CN116362850A - Resource allocation scheme for meta-universe service - Google Patents
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Abstract
The invention discloses a resource allocation scheme for a metauniverse service, which comprises framework members, framework description, system modeling, problem processing and market transaction design. At a high level, the resource allocation is expressed as a lyapunov problem with minimum average delay over time. The problem is solved by a new double-level alternative actor-criticizing algorithm. At a low level, a price-guided double netherlands auction mechanism is proposed for matching and price determination of heterogeneous resources. Experimental results based on real data show that compared with other methods, the method provided by the invention can reduce time delay and energy consumption and improve social benefit.
Description
Technical Field
The invention relates to the technical field of resource allocation, in particular to a resource allocation scheme for meta-space service.
Background
The advent of the meta-universe has attracted more and more attention in the next generation of the internet. Under the incentives of the explosive communication and computing technologies, the metauniverse service market is expanding, and metauniverse users can rent and be allocated available resources. However, many challenges remain in resource provisioning in the metaspace market, such as: the incentives for hierarchical, tightly-spaced meta-space service architecture, time dependence, and heterogeneity.
Previous studies treated the resource allocation problem as a single time problem. Although the solution to the optimization problem is relatively simple, the overall optimization performance and the use effect are not satisfactory, and the resource utilization rate is poor; there is a need for providing sexual resources in the metaspace services market. Traditional incentive mechanisms involving single kinds of resources are no longer applicable. The mechanism of excitation of heterogeneity is also highly desirable. As communication and computing technologies evolve, heterogeneous resources are associated and allocated. There is a need to establish incentive mechanisms involving heterogeneous resources.
Disclosure of Invention
The invention aims to provide a resource allocation scheme for meta-space services, so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: a resource allocation scheme for meta-space service is characterized in that: including architecture membership, architecture description, system modeling, problem handling, and market transaction design.
Preferably, the architecture members include a user and a service resource pool; the request size of the user n in the time slot t is marked as D n (t) channel gain of h n (t); the service resource pool B m Represents the maximum bandwidth available to the service resource pool m, F m Representing the maximum computational power resources that the service resource pool m can provide.
Preferably, the architecture description includes resource provisioning and heterogeneous auctions; in resource provision, metauniverse users offload their tasks to different service resource pools and are allocated to corresponding bandwidths and computing resources; in a heterogeneous auction, the metauniverse user and metauniverse service provider match each other, and the heterogeneous auction determines the transaction price.
Preferably, the system modeling includes:
communication frame: let B n，m (t) represents the bandwidth size, downlink bit rate, provided by the service resource pool m to the metauniverse user n at time t
Wherein h is n (t) is the channel gain, p n (t) is transmission capability, n 0 Is Gaussian noise, R n，m Representing the distance between the service resource pool m and the metauniverse user n;
assume that the task size of the submission of meta-universe user n at time t is D n (t), the uploading time of the task is:
the transmission energy consumption can be expressed as:
task processing framework: there are a large number of computations in the meta-universe, such as modeling, rendering, and dynamic changes of scenes. Assuming that the service resource pool m receives a graphics rendering task, the processing time is expressed as:
f n，m (t) represents the computing resources allocated by service resource pool m when processing metauniverse user n, C k The CPU cycle required for each bit of calculation processing task, k is the coefficient calculation processing energy consumption related to the hardware structure of the chip, and can be expressed as:
a blockchain framework: the blockchain system needs to complete three steps to process the record: the first step is block generation, wherein after the task of the user is processed, the processing record is packed into a block; the second step is to broadcast the block to other block producers; the final step is consensus verification, after receiving the broadcast block, the block producer verifies the block, and once the entire network has reached consensus, new blocks are chained into the blockchain.
Preferably, the problem handling includes:
modeling of optimization problems:
the total time delay of all meta-universe users is
Expressing minimizing the latency of the user as a joint optimization problem:
in these constraints, C1 and C2 specify that all users must assign computing tasks to one service resource pool, and that there is and only one resource pool; c3 and C4 ensure that the bandwidth and computing resources allocated to the user by the service resource pool cannot exceed the total bandwidth and computing resources owned by the service resource pool; c5 and C6 ensure that the bandwidth and computing resources to which the user is allocated are at least B 0 And f 0 The method comprises the steps of carrying out a first treatment on the surface of the C7 is a long term energy consumption constraint requiring that the time average energy consumption must not exceed an upper limit nσ, where σ is an energy consumption threshold;
lyapunov (Lyapunov) optimization:
a virtual energy consumption queue Q is arranged n，m (t) representing energy consumption constraint, Q n，m (t) represents the queue size of the meta-space user n in the service resource pool m in the t-th time slot:
when t=0, Q n，m (0) =0, η is a positive coefficient; definition Q (t) = { Q n，m (t) } formulate stability of virtual queues based on the Lyapunov optimization framework, in each slot, the quadratic Lyapunov function is defined as:
lyapunov drift based on Lyapunov function
Is defined asTo measure the stability of the virtual queues by measuring the difference between queues in two consecutive time periods, under the framework of Lyapunov optimization technology, there is an upper limit B of Lyapunov drift 1 ；
Based on the Lyapunov optimization framework, the Lyapunov drift Λl (Q (t)) is combined with the optimization target O (t), so that the problem P1 can be solved by minimizing the drift plus a penalty term, which can be expressed specifically as:
s.t.(C1)(C2)(C3)(C4)(C5)(C6)
wherein, beta is a weight coefficient in the Lyapunov optimization framework;
actor criticizer algorithm: the system comprises an actor module, a criticizing module and a queue network optimization module.
Preferably, the market transaction design comprises:
modeling of optimization problems: definition V n For the average rotational speed of metauniverse user n, R n To support the bitrate of his experience, the meta-universe experience of the user is evaluated by a fusion of instantaneous structural similarity and video multipart evaluation, in particular the SSIMf and VMAFg of the user can be obtained by calculation:
g(V n ，R n )＝min{100，b 1 +b 2 V n +b 3 R n +b 4 V n R n }
the value of average rotational speed sets R for health considerations and network game limitations n ∈[360°，1800°]Thus, the user's estimate can be given as:
similar to a userThe service resource pool has own prediction on the user's bid, and the service resource pool can evaluate the provided resources according to the solution in the last step
The valuation function of the meta-space service provider is expressed as: />
B k And f k Is the resource owned by each service provider, e is positive coefficient, w 1 ，w 2 ，w 3 Is a weight and the sum is 1;
auction market design:
the purpose of this auction is to match the metauniverse users with the service provider and to determine the purchase price and the sales price, using the bilateral call market, in which three identities are involved: buyers, sellers, and auctioneers.
Compared with the prior art, the invention has the beneficial effects that: the invention provides a bidirectional resource supply method of a meta space service with heterogeneous auctions, in which resource supply and heterogeneous auctions are alternately deployed on double layers respectively. At a high level, resource allocation is expressed as a Lyapunov problem with minimum average delay over time. The problem is solved by a new double-level alternative actor-criticizing algorithm. At a low level, a price-guided double netherlands auction mechanism is proposed for matching and price determination of heterogeneous resources. Experimental results based on real data show that compared with other methods, the method provided by the invention can reduce time delay and energy consumption and improve social benefit.
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FIG. 1 is a schematic diagram of a system architecture according to the present invention;
FIG. 2 is a queue convergence diagram of an experimental example of the invention;
FIG. 3 is a bar graph showing the number of users in an experimental example of the present invention;
FIG. 4 is a bar graph of experimental example market efficiency according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: a resource allocation scheme for meta-space services comprises architecture members, architecture description, system modeling, problem processing and market transaction design.
Wherein the architecture members include users and a service resource pool; the request size of the user n in the time slot t is marked as D n (t) channel gain of h n (t); the service resource pool B m Represents the maximum bandwidth available to the service resource pool m, F m Representing the maximum computational power resources that the service resource pool m can provide. In this system, users and resource providers do not directly exchange power resources, but exchange through an edge cloud market. Suppose there is a metauniverse user set
Service resource pool set->
Meta-universe service provider->
Different service resource pools have different meta-space service providers. The service resource pool has a rough average bandwidth and computing resource assessment for the owned provider, and at the beginning of each time slot, the service resource pool gives the bandwidth and computing resource that the service resource pool can provide.
In the present invention, the architecture description includes resource provisioning and heterogeneous auctions; in resource provision, metauniverse users offload their tasks to different service resource pools and are allocated to corresponding bandwidths and computing resources; in a heterogeneous auction, the metauniverse user and metauniverse service provider match each other, and the heterogeneous auction determines the transaction price.
In the present invention, the system modeling includes:
communication frame: let B n，m (t) represents the bandwidth size, downlink bit rate, provided by the service resource pool m to the metauniverse user n at time t
Wherein h is n (t) is the channel gain, p n (t) is transmission capability, n 0 Is Gaussian noise, R n，m Representing the distance between the service resource pool m and the metauniverse user n;
assume that the task size of the submission of meta-universe user n at time t is D n (t), the uploading time of the task is:
the transmission energy consumption can be expressed as:
task processing framework: there are a large number of computations in the meta-universe, such as modeling, rendering, and dynamic changes of scenes. Assuming that the service resource pool m receives a graphics rendering task, the processing time is expressed as:
f n，m (t) represents the computing resources allocated by service resource pool m when processing metauniverse user n, C k The CPU cycle required for each bit of calculation processing task, k is the coefficient calculation processing energy consumption related to the hardware structure of the chip, and can be expressed as:
a blockchain framework: the blockchain system needs to complete three steps to process the record: the first step is block generation, wherein after the task of the user is processed, the processing record is packed into a block; the second step is to broadcast the block to other block producers; the final step is consensus verification, after receiving the broadcast block, the block producer verifies the block, and once the entire network has reached consensus, new blocks are chained into the blockchain. In this system, a proof of work approach is taken to ensure that the nodes are honest and trustworthy. That is, after solving the task of the metauniverse user, it is necessary to first solve the hash problem posed by the blockchain, generate a block, and package it. The block is then broadcast to other verifiers to achieve consensus. By introducing blockchains in task processing, we can enhance the security and privacy of metauniverse users while experiencing high levels of metauniverse services. Let f b，m (t) allocating computing resources for generating blocks and handling hash problems at time slot t on behalf of service resource pool m, D n (t) is the size of the input data, including the size of the task processing record and the size of the encrypted data, I n，m (t) is the number of CPU cycles required to handle the hash problem in slot t, C b Is the processing density, then the time consumption of the block portion can be given by:
the energy consumption can be expressed as:
we use a binary variable a n，m (t) representing scheduling policy, α n，m (t) =1 represents that user n assigns a task to service resource pool m, a at time slot t n，n (t) =1 indicates no assignment. For the sake of subsequent easy expression, we define
Defining total overhead
v and μ are non-negative weights.
In the present invention, the problem processing includes:
modeling of optimization problems:
the total time delay of all meta-universe users is
Expressing minimizing the latency of the user as a joint optimization problem:
in these constraints, C1 and C2 specify that all users must assign computing tasks to one service resource pool, and that there is and only one resource pool; c3 and C4 ensure that the bandwidth and computing resources allocated to the user by the service resource pool cannot exceed the total bandwidth and computing resources owned by the service resource pool; c5 and C6 ensure that the bandwidth and computing resources to which the user is allocated are at least B 0 And f 0 The method comprises the steps of carrying out a first treatment on the surface of the C7 is a long term energy consumption constraint requiring that the time average energy consumption must not exceed an upper limit nσ, where σ is an energy consumption threshold;
lyapunov optimization:
a virtual energy consumption queue Q is arranged n，m (t) representing energy consumption constraint, Q n，m (t) represents the queue size of the meta-space user n in the service resource pool m in the t-th time slot:
when t=0, Q n，m (0) =0, η is a positive coefficient; definition Q (t) = { Q n，m (t) } formulate stability of virtual queues based on the Lyapunov optimization framework, in each slot, the quadratic Lyapunov function is defined as:
lyapunov drift based on Lyapunov function
Is defined as the difference between the queues in two consecutive time periods to measure the stability of the virtual queues, and has an upper limit B of Lyapunov drift under the framework of Lyapunov optimization technology 1 ；
Based on the Lyapunov optimization framework, the Lyapunov drift Λl (Q (t)) is combined with the optimization target O (t), so that the problem P1 can be solved by minimizing the drift plus a penalty term, which can be expressed specifically as:
s.t.(C1)(C2)(C3)(C4)(C5)(C6)
wherein, beta is a weight coefficient in the Lyapunov optimization framework;
actor criticizer algorithm: the system comprises an actor module, a criticizing module and a queue network optimization module.
Actor module: this module consists of an LSTM and an action generator. At the beginning of each time slot, LSTM takes as input the channel gain, the queue length, and the total amount of resources of the different service resource pools, and outputs the probability P of offloading each user to the different service resource pools n，m (t)。P n，m (t) is a value between 0, 1.
According to the probability obtained in the last step, n+1 assignment strategies can be generated according to the probability, wherein one strategy selects a service resource pool corresponding to the maximum probability for all users as a destination assigned by the task, the ith strategy in the remaining N assignment strategies selects the service resource pool with the highest probability as the destination assigned by the task, and the remaining users randomly select the assigned destination.
Criticizing home module: for n+1 assignment strategies generated in the actor module, substituting the assignment strategy into the problem P2, the problem P2 can be converted into a nonlinear programming problem according to the given assignment strategy, and the problem can be converted into a nonlinear programming problem according to a by a solver n，m (t) obtaining a corresponding optimal resource allocation amount, i.e., corresponding B n，m (t)，f n，m (t)，f b，m (t). For each assignment policy, calculating the corresponding time delay and energy consumption, and comparing the time delay and energy consumption with each other, and reserving the assignment policy corresponding to the optimal value of the problem P2 and the allocated resource number.
Queue network optimization module: and according to the optimal assignment strategy and the number of allocated resources obtained in the criticizing module. And updating the energy consumption queues of all users according to the energy consumption information, wherein the updated energy consumption queues are used as input in the next time slot, and the input of the time slot and the optimal assignment strategy are used as tagged input and output and put into a playback buffer pool. Every Λτ slots, LSTM randomly selects a set of tagged input-output to update LSTM network policies. The LSTM parameters are updated using an SGD algorithm to minimize its average cross entropy loss function.
In the present invention, the market transaction design includes:
modeling of optimization problems: definition V n For the average rotational speed of metauniverse user n, R n To support the bitrate of his experience, the meta-universe experience of the user is evaluated by a fusion of instantaneous structural similarity and video multipart evaluation, in particular the SSIMf and VMAFg of the user can be obtained by calculation:
g(V n ，R n )＝min{100，b 1 +b 2 V n +b 3 R n +b 4 V n R n }
the value of average rotational speed sets R for health considerations and network game limitations n ∈[360°，1800°]Thus, the user's estimate can be given as:
similar to the user, the service resource pool has own predictions of the user's bids, and the service resource pool evaluates the provided resources according to the solution in the previous step
The valuation function of the meta-space service provider is expressed as: />
B k And f k Is the resource owned by each service provider, e is positive coefficient, w 1 ，w 2 ，w 3 Is a weight and the sum is 1;
auction market design:
the purpose of this auction is to match the metauniverse users with the service provider and to determine the purchase price and the sales price, using the bilateral call market, in which three identities are involved: buyers, sellers, and auctioneers.
Auctioneers: the auctioneer has two clocks at the same time, one buyer side and one seller side, buyer clock C B (t) represents the price of the buyer at time slot t, its initial value is usually defined as p max ，p max Is based on past auction experience. For a given service resource pool m, the seller clock is denoted as C s (t) its initial value is usually defined as p min ，
Gamma is the price impact coefficient. In addition to this, the auctioneer needs to choose a step size ζ (t) to adjust the price of the time, and the auction usually starts from the buyer side.
The buyer: first, the auctioneer broadcasts a clock price C on the buyer side B (t) if the nth buyer satisfies the auction condition, i.e. his bid is higher than the current clock price, then this buyer is removed from the current auction queue and added to the successful auction buyer collection W B (t) and set its own bid to the current buyer's clock price. If C B (t) too large resulting in an unsatisfactory bid by the buyer, then C B (t) needs to be reduced according to the step size ζ (t).
The seller: similar to the buyer-side process, the auctioneer broadcasts a clock price C on the seller side S (t) if the kth seller meets the auction condition, i.e., his bid is below the current clock price, then the seller is removedFront auction queue and added to the successful auction seller collection W S (t) and set its own bid to the current seller's clock price. If C S (t) too small results in an unsatisfactory seller bid, then C S (t) need to be lifted according to step size ζ (t).
When the seller's clock exceeds the buyer's clock, the auction ends and the social benefit of the overall auction process can be defined as:
experimental example:
the original data set is about 3.51GB in a representative real data set of edge cloud service a company. The data covers more than 1000 ten thousand user requests between day 1 of 2021, 10 and day 29. All experiments were simulated on servers of Intel (R) Xeon (R) E5-2690 v4@2.60GHz CPU and NVIDIA Tesla P100-PCIE-16GB GPU and 64GB RAM. Default n=10, m=3, k=10, t=1000, β=35, w 1 ＝w 2 ＝0.4，w 3 ＝0.2，∈＝0.5，p max ＝110，γ＝0.093，η＝1000，σ＝0.06，v＝10，μ＝0.5。
The benchmark method for task assignment comprises the following steps: (i) DYRECEIVE, which only considers queue length. The metauniverse user's tasks will be assigned to the service resource pool that he has the lowest length in each slot queue. (ii) Random: the task of the metauniverse user will be dispatched to a random service resource pool; (iii) The Active Set (AS) is used for obtaining the assignment strategy and the allocated resource quantity simultaneously through an Active Set method. The auction benchmark method comprises the following steps: (i) Double Auaction (DA), buyer and seller prices are ranked in order, buyer matches from high to low, seller matches from low to high. (ii) Double Dutch Auction (DDA), the seller's auction clock is also set based on auction experience, rather than derived from predicted prices. (iii) Vickers Auaction (VA), like DA, the winner of the Auction is still a sequential match decision, but the bid is traded in the second-order bid, we set the trade increment to 5% of their own bid.
As shown in fig. 2, the queue convergence for the different methods at n=10 is shown, reaching convergence quickly and keeping the average queue length low. Because our approach takes into account current queue information, it can schedule more metauniverse user tasks into the optimal service resource pool, thereby reducing energy queue length.
The number of users affects. As can be seen from fig. 3, as the number of users increases, all algorithm delays and power consumption increase. DYRECEIVE performs poorly in terms of computational cost, as DYRECEIVE only considers queue length. Although the queue length does not increase rapidly, it does not guarantee that the correct selection is made nor that the resulting energy consumption is minimal. Random does not take into account any problems, resulting in unstable queues. The AS obtains a more modest result on a global consideration basis. But it is relatively inefficient when the number of users is large. When the number of users is 10, the method of the invention is 10.03%, 21.56% and 14.49% higher than Random, DYRECEIVE and AS respectively in energy consumption, and 36.55%, 17.16% and 12.05% higher in total delay. Moreover, as the number of users increases, the gap becomes larger and larger.
Fig. 4 shows the impact of different numbers of users on the overall social benefit in different auction algorithms. When the number of users is 10, the price-guided double netherlands clock mode of the invention is 3.85%,7.28% and 9.77% higher than that of DDA, DA and VA respectively.
In summary, the present invention proposes a two-way resource supply method for a meta-space service with heterogeneous auctions, in which resource supply and heterogeneous auctions are alternately deployed on two layers, respectively. At a high level, resource allocation is expressed as a Lyapunov problem with minimum average delay over time. The problem is solved by a new double-level alternative actor-criticizing algorithm. At a low level, a price-guided double netherlands auction mechanism is proposed for matching and price determination of heterogeneous resources. Experimental results based on real data show that compared with other methods, the method provided by the invention can reduce time delay and energy consumption and improve social benefit.
It should be noted that in this document, terms such as "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A resource allocation scheme for meta-space service is characterized in that: including architecture membership, architecture description, system modeling, problem handling, and market transaction design.
2. The meta-space service-oriented resource allocation scheme of claim 1, wherein: the architecture members include users and a service resource pool; the request size of the user n in the time slot t is marked as D n (t) channel gain of h n (t); the service resource pool B m Represents the maximum bandwidth available to the service resource pool m, F m Representing the maximum computational power resources that the service resource pool m can provide.
3. The meta-space service-oriented resource allocation scheme of claim 1, wherein: the architecture description includes resource provisioning and heterogeneous auctions; in resource provision, metauniverse users offload their tasks to different service resource pools and are allocated to corresponding bandwidths and computing resources; in a heterogeneous auction, the metauniverse user and metauniverse service provider match each other, and the heterogeneous auction determines the transaction price.
4. The meta-space service-oriented resource allocation scheme of claim 1, wherein: the system modeling includes:
communication frame: let B n，m (t) represents the bandwidth size, downlink bit rate, provided by the service resource pool m to the metauniverse user n at time t
Wherein h is n (t) is the channel gain, p n (t) is transmission capability, n 0 Is Gaussian noise, R n，m Representing the distance between the service resource pool m and the metauniverse user n;
assume that the task size of the submission of meta-universe user n at time t is D n (t), the uploading time of the task is:
the transmission energy consumption can be expressed as:
task processing framework: there are a large number of computations in the meta-universe, such as modeling, rendering, and dynamic changes of scenes. Assuming that the service resource pool m receives a graphics rendering task, the processing time is expressed as:
f n，m (t) represents the computing resources allocated by service resource pool m when processing metauniverse user n, C k The CPU cycle required for each bit of calculation processing task, k is the coefficient calculation processing energy consumption related to the hardware structure of the chip, and can be expressed as:
a blockchain framework: the blockchain system needs to complete three steps to process the record: the first step is block generation, wherein after the task of the user is processed, the processing record is packed into a block; the second step is to broadcast the block to other block producers; the final step is consensus verification, after receiving the broadcast block, the block producer verifies the block, and once the entire network has reached consensus, new blocks are chained into the blockchain.
5. The meta-space service-oriented resource allocation scheme of claim 1, wherein: the problem handling includes:
modeling of optimization problems:
the total time delay of all meta-universe users is
Expressing minimizing the latency of the user as a joint optimization problem:
in these constraints, C1 and C2 specify that all users must assign computing tasks to one service resource pool, and that there is and only one resource pool; c3 and C4 ensure that the bandwidth and computing resources allocated to the user by the service resource pool cannot exceed the total bandwidth and computing resources owned by the service resource pool; c5 and C6 ensure that the bandwidth and computing resources to which the user is allocated are at least B 0 And f 0 The method comprises the steps of carrying out a first treatment on the surface of the C7 is a long term energy consumption constraint requiring that the time average energy consumption must not exceed an upper limit nσ, where σ is an energy consumption threshold;
lyapunov optimization:
a virtual energy consumption queue Q is arranged n，m (t) representing energy consumption constraint, Q n，m (t) represents the queue size of the meta-space user n in the service resource pool m in the t-th time slot:
when t=0, Q n，m (0) =0, η is a positive coefficient; definition Q (t) = { Q n，m (t) } formulate stability of virtual queues based on the Lyapunov optimization framework, in each slot, the quadratic Lyapunov function is defined as:
lyapunov drift based on Lyapunov function
Is defined as the difference between the queues in two consecutive time periods to measure the stability of the virtual queues, and has an upper limit B of Lyapunov drift under the framework of Lyapunov optimization technology 1 ；
Based on the Lyapunov optimization framework, the Lyapunov drift Λl (Q (t)) is combined with the optimization target O (t), so that the problem P1 can be solved by minimizing the drift plus a penalty term, which can be expressed specifically as:
s.t.(C1)(C2)(C3)(C4)(C5)(C6)
wherein, beta is a weight coefficient in the Lyapunov optimization framework;
actor criticizer algorithm: the system comprises an actor module, a criticizing module and a queue network optimization module.
6. The meta-space service-oriented resource allocation scheme of claim 1, wherein: the market transaction design includes:
modeling of optimization problems: definition V n For the average rotational speed of metauniverse user n, R n To support other bodyThe bit rate of the experiment is evaluated through the fusion of instantaneous structural similarity and video multiparty evaluation, and the meta-universe experience of the user is evaluated, specifically, SSIM f and VMAF g of the user can be obtained through calculation:
g(V n ，R n )＝min{100，b 1 +b 2 V n +b 3 R n +b 4 V n R n }
the value of average rotational speed sets R for health considerations and network game limitations n ∈[360°，1800°]Thus, the user's estimate can be given as:
similar to the user, the service resource pool will have its own predictions of the user's bids
Valuation based on solving for the resources provided in the previous step
The valuation function of the meta-space service provider is expressed as: />
B k And f k Is the resource owned by each service provider, e is positive coefficient, w 1 ，w 2 ，w 3 Is a weight and the sum is 1;
auction market design:
the purpose of this auction is to match the metauniverse users with the service provider and to determine the purchase price and the sales price, using the bilateral call market, in which three identities are involved: buyers, sellers, and auctioneers.
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