CN111343238B - Method for realizing joint calculation and bandwidth resource allocation in mobile edge calculation - Google Patents

Method for realizing joint calculation and bandwidth resource allocation in mobile edge calculation Download PDF

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CN111343238B
CN111343238B CN202010084044.2A CN202010084044A CN111343238B CN 111343238 B CN111343238 B CN 111343238B CN 202010084044 A CN202010084044 A CN 202010084044A CN 111343238 B CN111343238 B CN 111343238B
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陈翔
邝巧斌
邱继云
龚杰
陈晓春
田华
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Shenzhen Research Institute Tsinghua University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04L67/00—Network arrangements or protocols for supporting network services or applications
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention discloses a method for realizing joint calculation and bandwidth resource allocation in mobile edge calculation, which is suitable for a mobile edge calculation wireless communication environment with short spectrum resources and calculation resources and high time delay requirements, and belongs to the field of wireless communication. The method comprises the following steps: firstly, a user sends an unloading request to a control center; then the control center distributes bandwidth resources and computing resources to all users requesting service according to the actual conditions of the users; and finally, calculating the system time delay. Aiming at the simple inverse proportional relation between the existing computing time and computing resources, the invention uses a general computing model to represent the relation between the computing power and the computing resources, thereby solving the problem that the prior representation relation has no generality; the method for realizing the joint calculation and the bandwidth resource allocation can better utilize the bandwidth resources and the calculation resources of the mobile edge calculation system, reduce the waiting time of users and further reduce the system time delay of the mobile edge calculation system.

Description

Joint calculation and bandwidth resource allocation realization method in mobile edge calculation
Technical Field
The invention relates to the technical field of resource allocation of wireless communication, in particular to a joint calculation and bandwidth resource allocation implementation method based on a general calculation model in mobile edge calculation.
Background
With the emergence of novel mobile applications such as face recognition, interactive games, augmented reality, autonomous driving, and the like, the requirements for computing power and low time delay are increasing. However, due to the physical limitations of mobile users, their resources are often limited. Mobile Edge Computing (MEC) deploys computing resources at an Access Point (AP) to ease the computing task of mobile users, which is a promising solution because it not only provides enough computing power at the mobile edge, but also shortens the computing time.
In the field of moving edge computing, a great deal of literature is available to study the computational offload problem. The computation time is inversely proportional to the allocated computational resources in most studies. However, in practice, some parts of the tasks are highly integrated and cannot be divided, and the output of some parts is the input of other parts, so that some tasks cannot be processed completely in parallel. Even if a large amount of parallel computing resources are allocated to one user, some of the parallel computing resources are not utilized due to the division limitation of the user task, i.e., the computing time is not reduced as the allocated computing resources are increased. Therefore, it is necessary to research the joint computation and bandwidth resource allocation implementation method based on the general computation model of the mobile edge computation.
Disclosure of Invention
The present invention aims to solve the above-mentioned drawbacks in the prior art, and provides a method for implementing joint computation and bandwidth resource allocation of a generic computation model in mobile edge computation.
The purpose of the invention can be achieved by adopting the following technical scheme:
a joint calculation and bandwidth resource allocation implementation method in mobile edge calculation comprises the following specific operation steps:
s1, initializing parameters, initializing the number K of users and the size l of a task needing to be unloaded and calculated by each user i belonging to {1, \8230;, K } i Initiating a computing task l i The required number w of Central Processing Unit (CPU) cycles i User i's transmission power p i Channel to user i power gain h i And power noise N of the channel 0 Initializing the bandwidth B available to the entire mobile edge computing system, the computing resources F available to the mobile edge computing server, initializing the general computing model g () of computing power and computing resources, g () being a concave non-decreasing nonlinear function such that c i ＝g(f i ) Wherein f is i Indicating the computing resources allocated to user i by the mobile edge computing server, c i Representing a computational resource f i Corresponding calculation(ii) a capability;
s2, the user sends an unloading request to the control center and simultaneously transmits relevant parameters of the user to the control center, wherein the unloading request comprises the task size l of unloading calculation i The number of CPU cycles w required for the calculation task i The transmission power p of the user i Power gain h of the channel i And power noise N of the channel 0 ；
S3, the control center allocates bandwidth resources and computing resources to all users requesting service according to the relevant parameters of the users;
s4, simultaneously transmitting respective unloading tasks by the users according to the allocated bandwidth resources, and calculating the unloading time of each user, wherein the unloading time represents the time required for unloading the tasks of the users to the mobile edge computing server;
s5, enabling the corresponding virtual machine to complete the corresponding computing task by the mobile edge computing server according to the computing resources distributed to each user, and computing the computing time required by each user, wherein the computing time represents the time required by the mobile edge computing server to complete the computing task of the specified user;
s6, the mobile edge calculation server feeds back the calculated result to the user, calculates feedback time, and defines the feedback time as the time consumed by the mobile edge calculation server for transmitting the calculated result to the user;
and S7, calculating system time delay, wherein the system time delay is defined as the maximum value of the total time of all users for completing the calculation tasks, and the total time of the calculation tasks is composed of unloading time, calculation time and feedback time.
Further, the step S3 is specifically as follows:
s31, defining two groups of variables b by the control center i And f i I ∈ {1, \8230;, K }, where b i Representing bandwidth resources allocated to user i, f i Representing the computational resources allocated to user i, and then these two sets of variables will be solved by the following steps;
s32, the control center determines an expression r of the data rate of each user i i ＝b i log(1+h i p i /N 0 ) Then determine the unload time expression t for each user i i ＝l i /r i Time to unload t i Representing the time required to offload the task of user i to the mobile edge computing server;
s33, the control center combines the computing power and the computing resource model g () to determine a computing power expression c distributed to the user i i ＝g(f i ) And determining that the mobile edge computing server completes the computing task l i Expression of required computation time
Figure BDA0002381391490000031
Defining the total time for each user i to complete its computing task
Figure BDA0002381391490000032
Wherein
Figure BDA0002381391490000033
Representing a computational task l i The result feedback time of (1);
s34, ignoring the total time T for each user to complete the calculation task i Time of feedback in
Figure BDA0002381391490000034
Get the expression
Figure BDA0002381391490000035
Defining the system time delay as the total time T for all users to complete the calculation task i Is denoted as max (T) i )；
S35, constructing a convex optimization problem by the control center
Figure BDA0002381391490000036
s.t.
Figure BDA0002381391490000037
Figure BDA0002381391490000038
Wherein s.t. is an abbreviation for subject to, representing a constraint in a convex optimization problem,
Figure BDA0002381391490000039
Figure BDA00023813914900000310
the convex optimization problem indicates that the sum of bandwidth resources and the sum of computing resources allocated to all K users do not exceed the available bandwidth resources and the available computing resources of the mobile edge computing server respectively, the convex optimization problem indicates that 'the optimal bandwidth and computing resources allocated to each user by the mobile edge computing server are adjusted to obtain the minimum system time delay', an interior point method in the convex optimization technology is used for solving, and a variable b is solved i And f i I ∈ {1, \8230;, K }, resulting in an optimal bandwidth and computing resource allocation scheme for the mobile edge computing server to each user.
Compared with the prior art, the invention has the following advantages and effects:
1. the method for realizing joint calculation and bandwidth resource allocation in mobile edge calculation disclosed by the invention has the advantages that the relation between the calculation capacity and the calculation resource is expressed by using a general calculation model, and the existing simple direct proportional relation is replaced, so that the method is more general.
2. Compared with the prior serial processing, the method for realizing the joint calculation and the bandwidth resource allocation in the mobile edge calculation can better utilize the bandwidth resource and the calculation resource of the mobile edge calculation system and reduce the waiting time of users, thereby reducing the system time delay of the mobile edge calculation system.
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FIG. 1 is a flowchart of the implementation method of joint computation and bandwidth resource allocation based on mobile edge computation according to the present invention;
FIG. 2 is a flowchart of the steps involved in initializing parameters and models and sending an unload request from a user to a control center and transmitting the relevant parameters in accordance with the present invention;
FIG. 3 is a flowchart of the steps of the present invention for allocating bandwidth resources and computational resources to all users requesting services by the control center;
FIG. 4 is a graph of the performance gain of linear and general non-linear computational resource models for different numbers of users in 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.
Example one
The embodiment discloses a joint calculation and bandwidth resource allocation implementation method of a general calculation model based on mobile edge calculation, which comprises the following specific operation steps:
s1, initializing parameters, initializing the number K of users and the size l of a task needing to be unloaded and calculated by each user i belonging to {1, \8230;, K } i Initiating a computing task l i Number of CPU cycles required w i User i's transmission power p i Power gain h of channel to user i i And power noise N of the channel 0 Initializing the bandwidth B available to the entire mobile edge computing system, the computing resources F available to the mobile edge computing server, initializing the general computing model g () of computing power and computing resources, g () being a concave non-decreasing nonlinear function such that c i ＝g(f i ) Wherein f is i Indicating the computing resources allocated to user i by the mobile edge computing server, c i Representing a computational resource f i The corresponding computing power;
s2, user direction control centerSending an unloading request and simultaneously transmitting relevant parameters of a user to a control center, wherein the relevant parameters comprise the task size l of unloading calculation i The number of CPU cycles w required for the calculation task i The transmission power p of the user i Power gain h of the channel i And power noise N of the channel 0 ；
S3, the control center allocates bandwidth resources and computing resources to all users requesting service according to the relevant parameters of the users;
in this embodiment, the steps are specifically as follows:
s31, defining two groups of variables b by the control center i And f i I ∈ {1, \8230;, K }, where b i Representing bandwidth resources allocated to user i, f i Representing the computational resources allocated to user i, the two sets of variables are solved by the following steps;
s32, the control center determines an expression r of the data rate of each user i i ＝b i log(1+h i p i /N 0 ) Then determine the unload time expression t for each user i i ＝l i /r i Time to unload t i Representing the time required to offload the task of user i to the mobile edge computing server;
s33, the control center combines the computing power and the computing resource model g () to determine a computing power expression c distributed to the user i i ＝g(f i ) And determining that the mobile edge computing server completes the computing task l i Expression of required computation time
Figure BDA0002381391490000061
Defining the total time for each user i to complete its computational task
Figure BDA0002381391490000062
Wherein
Figure BDA0002381391490000063
Representing a computational task l i The result feedback time of (1);
s34, ignoring each user to complete the calculation taskTotal time T of i Time of feedback in
Figure BDA0002381391490000064
Get the expression
Figure BDA0002381391490000065
Defining the system time delay as the total time T of all users to complete the calculation task i Is denoted as max (T) i )；
S35, constructing a convex optimization problem by the control center
Figure BDA0002381391490000066
s.t.
Figure BDA0002381391490000067
Figure BDA0002381391490000068
Wherein s.t. is an abbreviation for subject to, representing a constraint in a convex optimization problem,
Figure BDA0002381391490000069
Figure BDA00023813914900000610
indicating that the sum of the bandwidth resources and the sum of the computing resources allocated to all the K users do not exceed the available bandwidth resources and the available computing resources of the mobile edge computing server, respectively. The convex optimization problem means "get the minimum system delay by adjusting the optimal bandwidth and computing resource allocation of the mobile edge computing server to each user". Solving by using an interior point method in a convex optimization technology to obtain a variable b i And f i I ∈ {1, \8230;, K }, resulting in an optimal bandwidth and computing resource allocation scheme for the mobile edge computing server to each user.
S4, simultaneously transmitting respective unloading tasks by the users according to the allocated bandwidth resources, and calculating the unloading time of each user, wherein the unloading time represents the time required for unloading the tasks of the users to the mobile edge computing server;
s5, enabling the corresponding virtual machine to complete the corresponding computing task by the mobile edge computing server according to the computing resources distributed to each user, and computing the computing time required by each user, wherein the computing time represents the time required by the mobile edge computing server to complete the computing task of the specified user;
s6, the mobile edge calculation server feeds back the calculated result to the user, calculates feedback time, and defines the feedback time as the time consumed by the mobile edge calculation server for transmitting the calculated result to the user;
and S7, calculating system time delay, wherein the system time delay is defined as the maximum value of the total time of all users for completing the calculation tasks, and the total time of the calculation tasks is composed of unloading time, calculation time and feedback time.
In summary, the key of the joint computing and bandwidth resource allocation method is that the computing tasks of all users can be serviced at the same time, i.e. the tasks of all users are processed in parallel. In the general computing model, due to the limitation of user task division, even if a user is allocated a large amount of parallel computing resources to process its tasks in parallel, the computing resources cannot be fully utilized. When the system provides parallel services for all users, the parallel computing resources can be more fully utilized, thereby better utilizing the bandwidth resources and the computing resources in the mobile edge computing system.
Example two
The embodiment of the present invention will describe in detail the implementation method of joint computation and bandwidth resource allocation based on a general computation model in mobile edge computation in conjunction with fig. 1 to fig. 4 and with an embodiment of a mobile edge computing system specifically including multiple users and a server.
Consider the system model as follows: in a mobile edge computing system, there is a mobile edge computing server, the maximum computing resource F =5GHz, mobile edge meterThe total bandwidth of the computing system is B =40MHz, and the distance d between the user and the server obeys [120,150%]The rice is dispersed and evenly distributed, and the size of the task for unloading calculation is l i Following a uniform distribution of (0, 1) Mbits, the number of CPU cycles w required to compute a task i Satisfies the interval [1,3]×10 9 Discrete uniform distribution of cycles, the path loss between the user and the server is denoted β 0 (d/d 0 ) -ζ Wherein beta is 0 = 60dB for a reference distance d 0 Path loss at =10 meters, d represents the distance of the user from the server, and ζ =3 is the path loss exponent. General non-linear calculation model
Figure BDA0002381391490000071
The linear calculation model of the comparison is g l (f)＝0.98f。
Each user sends an offload request to the control center, which assumes that the bandwidth resource allocated to user i is b i The computing resource is f i The expression for calculating the time required for each user to unload and the expression for calculating the time required for each user to complete their calculation tasks, the total time for each user to complete their tasks mainly comprises these two parts of time, since the feedback time is negligible compared to the unloading time and the calculation time. And constructing a convex optimization problem by using the system time delay minimization problem, and solving by using an interior point method in a convex optimization technology to obtain an optimal bandwidth and computing resource allocation scheme.
FIG. 4 illustrates the performance gains of linear and general non-linear computational resource models at different numbers of users, where the performance gains of the linear and general models are expressed as η l,gain ＝1-T l /T l ' and eta log,gain ＝1-T log /T′ log . Wherein T is l And T log The linear and general non-linear computation resource models correspond to the system delay of the proposed scheme, respectively, and the linear and general non-linear computation resource models correspond to the system delay of the conventional scheme (stochastic serial processing), respectively, by T l 'and T' log And (4) showing. As shown, the general model of computing resources isThe gain is provided for the number of users, and the gain amplitude can reach 1 along with the increase of the number of users.
In summary, the implementation method for joint computation and bandwidth resource allocation in mobile edge computing proposed in the above embodiments can better utilize bandwidth resources and computation resources of a mobile edge computing system than the existing serial processing scheme, and reduce the waiting time of a user, thereby reducing the system delay of the mobile edge computing system.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (2)

1. A joint calculation and bandwidth resource allocation implementation method in mobile edge calculation is characterized in that the joint calculation and bandwidth resource allocation implementation method comprises the following steps:
s1, initializing parameters, namely initializing the number K of users and the size l of a task of each user i belonging to { 1., K } needing to be subjected to unloading calculation i Initiating a computing task l i Number of CPU cycles required w i User i's transmission power p i Channel to user i power gain h i And power noise N of the channel 0 Initializing the bandwidth B available to the entire mobile edge computing system, the computing resources F available to the mobile edge computing server, initializing the general computing model g () of computing power and computing resources, g () being a concave non-decreasing nonlinear function such that c i ＝g(f i ) Wherein f is i Indicating the computing resources allocated to user i by the mobile edge computing server, c i Representing a computational resource f i The corresponding computing power;
s2, the user sends an unloading request to the control center and simultaneously transmits relevant parameters of the user to the control center, wherein the unloading request comprises the task size l of unloading calculation i Calculating the number of CPU cycles required for the taskMesh w i The transmission power p of the user i Power gain h of the channel i And power noise N of the channel 0 ；
S3, the control center allocates bandwidth resources and computing resources to all users requesting service according to the relevant parameters of the users;
s4, simultaneously transmitting respective unloading tasks by the users according to the allocated bandwidth resources, and calculating the unloading time of each user, wherein the unloading time represents the time required for unloading the tasks of the users to the mobile edge computing server;
s5, enabling the corresponding virtual machine to complete the corresponding computing task and computing the computing time required by each user by the mobile edge computing server according to the computing resources distributed to each user, wherein the computing time represents the time required by the mobile edge computing server to complete the computing task of the specified user;
s6, the mobile edge computing server feeds back the computed result to the user and computes feedback time, and the feedback time is defined as time consumed by the mobile edge computing server to transmit the computed result to the user;
and S7, calculating the system time delay, and defining the system time delay as the maximum value of the total time of finishing the calculation tasks by all users, wherein the total time of the calculation tasks consists of unloading time, calculation time and feedback time.
2. The method for implementing joint computation and bandwidth resource allocation in mobile edge computation according to claim 1, wherein the step S3 is specifically as follows:
s31, defining two groups of variables b by the control center i And f i I ∈ {1,. K }, where b i Representing bandwidth resources allocated to user i, f i Representing the computing resources allocated to user i;
s32, the control center determines an expression r of the data rate of each user i i ＝b i log(1+h i P i /N 0 ) Then determine the unload time expression t for each user i i ＝l i /r i Time to unload t i Representing the time required to offload the task of user i to the mobile edge computing server;
s33, the control center combines the computing power and the computing resource model g () to determine a computing power expression c distributed to the user i i ＝g(f i ) And determining that the mobile edge computing server completes the computing task l i Expression of required computation time
Figure FDA0002381391480000021
Defining the total time for each user i to complete its computational task
Figure FDA0002381391480000022
Wherein
Figure FDA0002381391480000023
Representing a computational task l i The result feedback time of (1);
s34, ignoring the total time T for each user to complete the calculation task i Time of feedback in
Figure FDA0002381391480000024
Get the expression
Figure FDA0002381391480000025
Defining the system time delay as the total time T of all users to complete the calculation task i Is denoted as max (T) i )；
S35, the control center constructs a convex optimization problem
Figure FDA0002381391480000026
Figure FDA0002381391480000027
Figure FDA0002381391480000028
Wherein s.t. is an abbreviation for subject to, representing a constraint in a convex optimization problem,
Figure FDA0002381391480000029
Figure FDA0002381391480000031
the convex optimization problem indicates that the sum of bandwidth resources and the sum of computing resources distributed to all K users do not exceed the available bandwidth resources and the available computing resources of the mobile edge computing server respectively, the convex optimization problem indicates that the optimal bandwidth and computing resources distributed to each user by the mobile edge computing server are adjusted to obtain the minimum system delay, an interior point method in the convex optimization technology is used for solving, and a variable b is solved i And f i I ∈ { 1.,. K }, so as to obtain an optimal bandwidth and computing resource allocation scheme of the mobile edge computing server to each user.
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