of each mobile terminal, and randomly setting the initial unloading decisions of each mobile terminal as a th decision or a second decision;
s2, based on the information of the mobile terminal, the current resources of the system and the initial unloading decision set in the step S1, calculating the total time delay including the uplink transmission time delay, the calculation time delay and the downlink transmission time delay corresponding to the execution of the tasks of all the mobile terminals according to the initial decision;
s3, sequentially calculating a total time delay when the offload decision of each mobile terminal is adjusted to a decision opposite to the current offload decision, wherein if the total time delay is reduced after a certain mobile terminal adjusts the current offload decision, the offload decision of the mobile terminal is adjusted, otherwise, the offload decision of the mobile terminal is not adjusted;
and S4, after rounds of calculation are carried out on each mobile terminal, it is determined that no mobile terminals adjust the unloading decision in the round of adjustment process, and the adjustment process is ended to obtain the final unloading decision and the final system resource allocation scheme of each mobile terminal.
2. The method of claim 1, wherein the decision indicates that the mobile terminal offloads the task corresponding to its task request to the edge server for computation, and the second decision indicates that the mobile terminal places the task corresponding to its task request in a local computation.
3. management method for mobile edge calculation, according to claim 1 or 2, further comprising:
s5, independently allocating the uplink and downlink communication resources of the time and the computing resources of the edge server needed by the task of the auxiliary computing of the time for the mobile terminal with the final unloading decision of according to the system resource allocation scheme;
s6, executing the task request according to the final unloading decision of each mobile terminal.
4. The management method of mobile edge computations according to claim 3, wherein the uplink or downlink communication resources are allocated to the mobile terminal with the final offload decision being the decision by percentage, the computing resources of the edge server are allocated to the mobile terminal with the final offload decision being the decision by percentage, and the percentage of the resource occupied by every mobile terminals with the final offload decision being the decision is the percentage of the total task request requirement of all the mobile terminals with the final offload decision being the decision.
5. The management method for mobile edge computing as claimed in claim 3, wherein the system has task cycles to complete the task requests of all the mobile terminals at the current time, the method further comprising:
and S7, entering the next task cycles after the task requests of all the mobile terminals at the current moment are completed, and re-executing the steps S1-S6.
6. The management method for mobile edge calculation, according to claim 5, wherein the method further comprises, each time before executing step S1, obtaining the mobile terminal information of all mobile terminals in the coverage area of the base station and refreshing the user information table describing the mobile terminal information to calculate the total time delay each time with the updated mobile terminal information;
the mobile terminal information comprises position information, computing capacity and requested specific task information of the mobile terminal.
7. The management method for mobile edge calculation, according to claim 5, wherein the step S3 includes:
s31, sequentially calculating the total time delay when the unloading decision of each mobile terminal is adjusted to a decision opposite to the current unloading decision, wherein the total time delay is calculated each time based on the system resource allocation scheme adapted to the unloading decisions of all the mobile terminals after the current mobile terminal is adjusted;
s32, if the total delay is reduced after a certain mobile terminal adjusts the current offload decision, adjusting the offload decision and the resource allocation scheme of the mobile terminal, otherwise, not adjusting.
8. The managing method of for moving edge calculation, according to claim 1 or 2, wherein the downlink transmission delay is the ratio of the size of the calculation result data to the downlink transmission rate, wherein in calculating the downlink transmission delay, the corresponding size of the calculation result data is provided according to of the following ways:
under the condition that the same task is calculated by the edge server at the earlier stage, providing the size of calculation result data obtained at the earlier stage according to the historical data of the edge server as the size of the calculation result data of the corresponding task;
in the case that the same tasks are not calculated but the same type of tasks are calculated at the previous stage of the edge server, forming a reference range according to the historical data of the edge server and randomly providing th random values in the reference range as the size of the calculation result of the corresponding task, and
in the case where neither the same nor the same type of task has been calculated at the previous stage of the edge server, second random values smaller than or equal to the size of the task are randomly provided by the edge server as the size of the corresponding calculation result.
A computer-readable storage medium , having embodied thereon a computer program, the computer program being executable by a processor to perform the method of any of claims 1 to 8 to .
Edge server of the kind , for providing services including at least assisted computing for mobile terminals in an area covered by a base station associated therewith, characterized in that the edge server comprises:
or more processors, and
a memory, wherein the memory is to store executable instructions;
the one or more processors are configured to perform the method of any of claims 1-8 through via execution of the executable instructions.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112685163A (en) * | 2021-01-06 | 2021-04-20 | 北京信息科技大学 | Computing unloading method based on mobile edge computing and mobile edge computing server |
| CN112783567A (en) * | 2021-01-05 | 2021-05-11 | 中国科学院计算技术研究所 | DNN task unloading decision method based on global information |
| CN112867061A (en) * | 2021-04-26 | 2021-05-28 | 香港中文大学（深圳） | Resource allocation method and device |
| CN112995280A (en) * | 2021-02-03 | 2021-06-18 | 北京邮电大学 | Data distribution method and device for multi-content demand service |
| CN113344152A (en) * | 2021-04-30 | 2021-09-03 | 华中农业大学 | System and method for intelligently detecting and uploading full-chain production information of dairy products |
| CN116432082A (en) * | 2023-03-29 | 2023-07-14 | 武汉理工大学 | Ship fault feature analysis method, system and storage medium |
| CN116483585A (en) * | 2023-06-16 | 2023-07-25 | 江西师范大学 | Multi-service pre-deployment method based on edge computing |
| CN117119444A (en) * | 2023-10-25 | 2023-11-24 | 成都信息工程大学 | Position privacy protection method based on mobile edge calculation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152723A1 (en) * | 2016-03-07 | 2017-09-14 | 大唐移动通信设备有限公司 | Data transmission method, apparatus and system |
| CN107249218A (en) * | 2017-06-05 | 2017-10-13 | 东南大学 | Radio Resource and the combined distributing method of cloud resource in a kind of MEC |
| CN107682443A (en) * | 2017-10-19 | 2018-02-09 | 北京工业大学 | Joint considers the efficient discharging method of the mobile edge calculations system-computed task of delay and energy expenditure |
| CN108901075A (en) * | 2018-06-28 | 2018-11-27 | 电子科技大学 | A kind of resource allocation methods based on GS algorithm |
-
2019
- 2019-10-22 CN CN201911007166.5A patent/CN110740473B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017152723A1 (en) * | 2016-03-07 | 2017-09-14 | 大唐移动通信设备有限公司 | Data transmission method, apparatus and system |
| CN107249218A (en) * | 2017-06-05 | 2017-10-13 | 东南大学 | Radio Resource and the combined distributing method of cloud resource in a kind of MEC |
| CN107682443A (en) * | 2017-10-19 | 2018-02-09 | 北京工业大学 | Joint considers the efficient discharging method of the mobile edge calculations system-computed task of delay and energy expenditure |
| CN108901075A (en) * | 2018-06-28 | 2018-11-27 | 电子科技大学 | A kind of resource allocation methods based on GS algorithm |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112783567A (en) * | 2021-01-05 | 2021-05-11 | 中国科学院计算技术研究所 | DNN task unloading decision method based on global information |
| CN112685163A (en) * | 2021-01-06 | 2021-04-20 | 北京信息科技大学 | Computing unloading method based on mobile edge computing and mobile edge computing server |
| CN112685163B (en) * | 2021-01-06 | 2023-05-02 | 北京信息科技大学 | Calculation unloading method based on mobile edge calculation and mobile edge calculation server |
| CN112995280A (en) * | 2021-02-03 | 2021-06-18 | 北京邮电大学 | Data distribution method and device for multi-content demand service |
| CN112867061A (en) * | 2021-04-26 | 2021-05-28 | 香港中文大学（深圳） | Resource allocation method and device |
| CN113344152A (en) * | 2021-04-30 | 2021-09-03 | 华中农业大学 | System and method for intelligently detecting and uploading full-chain production information of dairy products |
| CN116432082A (en) * | 2023-03-29 | 2023-07-14 | 武汉理工大学 | Ship fault feature analysis method, system and storage medium |
| CN116432082B (en) * | 2023-03-29 | 2024-01-09 | 武汉理工大学 | Ship fault feature analysis method, system and storage medium |
| CN116483585A (en) * | 2023-06-16 | 2023-07-25 | 江西师范大学 | Multi-service pre-deployment method based on edge computing |
| CN116483585B (en) * | 2023-06-16 | 2023-09-15 | 江西师范大学 | Multi-service pre-deployment method based on edge computing |
| CN117119444A (en) * | 2023-10-25 | 2023-11-24 | 成都信息工程大学 | Position privacy protection method based on mobile edge calculation |
| CN117119444B (en) * | 2023-10-25 | 2024-01-16 | 成都信息工程大学 | Position privacy protection method based on mobile edge calculation |
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