CN116367310A - Maximum gain oriented channel allocation method for mobile edge calculation - Google Patents
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Abstract
The invention discloses a maximum gain oriented channel allocation method for mobile edge calculation, which belongs to the technical field of network management, and aims to research the influence of a channel allocation strategy on system unloading delay and energy consumption and construct an unloading optimization problem in a mobile edge calculation system model so as to minimize system unloading loss; calculating the unloading loss of each user, and determining the optimal channel of each user; allocating channels for users with the maximum unloading gain in each iteration through an allocation strategy guided by the maximum gain, deleting the allocated users and channels from a user set to be allocated and an unoccupied channel set, and repeating the process until the user set to be allocated is empty; and according to the obtained user channel allocation strategy, the user finishes unloading. The method of the invention can obtain higher system performance under the condition of reducing the implementation complexity.
Description
Technical Field
The invention relates to the technical field of network management, in particular to a maximum gain oriented channel allocation method for mobile edge calculation.
Background
Mobile edge computing is a very promising technology proposed in recent years that deploys edge servers near the network edge of users, so users do not need to upload tasks to a distant cloud server, but only to offload to a close-range edge server. As a distributed node, the edge server can efficiently and conveniently provide service for nearby users, and meanwhile, the privacy of data is also protected to a certain extent. In the context of the development of the internet of things, research into mobile edge computing systems is very necessary and has received extensive attention.
Offloading resource allocation is an important research direction for mobile edge computing systems, whose solutions will directly impact the user experience, which requires an efficient offloading decision scheme to coordinate competition between mobile devices. Furthermore, due to the heterogeneity of mobile edge computing systems and the scarcity of edge server computing resources, the task offloading decision of mobile edge computing systems will face greater challenges.
The existing research works are mostly based on traditional convex optimization methods and on machine learning methods. Among them, the convex optimization method has high algorithm complexity, poor robustness and low efficiency when facing to a large-scale mobile edge computing network, but the accuracy of the machine learning-based method cannot be guaranteed, and the model is usually required to be retrained after the environment changes, which brings about huge training cost.
Disclosure of Invention
The invention provides a maximum gain-oriented channel allocation method for mobile edge calculation, which improves the greedy strategy of the traditional greedy algorithm, and preferentially allocates channels to users with maximum unloading gain in each iteration process, thereby realizing the improvement of system performance.
The embodiment of the invention provides a maximum gain oriented channel allocation method facing to mobile edge calculation, which comprises the following steps: constructing a user set to be allocated and an unoccupied channel set, and establishing an unloading optimization problem with the aim of minimizing system unloading loss according to the influence of a channel allocation strategy on the unloading delay and energy consumption of a mobile edge computing system; calculating the unloading loss of each user for unloading under different channels based on the unloading optimization problem, and taking the channel with the minimum unloading loss as the optimal channel of the corresponding user; and calculating the unloading gain of each user unloaded under the optimal channel compared with other channels, distributing the optimal channel for the user corresponding to the maximum unloading gain, updating the user set to be distributed and the unoccupied channel set, and recalculating the optimal channel of each user to continue to distribute until all users in the user set to be distributed are distributed with the optimal channel.
Optionally, in one embodiment of the present invention, before establishing the offload optimization problem, the method further comprises: the mobile edge computing system is constructed, wherein the mobile edge computing system comprises a base station provided with an edge server, a plurality of single antenna users and a plurality of orthogonal channels.
Optionally, in one embodiment of the present invention, when offloading, each user selects only one channel for offloading and each channel only allows one user to occupy, where the impact on the offloading delay and energy consumption of the mobile edge computing system according to the channel allocation policy is aimed at minimizing the system offloading loss, and establishing an offloading optimization problem includes:
constructing the minimum system unloading loss objective function:
wherein ψ (x) is the system unloading loss, parameter λ t Weighting parameter lambda of system delay to objective function in current scene e The weight parameter of the energy consumption to the objective function in the current scene,
t is the maximum transmission and delay for simultaneous transmission over all channels k For the offloading delay of the transmission over channel k, < >>
D m For the task data size of user m, R mk Transmission rate offloaded for user m via channel k,/->
X for all channel sets mk For user m and channel kInter-channel allocation strategy, x mk =1 represents the task offloading of user m over channel k, x mk =0 means that user m is not unloaded via channel k, +.>
B is the channel bandwidth, h mk Representing the channel gain on channel k between user m and edge server, N 0 Variance of zero mean additive white gaussian noise, +.>
For the transmission energy consumption of all users, P m Is the transmit power of user m, +.>
Aggregate for all users;
establishing the unloading optimization problem according to the minimum system unloading loss objective function:
optionally, in an embodiment of the present invention, calculating, based on the offload optimization problem, offload loss when each user performs offload on a different channel, and taking a channel with the minimum offload loss as an optimal channel includes:
respectively calculating user sets to be allocated
All users of (a) through unoccupied channel set +.>
Unloading loss for all channels of a network, wherein the set of users to be allocated is +.>
Is/are selected from the unoccupied channel set by any user m>
The unloading loss of any channel k unloading is:
selecting a channel with minimum unloading loss for each user as the optimal channel of the corresponding user, and collecting users to be distributed
The optimal channel determination formula for any user m is:
optionally, in one embodiment of the present invention, the calculating the offloading gain of each user for offloading over other channels under the optimal channel includes:
user set to be allocated
Is/are via unoccupied channel set +.>
Unloading loss of the channel to be unloaded is subjected to ascending order, and the obtained result is:
wherein,,
selecting for user m the unoccupied channel set +.>
Unloading all channels of a networkChannel index with i-th lowest loading loss, +.>
M 0 For user set to be allocated->
The number of elements in the list;
calculating the unloading gain of each user unloading under the optimal channel compared with other channels, and any user set to be allocated
The offloading gain of the best channel for user m is:
wherein alpha is i ∈[0,1]Is the gain factor for the i-th lowest channel of each user offload loss.
Optionally, in an embodiment of the present invention, the user calculation mode corresponding to the maximum unloading gain is:
according to the maximum gain-oriented channel allocation method for mobile edge calculation, disclosed by the embodiment of the invention, the influence of a spectrum allocation strategy on the system time delay and the energy consumption is jointly considered, and different actual scenes can be adapted by changing the weight parameters of the time delay and the energy consumption. The maximum gain-oriented channel allocation method significantly improves the performance of the algorithm. The performance is significantly improved over greedy algorithms and approaches the optimal performance achievable with existing methods. And the complexity is very low, and the complexity is far lower than that of other existing algorithms with similar performances under the condition of ensuring the performances.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of a method for maximum gain steering channel allocation for mobile edge-oriented computation according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an implementation step of a maximum gain steering channel allocation method for mobile edge calculation according to an embodiment of the present invention;
FIG. 3 is a diagram illustrating performance comparisons of a method and a greedy algorithm, a branch-and-bound method, and a traversal search algorithm, according to an embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The maximum gain-oriented channel allocation method for mobile edge-oriented computation according to an embodiment of the present invention is described below with reference to the accompanying drawings. The usage scenario is a multi-link mobile edge computing system that includes an edge server and a plurality of users. First, the edge server estimates its wireless channel state information to each user, including wireless channel and noise power; then, constructing a channel allocation optimization problem according to an actual scene; then, adopting a channel allocation strategy of maximum gain steering to allocate channels for each user; and finally, according to the obtained channel allocation strategy, the user respectively uninstalls the tasks to the edge server through different channels. After the calculation is completed, the edge server transmits the calculation result back to the user.
Specifically, fig. 1 is a flowchart of a maximum gain oriented channel allocation method for mobile edge calculation according to an embodiment of the present invention.
As shown in fig. 1, the maximum gain steering channel allocation method for mobile edge oriented computation includes the following steps:
in step S101, a set of users to be allocated and a set of unoccupied channels are constructed, and an unloading optimization problem is established with the goal of minimizing system unloading loss according to the influence of the channel allocation policy on the unloading delay and energy consumption of the mobile edge computing system.
In an embodiment of the present invention, a mobile edge computing system is constructed prior to establishing the offload optimization problem, wherein the mobile edge computing system comprises a base station equipped with an edge server, a plurality of single antenna users, and a plurality of orthogonal channels. The user adopts a channel access scheme of orthogonal frequency division multiple access.
Initializing a set of users to be allocated
And unoccupied channel set +.>
The built mobile edge computing system shares an edge server, M users and K orthogonal channels, and uses the set +.>
And->
Representing the set of users and channels, respectively. User set to be allocated +.>
And unoccupied channel set +.>
Initialized to the set of all users and all channels, i.e. there is +.>
When offloading, each user selects only one channel for offloading and each channel allows only one user to occupy. For a constructed mobile edge computing system, an unloading optimization problem is constructed by considering a single connection constraint condition, and the unloading loss of the system, namely the weighted sum of the time delay and the energy consumption, is minimized by optimizing the allocation of an unloading channel of a user.
Establishing an offload optimization problem includes the steps of:
constructing a set of channel allocation indications
Wherein x is mk Representing the channel allocation strategy between user m and channel k, x mk =1 represents the task offloading of user m over channel k, x mk =0 represents that user m does not unload through channel k;
the single connection constraint is constructed as follows:
this constraint limits each user to selecting only one channel for offloading.
The constraint limits each channel to be occupied by at most one user, thereby avoiding inter-user interference. The two constraints together form a single connection constraint.
The constructed minimum system unloading loss objective function is as follows:
wherein ψ (x) is the system unloading loss, parameter λ t And lambda (lambda) e Respectively represent the influence of system time delay and energy consumption in the current scene, in particular the parameter lambda t For system time delay under current sceneWeight parameter of the scalar function, lambda e The weight parameter of the energy consumption to the objective function in the current scene,
t is the maximum transmission and delay for simultaneous transmission over all channels k For the offloading delay of the transmission over channel k, < >>
D m For the task data size of user m, R mk Transmission rate offloaded for user m via channel k,/->
X for all channel sets mk Strategy for channel allocation between user m and channel k, x mk =1 represents the task offloading of user m over channel k, x mk =0 means that user m is not unloaded via channel k, +.>
B is the channel bandwidth, h mk Representing the channel gain on channel k between user m and edge server, N 0 Variance of zero mean additive white gaussian noise, +.>
For the transmission energy consumption of all users, P m Is the transmit power of user m, +.>
Aggregate for all users.
Determining the weight parameter lambda of the time delay and the energy consumption of the system t And lambda (lambda) e The parameter is determined by the state of the current system. In particular, in time sensitive systems, such as ultra-reliable low delay communication scenarios, a larger lambda t Value and smaller lambda e The value is set; while in energy sensitive systems, such as scenes where the user's power is low or there is no constant energy input, a smaller lambda t Value sum comparisonLarge lambda e The value is set.
Establishing an unloading optimization problem according to an objective function of minimizing system unloading loss:
in step S102, the offloading loss of each user for offloading under different channels is calculated based on the offloading optimization problem, and the channel with the smallest offloading loss is used as the best channel for the corresponding user.
In the embodiment of the invention, the unloading loss of each user for unloading under different channels is calculated based on the unloading optimization problem, and the channel with the minimum unloading loss is taken as the optimal channel, and the method comprises the following steps:
respectively calculating user sets to be allocated
All users of (a) through unoccupied channel set +.>
Unloading loss for all channels of a network, wherein the set of users to be allocated is +.>
Is/are selected from the unoccupied channel set by any user m>
The unloading loss of any channel k unloading is:
separately determining sets
The best channel for all users in the network. The strategy for selecting the best channel is selectionEach user has the channel with the least offload loss as the best channel for the corresponding user. User set->
The best channel for any user m can be determined by:
in step S103, the offloading gain of offloading each user under the optimal channel compared with offloading gains of other channels is calculated, the optimal channel is allocated to the user corresponding to the maximum offloading gain, the set of users to be allocated and the set of unoccupied channels are updated, and the optimal channel of each user is recalculated to continue allocation until all users in the set of users to be allocated are allocated with the optimal channel.
Calculating the offloading gain for each user to offload at the optimal channel compared to the offloading of the other channels includes:
user set to be allocated
Is/are via unoccupied channel set +.>
Unloading loss of the channel to be unloaded is subjected to ascending order, and the obtained result is:
wherein,,
selecting for user m the unoccupied channel set +.>
Unloading loss i-th low channel index for all channels to be unloaded, < >>
M 0 For user set to be allocated->
The number of elements in the list.
Calculating the unloading gain of each user unloading under the optimal channel compared with other channels, and any user set to be allocated
The offloading gain of the best channel for user m is:
wherein alpha is i ∈[0,1]Is the gain factor for the i-th lowest channel of each user offload loss. Alpha i The specific numerical value of (3) is designed according to the actual scene. In general, the smaller i is, the corresponding gain factor α i The larger the value, this also represents the greater the impact of the earlier ordered channels on the offloading gain.
As shown in fig. 2, a user having the greatest offloading gain is allocated its best channel, and the user and the channel are respectively allocated from the set of users to be allocated
And unoccupied channel set +.>
The deletion in (a) specifically comprises:
determining a set of users to be allocated
The user with the greatest offloading gain in (c) may be determined by:
determining a user with maximum offloading gain
Is>
Can be calculated by formula (6);
channel is set
Assigned to user->
Will user
From the collection->
Delete, channel->
From disaggregation->
And deleted.
Judging user set to be distributed
If the channel allocation strategy is not the empty set, the optimal channel of each user is recalculated to continue allocation until all users in the user set to be allocated are allocated with the optimal channel, the channel allocation strategy of the users is output, and the tasks are unloaded to the edge server through different channels.
The user completing the offloading according to the allocated channel comprises the steps of: and according to the output user channel allocation strategy, all users transmit tasks to the edge server through the allocated channels. The edge server receives the tasks transmitted by each user, and after receiving the tasks, the edge server sequentially calculates each task and processes the tasks according to the principle of first arrival and first calculation. For the task of completion of calculation, the edge server transmits the calculation result back to the original user through the distributed channel, and the calculation result is processed according to the principle of completion of calculation and back; finally, the user receives the calculation result of the edge server and completes a series of decisions about the task according to the result.
Fig. 3 illustrates the performance comparison results of the method of the embodiment of the present invention and the conventional greedy algorithm, the branch-and-bound method, and the traversal search algorithm, and it can be seen that the performance of the method of the present invention is close to the optimal branch-and-bound method and traversal search algorithm, which are far superior to the conventional branch-and-bound algorithm. And with the increase in the number of users, the algorithm can still remain near optimal performance.
Table 1 is the result of comparing the computational complexity of the inventive method with that of a conventional greedy algorithm, branch-and-bound method, and traversal search algorithm. It can be seen that the computational complexity of the method of the present invention is comparable to that of the conventional greedy algorithm, far lower than that of the branch-and-bound method and the traversal search algorithm.
TABLE 1
According to the maximum gain-oriented channel allocation method for mobile edge calculation, which is provided by the embodiment of the invention, a greedy strategy of a traditional greedy algorithm is improved, and channels are preferentially allocated to users with maximum unloading gain in each iteration process, so that the system performance is improved. Firstly, constructing a channel allocation optimization problem of minimizing system unloading loss; then, calculating the unloading loss of each user to be allocated, and determining the optimal channel of each user; then, allocating channels for users with the best unloading gain in each iteration through an allocation strategy guided by the maximum gain, deleting the allocated users and channels from a user set to be allocated and an unoccupied channel set, and repeating the above processes until the user set to be allocated is empty; and finally, according to the obtained user channel allocation strategy, the user finishes unloading. Compared with the traditional optimization method, the algorithm complexity is greatly reduced on the premise of ensuring the performance.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more N executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present invention.
Claims (6)
1. A method for maximum gain directed channel allocation for mobile edge-oriented computation, comprising the steps of:
constructing a user set to be allocated and an unoccupied channel set, and establishing an unloading optimization problem with the aim of minimizing system unloading loss according to the influence of a channel allocation strategy on the unloading delay and energy consumption of a mobile edge computing system;
calculating the unloading loss of each user for unloading under different channels based on the unloading optimization problem, and taking the channel with the minimum unloading loss as the optimal channel of the corresponding user;
and calculating the unloading gain of each user unloaded under the optimal channel compared with other channels, distributing the optimal channel for the user corresponding to the maximum unloading gain, updating the user set to be distributed and the unoccupied channel set, and recalculating the optimal channel of each user to continue to distribute until all users in the user set to be distributed are distributed with the optimal channel.
2. The method of claim 1, wherein prior to establishing the offload optimization problem, the method further comprises:
the mobile edge computing system is constructed, wherein the mobile edge computing system comprises a base station provided with an edge server, a plurality of single antenna users and a plurality of orthogonal channels.
3. The method of claim 1, wherein each user selects only one channel for offloading and each channel allows only one user to occupy when offloading, wherein the impact on mobile edge computing system offloading latency and energy consumption according to a channel allocation policy is targeted to minimize system offloading losses, establishing an offloading optimization problem comprises:
constructing the minimum system unloading loss objective function:
wherein ψ (x) is the system unloading loss, parameter λ t Weighting parameter lambda of system delay to objective function in current scene e The weight parameter of the energy consumption to the objective function in the current scene,
t is the maximum transmission and delay for simultaneous transmission over all channels k For the offloading delay of the transmission over channel k, < >>
D m For the task data size of user m, R mk Transmission rate offloaded for user m via channel k,/->
X for all channel sets mk Strategy for channel allocation between user m and channel k, x mk =1 represents the task offloading of user m over channel k, x mk =0 means that user m is not unloaded via channel k, +.>
B is the channel bandwidth, h mk Representing the channel gain on channel k between user m and edge server, N 0 Variance of zero mean additive white gaussian noise, +.>
For the transmission energy consumption of all users, P m Is the transmit power of user m, +.>
Aggregate for all users;
establishing the unloading optimization problem according to the minimum system unloading loss objective function:
4. a method according to claim 3, wherein calculating the offload loss for each user to offload on a different channel based on the offload optimization problem, and wherein taking the channel with the smallest offload loss as the best channel comprises:
respectively calculating user sets to be allocated
All users of (a) through unoccupied channel set +.>
Unloading loss for all channels of a network, wherein the set of users to be allocated is +.>
Is/are selected from the unoccupied channel set by any user m>
The unloading loss of any channel k unloading is:
selecting a channel with minimum unloading loss for each user as the optimal channel of the corresponding user, and collecting users to be distributed
The optimal channel determination formula for any user m is:
5. the method of claim 4, wherein calculating the offloading gain for each user to offload at the optimal channel compared to offloading at other channels comprises:
user set to be allocated
Is/are via unoccupied channel set +.>
Unloading loss of the channel to be unloaded is subjected to ascending order, and the obtained result is:
wherein,,
selecting for user m the unoccupied channel set +.>
Unloading loss i-th low channel index for all channels to be unloaded, < >>
M 0 For user set to be allocated->
The number of elements in the list;
calculating the unloading gain of each user unloading under the optimal channel compared with other channels, and any user set to be allocated
Unloading of the best channel for user m in the middleThe carrier gain is:
wherein alpha is i ∈[0,1]Is the gain factor for the i-th lowest channel of each user offload loss.
6. The method of claim 5, wherein the user calculation corresponding to the maximum offload gain is:
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