CN115278779A - Rendering perception-based dynamic placement method for VR service module in MEC network - Google Patents

Rendering perception-based dynamic placement method for VR service module in MEC network Download PDF

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CN115278779A
CN115278779A CN202210900105.7A CN202210900105A CN115278779A CN 115278779 A CN115278779 A CN 115278779A CN 202210900105 A CN202210900105 A CN 202210900105A CN 115278779 A CN115278779 A CN 115278779A
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user
cost
time slot
delay
mec server
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张鹤立
刘春雨
李曦
纪红
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Beijing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0925—Management thereof using policies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0958—Management thereof based on metrics or performance parameters
    • H04W28/0967—Quality of Service [QoS] parameters
    • H04W28/0975—Quality of Service [QoS] parameters for reducing delays
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W28/00—Network traffic management; Network resource management
    • H04W28/02—Traffic management, e.g. flow control or congestion control
    • H04W28/08—Load balancing or load distribution
    • H04W28/09—Management thereof
    • H04W28/0958—Management thereof based on metrics or performance parameters
    • H04W28/0967—Quality of Service [QoS] parameters
    • H04W28/0983—Quality of Service [QoS] parameters for optimizing bandwidth or throughput
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

Abstract

The invention discloses a rendering perception-based dynamic placement method for VR service modules in an MEC network, belonging to the field of rendering of VR games; the method specifically comprises the following steps: firstly, building a cellular network scene comprising U user players, M base stations and H VR game service modules; respectively calculating placement cost, migration cost, rendering cost and communication cost aiming at the current time slot t; constructing a target function and a constraint condition based on the cost of each time slot, and enabling the total cost of the whole network to be minimum under the condition of meeting the delay constraint of each user; finally, the objective function is decomposed into two sub-problems: dynamic access and service module placement problems and quasi-static resource allocation problems; and respectively solving by using a minimum cut theory and a convex optimization method to obtain an optimal user access scheme, an optimal VR service module placement scheme and an optimal spectrum resource distribution scheme. The invention realizes a better balance between the route delay cost of the rendered VR video stream and the migration cost of the corresponding VR service module.

Description

Rendering perception-based dynamic placement method for VR service module in MEC network
Technical Field
The invention belongs to the field of rendering of VR games, and particularly relates to a rendering perception-based dynamic placement method for VR service modules in an MEC network.
Background
In recent years, combining multiple access edge computing (MEC) technology with wireless Virtual Reality (VR) gaming is a promising computing paradigm. The defect that the computing resources of the mobile equipment are insufficient can be overcome by unloading the rendering task to the edge node for rendering. However, to execute a rendering task on an MEC server, the MEC server needs to have a VR service module corresponding to the rendering task deployed thereon. Obviously, it is not practical for an edge server with limited storage capacity to deploy all VR service modules in the network scenario at the same time.
The prior art studies on VR video are as follows:
document [1] proposes a task offloading scheme supporting a block chain to resist malicious attacks, reduce the computational load of a virtual machine, and meet the high QoE of VR users;
document [2] proposes to minimize the long-term energy consumption of MEC systems based on terahertz wireless access by jointly optimizing viewport rendering offload and downlink transmission power control to support high-quality immersive VR video services.
Document [3] proposes a wireless VR network supporting MECs. The network predicts the field of view of each VR user in real time using a recurrent neural network and offloads the VR rendering tasks from the VR devices to the MEC server through a rendering model migration function.
Document [4] considers the provision of multi-tile wireless VR video services over MEC networks; the goal is to minimize system delay and energy consumption. First, the study converts the time-varying view popularity into a model-free markov chain to effectively capture its dynamics. Then, a hybrid strategy is adopted to coordinate dynamic cache replacement and deterministic offloading so as to fully utilize system resources.
However, the article reduces the end-to-end delay of the user by optimizing the offloading policy of the VR rendering task, and improves the QoE of the user. However, in a network environment with limited memory space of the MEC server, little attention is paid to the placement of various types of VR service modules to support corresponding rendering tasks, and the influence of user mobility on VR service module placement strategies. In addition, the placement strategy of the VR service module has little concern on the routing delay and the network resource consumption cost of the rendered VR video stream.
Edge rendering inevitably introduces edge computation delay and transmission delay caused by the return of rendered VR video stream to the mobile terminal; in particular, the increase in delay is more pronounced since the data volume of VR video streams is generally huge. Therefore, it is important to optimize the transmission path of the rendered VR video stream and to reasonably allocate edge resources including radio spectrum and computation.
It should also be noted that deploying service modules on MEC servers increases placement costs and is limited by storage capacity, and service modules for various types of VR games cannot be deployed on each MEC at the same time. However, a premise for performing rendering tasks on the MEC server is that the VR game service module that the user participates in is already deployed on the MEC server. Therefore, it is important to tightly couple service module layout optimization with computing resource allocation to optimize the delivery performance of wireless VR game video streams.
In addition, in a network scenario where multiple wireless VR games are concurrent, the geographic location of the user changes as the user moves, and accordingly, the access base station may also change accordingly. To ensure low routing cost for rendered VR video streams, the VR service module corresponding thereto may need to be migrated to a new base station. Such migration will undoubtedly increase migration costs, including hardware loss costs and data migration delay costs. Therefore, it becomes important to dynamically jointly optimize the routing overhead and the migration overhead.
[1]P.Lin,Q.Song,F.R.Yu,D.Wang and L.Guo,"Task Offloading for Wireless VR-Enabled Medical Treatment With Blockchain Security Using Collective Reinforcement Learning,"in IEEE Internet of Things Journal,vol.8,no.21,pp.15749-15761,1Nov.1,2021,doi:10.1109/JIOT.2021.3051419.
[2]J.Du,F.R.Yu,G.Lu,J.Wang,J.Jiang and X.Chu,"MEC-Assisted Immersive VR Video Streaming Over Terahertz Wireless Networks:A Deep Reinforcement Learning Approach,"in IEEE Internet of Things Journal,vol.7,no.10,pp.9517-9529,Oct.2020,doi:10.1109/JIOT.2020.3003449.
[3]X.Liu and Y.Deng,"A Decoupled Learning Strategy for MEC-enabled Wireless Virtual Reality(VR)Network,"2021IEEE International Conference on Communications Workshops(ICC Workshops),2021,pp.1-6,doi:10.1109/ICCWorkshops50388.2021.9473847.
[4]C.Zheng,S.Liu,Y.Huang and L.Yang,"Hybrid Policy Learning for Energy-Latency Tradeoff in MEC-Assisted VR Video Service,"in IEEE Transactions on Vehicular Technology,vol.70,no.9,pp.9006-9021,Sept.2021,doi:10.1109/TVT.2021.3099129.
Disclosure of Invention
Aiming at the problems, the invention provides a rendering perception-based dynamic placement method of a VR service module in an MEC network, which tightly combines the unloading of the rendering task of a VR game with the placement of the service module; meanwhile, in order to further reduce the end-to-end delay of VR video delivery, the routing delay of VR video stream and the migration cost of service module are optimized in a combined manner; the goal is to minimize the sum of network long time overheads while satisfying the tolerable delay constraints of each game user.
The rendering perception-based VR service module dynamic placement method in the MEC network comprises the following specific steps:
step one, building a cellular network scene comprising U user players, M base stations and H VR games.
The user set is represented as
Figure BDA0003770487900000021
Each base station is respectively provided with an MEC server, and the base station set is represented as
Figure BDA0003770487900000022
The base stations are connected through wires;
there are H types of VR games, the set being
Figure BDA0003770487900000023
While H different service modules are required to support the corresponding VR games.
Step two, aiming at the current time slot t, respectively calculating the placement cost, the migration cost, the rendering cost and the communication cost of the time slot;
time slot set representationIs composed of
Figure BDA0003770487900000031
a) The placement cost calculation formula is as follows:
Figure BDA0003770487900000032
λmhthe storage resource occupation cost required for placing the service module of the VR game h on the MEC server m is represented;
Figure BDA0003770487900000033
indicating whether the MEC server m stores an index quantity of a service module of the VR game h at the time slot t; if so, then
Figure BDA0003770487900000034
If not, then,
Figure BDA0003770487900000035
b) The migration cost calculation formula is as follows:
Figure BDA0003770487900000036
wherein the content of the first and second substances,
Figure BDA0003770487900000037
fhrepresenting the cost of reconfiguration of the service modules for VR game h.
Figure BDA0003770487900000038
ghRepresenting the migration delay of a service module of the VR game h, wherein upsilon represents a regulation parameter, and the reconfiguration cost is enabled by regulating the value of upsilon
Figure BDA0003770487900000039
And migration delay
Figure BDA00037704879000000310
The magnitude of (a) is in the same order of magnitude.
c) The rendering cost calculation formula is as follows:
Figure BDA00037704879000000311
Figure BDA00037704879000000312
an index amount indicating whether VR game h selects to perform rendering on MEC server m, and if so,
Figure BDA00037704879000000313
if not, then the mobile terminal can be switched to the normal mode,
Figure BDA00037704879000000314
Figure BDA00037704879000000315
representing the computing resources required by VR game h at time slot t;
Figure BDA00037704879000000316
representing the computing resources allocated to the VR game h by the MEC server m at the time slot t;
d) The communication cost calculation formula is as follows:
Figure BDA00037704879000000317
Figure BDA00037704879000000318
the sum of downlink transmission time delays of all users in a time slot t is represented, namely the network downlink communication cost;
Figure BDA00037704879000000319
Figure BDA0003770487900000041
representing the downlink transmission delay of a user u at a time slot t; the calculation formula is as follows:
Figure BDA0003770487900000042
Figure BDA0003770487900000043
an index quantity indicating whether the user u accesses the MEC server m at the time slot t, and if so,
Figure BDA0003770487900000044
if not, then,
Figure BDA0003770487900000045
Figure BDA0003770487900000046
indicating the size of the video stream to be transmitted to user u at time slot t;
Figure BDA0003770487900000047
represents the uplink transmission rate between the user u and the MEC server m at the time slot t;
Figure BDA0003770487900000048
representing the routing delay of all users at the time slot t, namely the routing overhead of the network; the calculation formula is as follows:
Figure BDA0003770487900000049
Figure BDA00037704879000000410
representing the time delay of the rendered video stream to the user u to access the MEC server at the time slot t; the formula is as follows:
Figure BDA00037704879000000411
Figure BDA00037704879000000412
an index amount indicating whether the user u joins the VR game h; if the number of the data packets is more than the preset value,
Figure BDA00037704879000000413
if not, then,
Figure BDA00037704879000000414
Figure BDA00037704879000000415
an index quantity indicating whether the user u accesses the MEC server m' at the time slot t, and if so,
Figure BDA00037704879000000416
if not, then,
Figure BDA00037704879000000417
d (m, m ') represents a routing delay between the MEC server m to the MEC server m'.
And step three, constructing an objective function and constraint conditions based on the placement cost, the migration cost, the rendering cost and the communication cost of each time slot, and enabling the total cost of the whole network to be minimum under the delay constraint of each user.
The objective function is:
Figure BDA00037704879000000418
Figure BDA00037704879000000419
Figure BDA00037704879000000420
Figure BDA00037704879000000421
Figure BDA00037704879000000422
Figure BDA00037704879000000423
C6:Du',t≤Du
Figure BDA00037704879000000424
ε1，ε2，ε3and ε4For the weight coefficients, the communication costs are respectively corresponded
Figure BDA00037704879000000425
Cost of rendering
Figure BDA00037704879000000426
Cost of placement
Figure BDA0003770487900000051
And migration cost
Figure BDA0003770487900000052
The proportion in the objective function.
Constraint C1Ensuring that a user cannot connect to multiple MEC servers simultaneously, while each user can connect to one MEC server;
constraint C2Ensuring that one VR game can be executed in one time slot and only one MEC server can be selected to execute the rendering task;
constraint C3Ensuring that the total bandwidth allocated to the access user by the MEC server m does not exceed the whole bandwidth of the wireless access network in each time slot;
Figure BDA0003770487900000053
a spectrum bandwidth representing the time slot t allocated to the user u from the MEC server m; b istRepresents the entire bandwidth of the radio access network;
constraint C4Ensuring that the computing resources allocated by the MEC server to the VR games it serves cannot exceed its maximum computing resources; k ismIs the maximum computing power of MEC server m.
Constraint C5It is ensured that the service module size of the VR game stored in the MEC server m does not exceed the maximum storage capacity of the MEC server m. w is ahData size of service module for VR Game h
Constraint C6Ensure that the total delay of each VR game cannot exceed its maximum tolerated delay. DuThe maximum tolerated delay for VR game h. From the above description, the actual end-to-end delay of user u over time period t can be expressed as:
Figure BDA0003770487900000054
wherein
Figure BDA0003770487900000055
Indicating the downlink transmission delay of the rendered video stream transmitted by the access base station of the user u to the user u at the time slot t,
Figure BDA0003770487900000056
indicating the routing delay required for the rendered video stream to be routed from the rendering base station to the access base station of user u at time slot t,
Figure BDA0003770487900000057
representing the rendering delay required for rendering the video stream requested by user u at time slot t,
Figure BDA0003770487900000058
indicating migration of VR service module required by user u to corresponding rendering at time slot tThe migration delay required by the base station is dyed.
And step four, decomposing the objective function into two subproblems on the premise of meeting the constraint condition: dynamic access and service module placement problems and quasi-static resource allocation problems; and respectively solving by using a minimum cut theory and a convex optimization method to obtain an optimal user access scheme, an optimal VR service module placement scheme and an optimal spectrum resource distribution scheme.
The invention has the advantages that:
1. the rendering perception-based dynamic placement method for the VR service module in the MEC network reduces the overhead of the whole system in the VR game video stream delivery process, including the consumption cost and the time delay cost of various network resources. Simulation results show that the dynamic placement method of the rendering-aware service module can flexibly select the user access base station and the rendering task execution base station according to the real-time position of the user, and finally the cost of the whole system is minimized in a long and continuous time.
2. The rendering perception-based dynamic placement method for the VR service module in the MEC network can meet the requirements of low time delay of VR game service and real-time movement of users, so that the computing capacity of terminal equipment and the cache capacity of an edge server are no longer barriers for providing low-time-delay wireless VR service.
3. The rendering perception-based dynamic placement method for the VR service module in the MEC network realizes a better balance between the route delay cost of the rendered VR video stream and the migration cost of the corresponding VR service module.
Drawings
FIG. 1 is a flowchart of a rendering awareness-based dynamic placement method for VR service modules in an MEC network according to the present invention;
FIG. 2 is a schematic diagram of a cellular network scenario constructed in accordance with the present invention, including a player, a base station, and a VR gaming service module;
FIG. 3 is a graph comparing the effect of the total network overhead of the method and the particle swarm algorithm with the iteration time;
FIG. 4 is a graph comparing the relationship between the computing power of the method and the particle swarm algorithm MEC server and the average delay of the user;
FIG. 5 is a comparison graph of the relationship between the computing power and the total network overhead of the MEC server of the method and the particle swarm algorithm;
FIG. 6 is a graph comparing the effect of the delay constraints of the method and the particle swarm optimization on the total network overhead and the average user delay;
FIG. 7 is a comparison graph of the actual time delay of the user under the condition of no time delay constraint and tolerable time delay of the method and the particle swarm optimization.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
To meet the low latency requirements of wireless VR gaming services in a multidimensional (storage-computation-communication) constrained MEC network. The invention discloses a rendering perception-based dynamic placement method for VR service modules in an MEC network, which is used for unloading rendering tasks of VR games to an MEC server and tightly combining unloading decisions with service module placement. Meanwhile, in order to further reduce the end-to-end delay of VR video delivery, in the placement method, the routing delay of VR video stream and the migration cost of the service module are optimized in a combined manner; specifically, the scheme optimizes the cost of migrating VR service modules between different base stations between adjacent time slots while considering the reasonable allocation of bandwidth, calculation and storage resources in each time slot. The goal is to minimize the sum of the network long-time spending under the condition of satisfying the tolerable delay constraint of each game user, namely the sum of the total delay of all users and all consumed network resources in a period of time; for this purpose, the invention is modeled as a high-order, non-convex and time-varying function, and the placement problem is converted into the minimum cutting problem by constructing a series of auxiliary graphs. Finally, a two-stage iterative algorithm based on convex optimization and graph theory is provided to solve the objective function. A large number of simulation results show that compared with other baseline algorithms, the method can ensure lower end-to-end delay and lower network cost.
As shown in fig. 1, the rendering-aware-based VR service module dynamic placement method in the MEC network specifically includes the following steps:
step one, building a cellular network scene comprising U user players, M base stations and H VR game service modules.
The MEC server is a micro data center, typically deployed with a cellular base station or WiFi access point. Hardware resources in the MEC server are virtualized by adopting some lightweight virtualization technologies, and flexible sharing of the resources is achieved.
As shown in fig. 2, in a cellular network with MEC servers deployed, there are U players and M Base Stations (BSs), each deployed with one MEC server. The user set is represented as
Figure BDA0003770487900000061
Each base station is respectively provided with an MEC server, and the base station set is represented as
Figure BDA0003770487900000071
The base stations are connected through wires; there are H types of VR games in the scene, the set is
Figure BDA0003770487900000072
Therefore, H different VR service modules are needed to support VR games.
In addition, to make dynamic decisions, the problem is modeled as a slotted system and used
Figure BDA0003770487900000073
To represent a contiguous set of time slots, each of which is assumed to be much larger than the delay incurred by transmission and processing.
Step two, aiming at the current time slot t, respectively calculating the placement cost, the migration cost, the rendering cost and the communication cost of the time slot;
a) Cost of placement
Assume that VR Game service Module places a set for policy
Figure BDA0003770487900000074
To indicate. When in use
Figure BDA0003770487900000075
Then, the MEC server m stores a service module of the VR game h in the time slot t; otherwise
Figure BDA0003770487900000076
λ is used for storage resource occupation cost required for placing service module of VR game h on MEC server mmhRepresents; the total placement cost of the VR game service module is expressed by the following equation:
Figure BDA0003770487900000077
suppose that the storage capacity of the server m is pimThe size of the service module of the VR game h is omegahSince the total size of the VR game service modules deployed at server m should not exceed the maximum storage capacity of that server, the constraint on placement cost should be expressed as:
Figure BDA0003770487900000078
Πmrepresenting the storage capacity, ω, of the MEC server mhRepresents the size of a service module of the VR game h;
b) And migration cost
As the user moves, the base station transmitting the rendered data to the user may change due to changes in geographic location. At the same time, a server that originally provided rendering services for a group of users may no longer be the best choice to provide rendering services. Thus, the system may need to select a new suitable base station for the group of users to render, and may even need to re-deploy the corresponding VR game service module on the newly selected BS. That is, the data information of the traffic module may need to be migrated from the old MEC server to the new MEC server and the environment built on the new MEC. However, migration of VR game service modules incurs hardware loss costs and data migration delay costs.
The migration delay for each user belonging to the same group is equal and can be expressed as:
Figure BDA0003770487900000079
Figure BDA00037704879000000710
an index value indicating whether the user u joins the service module of the VR game h; if so, the mobile terminal can be started,
Figure BDA00037704879000000711
if not, then,
Figure BDA00037704879000000712
Figure BDA00037704879000000713
a delay representing migration of a service module of the VR game h, which was not stored in the MEC server m at the previous time slot, into the MEC server m at the time slot t;
in addition, all migration costs are expressed as:
Figure BDA0003770487900000081
wherein the content of the first and second substances,
Figure BDA0003770487900000082
fhrepresenting the cost of reconfiguration of the service modules for VR game h.
Figure BDA0003770487900000083
ghRepresenting the migration delay of a service module of the VR game h, wherein upsilon represents a regulation parameter, and the reconfiguration cost is enabled by regulating the value of upsilon
Figure BDA0003770487900000084
And migration delay
Figure BDA0003770487900000085
The magnitude of (a) is in the same order of magnitude.
c) Rendering cost
There may be overlapping computing tasks for the same group of users, and the computing resources on each server are allocated in groups, assuming that the MEC server performs centralized computing after collecting all the information for the same group of players. In an MEC network, when an MEC server serves only one group, the group may have more computing resources available to perform rendering, resulting in a lower processing latency experience.
However, each MEC server typically needs to serve multiple game groups simultaneously, which may result in competition for computing resources. In particular, if one MEC server provides rendering services for too many game groups, the rendering delay for all game groups performing rendering tasks at that server will increase significantly. By indicating quantities
Figure BDA0003770487900000086
To indicate whether user u joins VR game h. Since one participant can only participate in one game, the corresponding constraint is expressed as:
Figure BDA0003770487900000087
by using
Figure BDA0003770487900000088
To represent a set of base station selection strategies being presented. When VR game h selects MEC server m to perform a rendering task,
Figure BDA0003770487900000089
otherwise
Figure BDA00037704879000000810
In addition, in order to ensure information synchronization between users in the same group, assuming that a group of users can only select one MEC server to process tasks at one time point, the constraint condition can be expressed as:
Figure BDA00037704879000000811
because the cost of placing the VR game service module on the server is high, the invention places the VR game service module on the base station for processing the rendering task of the game group, and obtains the following formula:
Figure BDA00037704879000000812
assume that the maximum computing power of MEC server m is KmThe computing resources allocated to VR game h by server m at time slot t are
Figure BDA00037704879000000813
By using
Figure BDA00037704879000000814
To represent an allocation scheme for computing resources.
Figure BDA00037704879000000815
Indicating the computational resources required by VR game h at time slot t. The rendering delays for the same set of players are equal. Therefore, the rendering delay of user u at time slot t is represented as:
Figure BDA0003770487900000091
the rendering cost may be represented by the sum of the rendering delays of all game groups:
Figure BDA0003770487900000092
meanwhile, the sum of the computing resources allocated by the MEC server to all game groups it serves cannot exceed its maximum computing resource. Thus, the corresponding computational resource constraint can be expressed as:
Figure BDA0003770487900000093
d) Cost of communication
The invention provides a communication model in a mobile edge computing network based on mmWave, which is mainly focused on downlink transmission. Meanwhile, route transmission delay is introduced. By using
Figure BDA0003770487900000094
To indicate access policies and, furthermore, users cannot connect to multiple base stations simultaneously, there is a need to ensure that each player can connect to an appropriate base station. Thus, the following constraint equation is obtained:
Figure BDA0003770487900000095
the time slot t is allocated to the user u from the MEC server m with the spectrum bandwidth of
Figure BDA0003770487900000096
In combination with
Figure BDA0003770487900000097
As a bandwidth allocation scheme. Since the total bandwidth allocated to its access subscribers by MEC server m does not exceed the entire bandwidth of the radio access network at time slot t, i.e. BtThe corresponding bandwidth constraint is expressed as:
Figure BDA0003770487900000098
then the uplink transmission rate between user u and MEC server m at time slot t is:
Figure BDA0003770487900000099
suppose that the time slot t requires the size of the video stream transmitted to user u to be
Figure BDA00037704879000000910
The downlink transmission delay is:
Figure BDA00037704879000000911
Figure BDA00037704879000000912
an index quantity indicating whether the user u accesses the MEC server m at the time slot t, and if so,
Figure BDA00037704879000000913
if not, then,
Figure BDA00037704879000000914
the sum of the downlink transmission delays of all users in the time slot t, that is, the network downlink communication cost, is:
Figure BDA0003770487900000101
and dividing the VR games into H groups according to the difference of the VR games participated by the user, wherein different game groups need different service modules for rendering. An appropriate MEC server needs to be selected to render for VR game h and then the rendered video stream is quickly routed to the visiting base station of user u participating in VR game h. Wherein, the selected MEC server needs to deploy a corresponding VR service module and has enough computing resources to execute the rendering task. According to the above assumption, at time slot t, the time delay of the rendered video stream to reach the MEC server of user u is represented as:
Figure BDA0003770487900000102
wherein
Figure BDA0003770487900000103
An index quantity indicating whether the user u accesses the MEC server m' at the time slot t, and if so,
Figure BDA0003770487900000104
if not, then,
Figure BDA0003770487900000105
d (m, m ') is the routing delay between the MEC server m to the base station MEC server m'. The routing delay of all users in time slot t, i.e. the routing overhead of the network, is:
Figure BDA0003770487900000106
therefore, the communication overhead of the time slot t can be expressed as the sum of the downlink transmission delay and the routing delay.
Figure BDA0003770487900000107
And step three, constructing an objective function and constraint conditions based on the placement cost, the migration cost, the rendering cost and the communication cost of each time slot, and enabling the total cost of the whole network to be minimum under the delay constraint of each user.
The goal of rendering aware based service module dynamic placement strategy is to minimize the total cost of the entire network over a long period of time while satisfying the delay constraints of each user. The strategy simultaneously considers a resource allocation scheme in each time slot and an inter-base station service module migration scheme between adjacent time slots.
The objective function is:
Figure BDA0003770487900000111
Figure BDA0003770487900000112
Figure BDA0003770487900000113
Figure BDA0003770487900000114
Figure BDA0003770487900000115
Figure BDA0003770487900000116
C6:Du',t≤Du
Figure BDA0003770487900000117
ε1，ε2，ε3and ε4For the weight coefficients, the communication costs are respectively corresponded
Figure BDA0003770487900000118
Cost of rendering
Figure BDA0003770487900000119
Cost of placement
Figure BDA00037704879000001110
And migration cost
Figure BDA00037704879000001111
The proportion in the objective function.
Constraint C1Ensuring that a user cannot connect to multiple MEC servers simultaneously, while each user can connect to one MEC server;
constraint C2Ensuring that one VR game can be executed in one time slot and only one MEC server can be selected to execute the rendering task;
constraint C3Securing MEC serversm total bandwidth allocated to its access users, not exceeding the entire bandwidth of the wireless access network in each time slot;
Figure BDA00037704879000001112
a spectrum bandwidth representing the time slot t allocated to the user u from the MEC server m; b istRepresents the entire bandwidth of the radio access network;
constraint C4Ensuring that the computing resources allocated by the MEC server to the VR games it serves cannot exceed its maximum computing resources; k ismIs the maximum computing power of MEC server m.
Constraint C5It is ensured that the VR game service module size stored in the MEC server m does not exceed the maximum storage capacity of the MEC server m. w is ahIs the data size of the service module of VR game h.
Constraint C6Ensure that the total delay of each VR game cannot exceed its maximum tolerated delay. DuThe maximum tolerated delay for VR game h. From the above description, the actual end-to-end delay of user u in slot t can be expressed as:
Figure BDA00037704879000001113
wherein
Figure BDA0003770487900000121
Indicating the downlink transmission delay of the rendered video stream transmitted by the access base station of the user u to the user u at the time slot t,
Figure BDA0003770487900000122
indicating the routing delay required for the rendered video stream to be routed from the rendering base station to the access base station of user u at time slot t,
Figure BDA0003770487900000123
representing the rendering delay required for rendering the video stream requested by user u at time slot t,
Figure BDA0003770487900000124
and the migration time delay required for migrating the VR service module required by the user u to the corresponding rendering base station in the time slot t is represented.
And step four, decomposing the objective function into two subproblems on the premise of meeting the constraint condition: dynamic access and service module placement problems and quasi-static resource allocation problems; and respectively solving by using a minimum cut theory and a convex optimization method to obtain an optimal user access scheme, an optimal VR service module placement scheme and an optimal spectrum resource distribution scheme.
The method comprises the following main steps:
1) Establishing an initial user access model, a VR service module placement model and a resource distribution model;
2) Constructing an auxiliary graph and giving each edge in the graph an appropriate weight. And determining whether to replace the access base stations of some users and the placing base stations of some VR service modules by the base station alpha by determining the minimum cut set of the auxiliary graph.
3) On a given access and placement scheme, the computational and spectral resources at each base station are allocated rationally to optimize the overhead per slot and further optimize the network overhead for the entire system.
The two subproblems are respectively expressed by the following formulas:
Figure BDA0003770487900000125
Figure BDA0003770487900000126
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003770487900000127
represents a base station providing rendering service for VR game h at time slot t when
Figure BDA0003770487900000128
When the temperature of the water is higher than the set temperature,
Figure BDA0003770487900000129
in the same way, the method for preparing the composite material,
Figure BDA00037704879000001210
represents a base station providing rendering service for VR game h at time slot t-1 when
Figure BDA00037704879000001211
When the temperature of the water is higher than the set temperature,
Figure BDA00037704879000001212
Figure BDA00037704879000001213
indicates that user u accesses the base station in time slot t when
Figure BDA00037704879000001214
When the temperature of the water is higher than the set temperature,
Figure BDA00037704879000001215
and the number of the first and second electrodes,
Figure BDA00037704879000001216
indicating that at time slot t, the base station
Figure BDA00037704879000001217
The size of the bandwidth resource allocated to user u;
Figure BDA00037704879000001218
indicating that at time slot t, the base station
Figure BDA00037704879000001219
The size of the computing resources allocated to VR game h.
Figure BDA00037704879000001220
Indicates that in time slot t, user u and its access base station
Figure BDA00037704879000001221
The signal to noise ratio therebetween.
Figure BDA00037704879000001222
Indicating the computational resources required by VR game h at time slot t.
(1) Aiming at the first subproblem, optimizing the dynamic access and service module dynamic placement problem through a minimal cut theory;
firstly, introducing an alpha expansion algorithm; then, an auxiliary graph is constructed, and each edge in the graph is given a proper weight to optimize the objective function. The minimum value of the objective function is equal to the sum of the weights of all edges in the minimum cut set of the auxiliary graph;
the alpha extension is defined as binary optimization and reflects the trend of the service module moving from the current base station to the base station alpha and the trend of the user accessing the current base station to the base station alpha. For example when base station alpha is selected as the extension of the access base station.
Figure BDA0003770487900000131
There is a binary choice, when user u continues to maintain access to the current base station,
Figure BDA0003770487900000132
when user u changes the access base station to base station alpha,
Figure BDA0003770487900000133
for convenience of calculation, the expanded result can also be an index vector with binary decision variables
Figure BDA0003770487900000134
And (4) showing. When the temperature is higher than the set temperature
Figure BDA0003770487900000135
When the temperature of the water is higher than the set temperature,
Figure BDA0003770487900000136
otherwise
Figure BDA0003770487900000137
When in use
Figure BDA0003770487900000138
Time of flight
Figure BDA0003770487900000139
Otherwise
Figure BDA00037704879000001310
After base station alpha spreading, using binary variable
Figure BDA00037704879000001311
Reconstructed CostIIs composed of
Figure BDA00037704879000001312
Converting a sub-problem into a binary optimization problem with a variable of 0 or 1
Figure BDA00037704879000001313
At the same time, define
Figure BDA00037704879000001314
Then, an auxiliary graph is constructed
Figure BDA00037704879000001315
And each edge is assigned with a weight value;
in the auxiliary graph, there are T points and U points corresponding to the player as the credit
Figure BDA00037704879000001316
Game group notation with T x H vertices
Figure BDA00037704879000001317
A source node S and a destination node T. Edges are added to the graph and each edge is given an appropriate weight.
Connecting the vertex
Figure BDA00037704879000001318
And vertex
Figure BDA00037704879000001319
Are connected and weighted as
Figure BDA00037704879000001320
Connecting the vertex
Figure BDA00037704879000001321
And vertex
Figure BDA00037704879000001322
Are connected and weighted as
Figure BDA00037704879000001323
Connecting the vertex
Figure BDA00037704879000001324
Is connected with a terminal node T and is weighted as
Figure BDA00037704879000001325
Connecting the vertex
Figure BDA00037704879000001326
Is connected with the terminal point T and is weighted as
Figure BDA00037704879000001327
Connecting the vertex
Figure BDA00037704879000001328
Is connected with the source node S and is weighted to be lambdaαm。
Through the assignment mode, the minimum value of the target function is equal to the sum of the weights of all edges in the minimum cut set of the auxiliary graph.
Dividing the vertexes in the graph into two sets by using a minimal cut algorithm, assigning the vertexes which are positioned in the same set with the source node S as 0, and assigning the vertexes which are positioned in the same set with the terminal node T as 1, thereby determining the index variable
Figure BDA00037704879000001329
And
Figure BDA00037704879000001330
can determine the iteration process by back-stepping
Figure BDA00037704879000001331
And
Figure BDA00037704879000001332
the value of (a). Through multiple iterations, a user access scheme and a VR service module placement scheme can be finally determined.
(2) Resource allocation scheme based on convex optimization
Cost is to be paid attention to for the second sub-problemIIBy properly allocating computational and spectral resources, the total transmission and editing delay in each time interval is minimized.
When in use
Figure BDA0003770487900000141
And
Figure BDA0003770487900000142
when determined, the original optimization problem can be expressed as:
Figure BDA0003770487900000143
Figure BDA0003770487900000144
Figure BDA0003770487900000145
wherein Λ is a penalty function, and the optimization problem is a convex problem. The ADMM algorithm and the CVX tool box are used for solving the problems to obtain a distribution scheme of computing resources and spectrum resources, namely
Figure BDA0003770487900000146
And
Figure BDA0003770487900000147
the value of (a).
(3) Two-stage iterative algorithm based on alpha expansion
Because the original problem has more optimization variables, the algorithm complexity is higher. In order to reduce the complexity of the algorithm and obtain the optimal solution of the original problem, the original problem is solved in two steps. And obtaining an optimal user access scheme and a VR service module placement scheme after one round of alpha expansion according to the algorithm 1. Substituting the results of the two optimal schemes into an algorithm 2 as fixed values, and solving the allocation scheme of the calculation sum spectrum resources in each time slot according to the algorithm 2. And calculating an objective function through a user access scheme obtained by the iteration of the round, a VR service module placement scheme, and a calculation and spectrum resource allocation scheme. And comparing the objective function value calculated in the current round with the historical optimal result, and keeping the smaller value of the objective function value and the historical optimal result and the corresponding problem solution. And carrying out the next iteration and repeating the process.
The invention considers that the buffer capacity of the edge base station is limited, when the user moves, new access base stations and working base stations for executing rendering tasks are allocated to the current user again according to the current environment, such as channel state, the size of calculation tasks unloaded by each user, the current placement position of a VR service module and the like, and meanwhile, required working data are transferred from the original working base station to the new working base stations so as to meet the requirement of low time delay of the wireless VR game service.
The present invention uses MATLAB for simulation. Consider a 100m x 100m cellular network scenario with 100 users and 10 base stations. The data used for the simulation are shown in table 1 below.
TABLE 1
Total bandwidth 1GHz
Kind of game 40
Coefficient of downlink path loss [2.75-4.75]
Video stream size [5-20]Mb
Server storage space [400-900]G
Server computing power [30-80]GHz
As shown in fig. 3, the minimum value of the total network overhead is found through iteration under the condition that the maximum computing capacity of each MEC server is 60ghz and each server can place data with the size of 600G at most. The total network overhead of the method and the particle swarm algorithm is rapidly reduced along with the increase of the iteration times at the beginning, and then the total network overhead is converged and kept at an almost constant value. As can be seen from the iteration chart, the convergence time of the method is about 18 generations, and the convergence time of the particle swarm algorithm is about 25 generations. Therefore, compared with other schemes, the method converges faster in the iterative process and keeps the total network overhead at the lowest.
As shown in fig. 4, which is the relationship between the computing power of the MEC server and the average delay of the users; as shown in fig. 5, is the relationship between the computing power of the MEC server and the total network overhead. In the above two figures, as the computing power of the MEC server increases, the average delay of the user and the total network overhead are greatly reduced. This is mainly because the more computing resources an MEC server can provide to a user, the less delay is required to perform rendering. Meanwhile, the richer the computing resources on the MEC server, the more MEC servers are available for the system to select to provide rendering services for each game group, thus saving the network cost of routing to some extent.
As shown in fig. 6, the average delay and total network overhead of users without delay constraint are compared with the average delay and total network overhead of users with delay constraint. The network parameters are that the maximum computing capacity of each MEC server is 60GHz, and the storage capacity is 600g. When the delay constraint of each user is not required to be considered, the feasible domain of the target problem becomes large, and compared with the time delay constraint, the total network overhead is reduced, but the average delay of the users is increased. At the same time, some users cannot complete the corresponding video processing task within a tolerable delay, as shown in fig. 7.

Claims (4)

  1. A rendering perception-based VR service module dynamic placement method in an MEC network is characterized by comprising the following specific steps:
    firstly, building a cellular network scene comprising U user players, M base stations and H VR game service modules;
    the set of U users is represented as:
    Figure FDA0003770487890000011
    the set of M base stations is represented as:
    Figure FDA0003770487890000012
    there are H types of VR games, the set being
    Figure FDA0003770487890000013
    H different VR service modules are needed to support corresponding VR games;
    then, aiming at the current time slot t, respectively calculating the placement cost, the migration cost, the rendering cost and the communication cost of the time slot;
    the set of time slots is represented as
    Figure FDA0003770487890000014
    Establishing a target function and a constraint condition based on the placement cost, the migration cost, the rendering cost and the communication cost of each time slot, and enabling the total cost of the whole network to be minimum under the delay constraint of each user;
    the objective function is:
    Γ1:
    Figure FDA0003770487890000015
    s.t.C1:
    Figure FDA0003770487890000016
    C2:
    Figure FDA0003770487890000017
    C3:
    Figure FDA0003770487890000018
    C4:
    Figure FDA0003770487890000019
    C5:
    Figure FDA00037704878900000110
    C6:Du',t≤Du
    Figure FDA00037704878900000111
    Figure FDA00037704878900000112
    it is meant that the access policy,
    Figure FDA00037704878900000113
    as a scheme for the allocation of the bandwidth,
    Figure FDA00037704878900000114
    representing an allocation scheme of the computing resources,
    Figure FDA00037704878900000115
    indicating the presentation of a set of base station selection strategies,
    Figure FDA00037704878900000116
    representing a VR gaming service module placement strategy;
    ε1，ε2，ε3and ε4For the weight coefficients, the communication costs are respectively corresponded
    Figure FDA00037704878900000117
    Cost of rendering
    Figure FDA00037704878900000118
    Cost of placement
    Figure FDA00037704878900000119
    And migration cost
    Figure FDA00037704878900000120
    The proportion in the objective function;
    constraint C1Ensuring that a user cannot connect to multiple MEC servers simultaneously, while each user can connect to one MEC server;
    Figure FDA00037704878900000121
    an index quantity indicating whether the user u accesses the MEC server m at the time slot t, and if so,
    Figure FDA00037704878900000122
    if not, then the mobile terminal can be switched to the normal mode,
    Figure FDA00037704878900000123
    constraint C2Ensuring that one VR game can be executed in one time slot and only one MEC server can be selected to execute the rendering task;
    Figure FDA00037704878900000124
    an index amount indicating whether the service module of VR game h selects MEC server m, and if so,
    Figure FDA00037704878900000125
    if not, then,
    Figure FDA00037704878900000126
    constraint C3Ensuring that the total bandwidth allocated to its access user by the MEC server m does not exceed the entire bandwidth of the radio access network in each timeslot;
    Figure FDA00037704878900000214
    a spectrum bandwidth representing the time slot t allocated to the user u from the MEC server m; b istRepresents the entire bandwidth of the radio access network;
    constraint C4Ensuring that the computing resources allocated by the MEC server to the VR games it serves cannot exceed its maximum computing resources;
    Figure FDA0003770487890000021
    representing the computing resources allocated to the VR game h by the MEC server m at the time slot t; kmIs the maximum computing power of MEC server m;
    constraint C5Ensuring that the size of VR game service module stored in MEC server m does not exceed the maximum storage capacity Π of MEC server mm；
    Figure FDA0003770487890000022
    Indicating whether the MEC server m stores an index quantity of a service module of the VR game h at the time slot t; if so, then
    Figure FDA0003770487890000023
    If not, then,
    Figure FDA0003770487890000024
    whthe data size of a service module for the VR game h;
    constraint C6Ensuring that the total delay of each VR game cannot exceed its maximum tolerated delay; d'u,tActual end-to-end delay of user u at time period t; duMaximum tolerated delay for VR game h;
    and finally, decomposing the objective function into two subproblems on the premise of meeting the constraint condition: dynamic access and service module placement problems and quasi-static resource allocation problems; and respectively solving by using a minimum cut theory and a convex optimization method to obtain an optimal user access scheme, an optimal VR service module placement scheme and an optimal spectrum resource distribution scheme.
  2. 2. The method of claim 1, wherein in the network scenario, each base station is respectively deployed with an MEC server, and the base stations are connected by a wire.
  3. 3. The method of claim 1, wherein the placement cost calculation formula is:
    Figure FDA0003770487890000026
    λmhthe storage resource occupation cost required for placing the service module of the VR game h on the MEC server m is represented;
    the migration cost calculation formula is as follows:
    Figure FDA0003770487890000027
    wherein the content of the first and second substances,
    Figure FDA0003770487890000028
    fhrepresenting a cost of reconfiguration of the service modules of the VR game h;
    Figure FDA0003770487890000029
    ghrepresenting the migration delay of a service module of the VR game h, wherein upsilon represents a regulation parameter, and the reconfiguration cost is enabled by regulating the value of upsilon
    Figure FDA00037704878900000210
    And migration delay
    Figure FDA00037704878900000211
    The numerical value of (A) is in the same order of magnitude;
    the rendering cost calculation formula is as follows:
    Figure FDA00037704878900000212
    Figure FDA00037704878900000213
    representing the computing resources required by the VR game h at the time slot t;
    the communication cost calculation formula is as follows:
    Figure FDA0003770487890000031
    Figure FDA0003770487890000032
    the sum of the downlink transmission time delay of all users in the time slot t is represented, namely the network downlink communication cost;
    Figure FDA0003770487890000033
    Figure FDA0003770487890000034
    representing the downlink transmission delay of the user u at the time slot t; the calculation formula is as follows:
    Figure FDA0003770487890000035
    Figure FDA0003770487890000036
    indicating the size of the video stream to be transmitted to user u at time slot t;
    Figure FDA0003770487890000037
    represents the uplink transmission rate between the user u and the MEC server m at the time slot t;
    Figure FDA0003770487890000038
    representing the routing delay of all users at the time slot t, namely the routing overhead of the network; the calculation formula is as follows:
    Figure FDA0003770487890000039
    representing the time delay of the rendered video stream to the user u to access the MEC server at the time slot t; the formula is as follows:
    Figure FDA00037704878900000310
    Figure FDA00037704878900000311
    indicates that user u isWhether the index amount of the VR game h is added or not; if so, the mobile terminal can be started,
    Figure FDA00037704878900000312
    if not, then,
    Figure FDA00037704878900000313
    Figure FDA00037704878900000314
    an index quantity indicating whether the user u accesses the MEC server m' at the time slot t, and if so,
    Figure FDA00037704878900000315
    if not, then,
    Figure FDA00037704878900000316
    d (m, m ') represents a routing delay between the MEC server m to the MEC server m'.
  4. 4. The method of claim 1, wherein the actual end-to-end delay D of the user u at time period tu',tExpressed as:
    Figure FDA00037704878900000317
    wherein
    Figure FDA00037704878900000318
    Indicating the downlink transmission delay of the rendered video stream transmitted by the access base station of the user u to the user u at the time slot t,
    Figure FDA00037704878900000319
    indicating the routing delay required for the rendered video stream to be routed from the rendering base station to the access base station of user u at time slot t,
    Figure FDA00037704878900000320
    representing the rendering delay required for rendering the video stream requested by user u at time slot t,
    Figure FDA00037704878900000321
    and the migration time delay required for migrating the VR service module required by the user u to the corresponding rendering base station in the time slot t is represented.
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CN117689096A (en) * 2024-01-25 2024-03-12 武汉科技大学 Mobile charging scheduling method with obstacle avoidance function
CN117689096B (en) * 2024-01-25 2024-04-19 武汉科技大学 Mobile charging scheduling method with obstacle avoidance function

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689096A (en) * 2024-01-25 2024-03-12 武汉科技大学 Mobile charging scheduling method with obstacle avoidance function
CN117689096B (en) * 2024-01-25 2024-04-19 武汉科技大学 Mobile charging scheduling method with obstacle avoidance function

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