CN107995660B - Joint task scheduling and resource allocation method supporting D2D-edge server unloading - Google Patents
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Abstract
The invention relates to a joint task scheduling and resource allocation method supporting D2D-edge server unloading, and belongs to the technical field of wireless communication. The method comprises the following steps: step 1) modeling user joint overhead; step 2) modeling user task execution time delay; step 3) modeling user task execution energy consumption; step 4), modeling user task scheduling and resource allocation limiting conditions; and 5) determining a user task scheduling and resource allocation strategy based on the minimization of the user joint overhead. The invention can realize the minimization of task execution overhead by optimizing and determining the user task scheduling and resource allocation strategy.
Description
Technical Field
The invention belongs to the technical field of wireless communication, and relates to a joint task scheduling and resource allocation method supporting D2D-edge server unloading.
Background
With the rapid development of mobile internet and the popularization of intelligent terminals, the requirements of applications such as Augmented Reality (AR), Virtual Reality (VR), and mobile high definition video on Quality of Service (QoS) are increasing. However, the limitation of the processor resources of the smart device and the shortage of the traditional Mobile Cloud Computing (MCC) network architecture result in that the whole network cannot meet the business requirement of processing a large amount of data in a short time, and in addition, the high power consumption of the Mobile device also seriously affects the service Experience (QoE) of the user. The method promotes the marginal deployment of the cloud server and the fusion with the base station, and provides support for the service requirements of low time delay and low power consumption
In the existing research, there is a literature that an unloading strategy is designed for a multi-user unloading scene, energy consumption of users is minimized on the premise of meeting the maximum allowable execution delay, and the unloading strategy of each user is obtained by solving the optimal power allocation and the optimal computing resource allocation of each user. For another example, there is a literature that researches on minimizing execution delay by using Dynamic Frequency and Voltage Scaling (DFVS) and energy harvesting techniques, and proposes a Dynamic computation offload algorithm based on lyapunov optimization, which first makes a binary offload decision in units of time slots, and then allocates computation resources to locally executed users or allocates power to offloaded users.
Existing resource allocation schemes based on task-off user network scenarios are less studied in view of cellular D2D network scenarios, however, heterogeneous characteristics of access networks may present difficulties and challenges to resource allocation approaches. In addition, in the existing resource allocation research, time delay reduction is considered more, compromise between time delay and energy consumption is executed in fewer research tasks, which may result in increased network energy consumption, and transmission performance and user experience are difficult to guarantee for energy efficiency sensitive user equipment.
Disclosure of Invention
In view of this, the present invention aims to provide a method for joint task scheduling and resource allocation supporting D2D-edge server offloading, assuming that a user needs to execute a certain computation task, both the mobile edge computation server and the D2D have certain task computation and processing capabilities for the end user, the user may use local execution, or may use the cellular mobile edge computation server or the D2D end to implement task offloading, modeling the user joint cost is an optimization objective, and implement joint optimization allocation of user task scheduling, communication resources and computation resources.
In order to achieve the purpose, the invention provides the following technical scheme:
the joint task scheduling and resource allocation method for supporting D2D-edge server unloading comprises the following steps:
s1: modeling user joint overhead;
s2: modeling time delay required by user task execution;
s3: modeling energy consumption required by user task execution;
s4: modeling user task scheduling and resource allocation limiting conditions;
s5: and determining a user task scheduling and resource allocation strategy based on the minimization of the user joint overhead.
Further, the step S1 specifically includes: according to the formula
Modeling user joint overheads
The sum of the overhead of performing tasks for all users in the network, wherein,
the cost required by the ith user to execute the task is more than or equal to 1 and less than or equal to N, and N is the number of users to execute the task in the network;
Further, step S2 specifically includes: according to the formula ti＝max{ti,L,ti,B,ti,DModeling the time delay needed by the execution of the ith user task, wherein ti,LIndicates the time delay t needed by the ith user to execute the task locallyi,BThe time delay required by the ith user to unload the task to the base station mobile edge computing server is represented, ti,DRepresenting the time delay required for the ith user to unload the task to the D2D user for execution;
ti,Lis modeled as
Wherein λ isi,LRepresenting the proportion of the amount of tasks performed locally by the ith user, DiRepresenting the amount of computing resources required by the ith user to perform the task, fiRepresents the CPU frequency of the ith user; said t isi,BIs modeled as
Wherein x isi,BScheduling decision identifier, x, representing the offloading of the ith user task to the base station mobile edge computing serveri,B1 means that the ith user unloads the task to the base station mobile edge computing server for execution, otherwise xi,B＝0，ZiRepresenting the amount of data, R, of the ith user's task to be performedi,BIndicating the transmission rate, mu, of the link between the ith user and the base stationiThe calculation resource proportion of the base station moving edge calculation server distributed by the ith user is represented, and F represents the total calculation resource amount of the base station moving edge calculation server; said t isi,DIs modeled as
Wherein x isi,jScheduling decision identifier, x, indicating the offloading of the ith user task to the jth D2D useri,j1 means that the ith user offloads the task to the jth D2D user for execution, otherwise xi,j＝0，Ri,DIndicating the transmission rate of the link between the ith user and the D2D user,
representing the CPU frequency of the jth D2D user, j is more than or equal to 1 and less than or equal to M, and M is the number of D2D users in the network;
the R isi,BIs modeled as
Wherein eta isiRepresents the bandwidth resource proportion W allocated by the base station for the ith userBRepresenting the transmission bandwidth of the base station, piIndicating the transmission power of the ith user task data, gi,BIndicating the channel gain, σ, of the link between the ith user and the base station2Is the transmission channel noise power; the R isi,DIs modeled as
Wherein, WDRepresents the transmission bandwidth, g, of the D2D linki,jIndicating the channel gain of the link between the ith user and the jth D2D user.
Further, the step S3 specifically includes: according to the formula ei＝ei,L+ei,B+ei,DModeling energy consumption required for the execution of the ith user task, wherein ei,LIndicating the energy consumption required for the ith user to perform the task locally, ei,BIndicating the energy consumption required for the ith user to unload the task to the base station mobile edge computing server for execution, ei,DIndicating that the ith user offloads the task to the energy consumption required by the D2D user to execute;
said ei,LIs modeled as
Wherein δ represents an effective capacitance coefficient related to the CPU chip structure; said ei,BIs modeled as
Said ei,DIs modeled as
Further, the step S4 specifically includes: modeling user task scheduling and resource allocation constraints, wherein the task scheduling constraints are modeled as xi,B∈{0,1}，xi,j∈{0,1}，λi,L∈[0,1]，
And
the task unloading data transmission rate limiting condition is modeled as
And
wherein,
representing the lowest transmission rate of the ith user task, the resource allocation constraint is modeled as
And
further, the step S5 specifically includes: determining a user task scheduling and resource allocation strategy based on the minimization of the user joint overhead: under the condition of meeting the task scheduling and resource allocation limiting conditions, the user task scheduling and resource allocation strategy is optimized and determined by taking the minimization of the user joint overhead as a target, namely
The invention has the beneficial effects that: the invention can ensure that the user task scheduling strategy is optimal under the condition of effective task execution, the communication and calculation resource distribution is optimal, and the user overhead minimization is realized.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic diagram of a network supporting D2D-edge server offloading;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention provides a joint task scheduling and resource allocation method supporting D2D-edge server unloading, supposing that a user needs to execute a certain calculation task, a mobile edge calculation server and a D2D opposite-end user both have certain task calculation and processing capabilities, the user can execute locally, or realize task unloading through the mobile edge calculation server or the D2D opposite end, modeling user joint cost is an optimization target, and joint optimization of user task scheduling and communication resource and calculation resource allocation strategies is realized.
The joint task scheduling and resource allocation method supporting D2D-edge server unloading provided by the invention assumes that in a network with cellular communication and D2D communication coexisting, two different networks and the inside of the same access network adopt an orthogonal multiple access mode, so that task transmission is free of interference; a plurality of users to be executed tasks and D2D users exist in the network, and the users to be executed tasks can select proper ways to unload the tasks; and modeling user joint overhead is the sum of the total overhead of all users executing tasks in the network, and optimizing and realizing task scheduling and resource allocation strategies based on the user joint overhead.
As shown in fig. 1, there are multiple users to be executed with tasks in the network, and the users select a suitable manner to unload the tasks, and minimize the task execution overhead by optimizing the user task scheduling policy and the resource allocation policy.
As shown in fig. 2, the method of the present invention specifically includes the following steps:
1) modeling user joint overhead;
modeling user joint spending, in particular according to formula
Modeling user joint overheads
The sum of the overhead of performing tasks for all users in the network, wherein,
modeling for the cost required by the ith user to execute the task, wherein i is more than or equal to 1 and less than or equal to N, N is the number of users to execute the task in the network
Is composed of
Wherein, tiIndicating the delay required for the execution of the ith user task, eiIndicating the energy consumption required by the ith user to perform the task,
a weighting factor representing the delay overhead of the ith user,
and the weighting coefficient represents the energy consumption cost of the ith user.
2) Modeling time delay required by user task execution;
modeling the time delay required by the execution of the user task, specifically according to a formula ti＝max{ti,L,ti,B,ti,DModeling the time delay needed by the execution of the ith user task, wherein ti,LIndicates the time delay t needed by the ith user to execute the task locallyi,BThe time delay required by the ith user to unload the task to the base station mobile edge computing server is represented, ti,DRepresenting the time delay required by the ith user to unload the task to the D2D user for execution, and modeling ti,LIs composed of
Wherein λ isi,LRepresenting the proportion of the amount of tasks performed locally by the ith user, DiRepresenting the amount of computing resources required by the ith user to perform the task, fiRepresenting the CPU frequency of the ith user, model ti,BIs composed of
Wherein x isi,BScheduling decision identifier, x, representing the offloading of the ith user task to the base station mobile edge computing serveri,B1 means that the ith user unloads the task to the base station mobile edge computing server for execution, otherwise xi,B＝0，ZiDenotes the ithAmount of data, R, of a task to be performed by an individual useri,BIndicating the transmission rate, mu, of the link between the ith user and the base stationiThe calculation resource proportion of the base station moving edge calculation server distributed by the ith user is represented, and F represents the total calculation resource amount of the base station moving edge calculation server; modeling ti,DIs composed of
Wherein x isi,jScheduling decision identifier, x, indicating the offloading of the ith user task to the jth D2D useri,j1 means that the ith user offloads the task to the jth D2D user for execution, otherwise xi,j＝0，Ri,DIndicating the transmission rate of the link between the ith user and the D2D user,
representing the CPU frequency of the jth D2D user, j is more than or equal to 1 and less than or equal to M, M is the number of D2D users in the network, and R is modeledi,BIs composed of
Wherein eta isiRepresents the bandwidth resource proportion W allocated by the base station for the ith userBRepresenting the transmission bandwidth of the base station, piIndicating the transmission power of the ith user task data, gi,BIndicating the channel gain, σ, of the link between the ith user and the base station2Modeling R for transmission channel noise poweri,DIs composed of
Wherein, WDRepresents the transmission bandwidth, g, of the D2D linki,jIndicating the channel gain of the link between the ith user and the jth D2D user.
3) Modeling energy consumption required by user task execution;
modeling energy consumption required by user task execution, specifically according to formula ei＝ei,L+ei,B+ei,DModeling energy consumption required for the execution of the ith user task, wherein ei,LIndicating the energy consumption required for the ith user to perform the task locally, ei,BIs shown asi users offloading tasks to the base station mobile edge computing server for execution, ei,DRepresenting the energy consumption required by the ith user to unload the task to the D2D user for execution, model ei,LIs composed of
Where δ represents the effective capacitance coefficient associated with the CPU chip structure, model ei,BIs composed of
Modeling ei,DIs composed of
4) Modeling user task scheduling and resource allocation limiting conditions;
modeling user task scheduling and resource allocation limiting conditions, specifically, modeling the task scheduling limiting conditions as xi,B∈{0,1}，xi,j∈{0,1}，λi,L∈[0,1]，
And
the task unloading data transmission rate limiting condition is modeled as
And
wherein,
representing the lowest transmission rate of the ith user task, the resource allocation constraint is modeled as
And
5) determining a user task unloading and resource allocation strategy based on the minimization of the user joint overhead;
determining a user task scheduling and resource allocation strategy based on the minimization of the user joint overhead, specifically, optimizing and determining the user task scheduling and resource allocation strategy by taking the minimization of the user joint overhead as a target under the condition of meeting the limitation of task scheduling and resource allocation, namely
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.
Claims (5)
1. The joint task scheduling and resource allocation method supporting D2D-edge server unloading is characterized in that: the method comprises the following steps:
s1: modeling user joint overhead;
s2: modeling time delay required by user task execution;
s3: modeling energy consumption required by user task execution;
s4: modeling user task scheduling and resource allocation limiting conditions;
s5: determining a user task scheduling and resource allocation strategy based on the minimization of user joint overhead under the condition of meeting task scheduling and resource allocation;
the S1 specifically includes: according to the formula
Modeling user joint overheads
For all users in the networkThe sum of the overhead of executing the tasks, wherein,
the cost required by the ith user to execute the task is more than or equal to 1 and less than or equal to N, and N is the number of users to execute the task in the network;
2. The method of claim 1, wherein the joint task scheduling and resource allocation method supporting D2D-edge server offloading comprises: the S2 specifically includes: according to the formula ti＝max{ti,L,ti,B,ti,DModeling the time delay needed by the execution of the ith user task, wherein ti,LIndicates the time delay t needed by the ith user to execute the task locallyi,BThe time delay required by the ith user to unload the task to the base station mobile edge computing server is represented, ti,DRepresenting the time delay required for the ith user to unload the task to the D2D user for execution;
ti,Lis modeled as
Wherein λ isi,LRepresenting the proportion of the amount of tasks performed locally by the ith user, DiRepresenting the amount of computing resources required by the ith user to perform the task, fiRepresents the CPU frequency of the ith user; t is ti,BIs modeled as
Wherein x isi,BScheduling decision identifier, x, representing the offloading of the ith user task to the base station mobile edge computing serveri,B1 means that the ith user unloads the task to the base station mobile edge computing server for execution, otherwise xi,B＝0，ZiRepresenting the amount of data, R, of the ith user's task to be performedi,BIndicating the transmission rate, mu, of the link between the ith user and the base stationiThe calculation resource proportion of the base station moving edge calculation server distributed by the ith user is represented, and F represents the total calculation resource amount of the base station moving edge calculation server; t is ti,DIs modeled as
Wherein x isi,jScheduling decision identifier, x, indicating the offloading of the ith user task to the jth D2D useri,j1 means that the ith user offloads the task to the jth D2D user for execution, otherwise xi,j＝0，Ri,DIndicating the transmission rate of the link between the ith user and the D2D user,
representing the CPU frequency of the jth D2D user, j is more than or equal to 1 and less than or equal to M, and M is the number of D2D users in the network;
Ri,Bis modeled as
Wherein eta isiRepresents the bandwidth resource proportion W allocated by the base station for the ith userBRepresenting the transmission bandwidth of the base station, piIndicating the transmission power of the ith user task data, gi,BIndicating the channel gain, σ, of the link between the ith user and the base station2Is the transmission channel noise power; ri,DIs modeled as
Wherein, WDRepresents the transmission bandwidth, g, of the D2D linki,jIndicating the channel gain of the link between the ith user and the jth D2D user.
3. The method of claim 2, wherein the joint task scheduling and resource allocation method supporting D2D-edge server offloading comprises: the S3 specifically includes: according to the formula ei＝ei,L+ei,B+ei,DModeling energy consumption required for the execution of the ith user task, wherein ei,LIndicating the energy consumption required for the ith user to perform the task locally, ei,BIndicating the energy consumption required for the ith user to unload the task to the base station mobile edge computing server for execution, ei,DIndicating that the ith user offloads the task to the energy consumption required by the D2D user to execute;
ei,Lis modeled as ei,L＝λi,LDiδfi 2Wherein δ represents an effective capacitance coefficient associated with the CPU chip architecture; e.g. of the typei,BIs modeled as
ei,DIs modeled as
4. The method of claim 3, wherein the joint task scheduling and resource allocation method supporting D2D-edge server offloading comprises: the S4 specifically includes: modeling user task scheduling and resource allocation constraints, wherein the task scheduling constraints are modeled as xi,B∈{0,1}，xi,j∈{0,1}，λi,L∈[0,1]，
And
the task unloading data transmission rate limiting condition is modeled as
And
wherein,
representing the lowest transmission rate of the ith user task, the resource allocation constraint is modeled as
And
5. the method of claim 4, wherein the joint task scheduling and resource allocation method supporting D2D-edge server offloading comprises: the S5 specifically includes: determining a user task scheduling and resource allocation strategy based on the minimization of the user joint overhead: under the condition of meeting the task scheduling and resource allocation limiting conditions, the user task scheduling and resource allocation strategy is optimized and determined by taking the minimization of the user joint overhead as a target, namely
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