CN110958675B - Terminal access method based on 5G fog computing node - Google Patents

Terminal access method based on 5G fog computing node Download PDF

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CN110958675B
CN110958675B CN201911035956.4A CN201911035956A CN110958675B CN 110958675 B CN110958675 B CN 110958675B CN 201911035956 A CN201911035956 A CN 201911035956A CN 110958675 B CN110958675 B CN 110958675B
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CN110958675A (en
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杨龙祥
孙永源
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Nanjing University of Posts and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
    • H04W52/02—Power saving arrangements
    • H04W52/0203—Power saving arrangements in the radio access network or backbone network of wireless communication networks
    • H04W52/0206—Power saving arrangements in the radio access network or backbone network of wireless communication networks in access points, e.g. base stations
    • 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
    • H04W28/10—Flow control between communication endpoints
    • H04W28/14—Flow control between communication endpoints using intermediate storage
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W48/00—Access restriction; Network selection; Access point selection
    • H04W48/20—Selecting an access point
    • 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
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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Abstract

The invention provides a terminal access scheme based on a 5G fog computing node, which comprises the following steps: step 1) constructing a system model of a fog wireless access node according to an information source, a fog wireless network interface and a processor queue; step 2) constructing a target function according to actual data of an information source reaching a fog wireless network interface; step 3) constructing a virtual queue according to the system power, and constructing a second-order Lyapunov equation according to the actual queue and the virtual queue; step 4), selecting a proper penalty factor and a control strategy optimization objective function; and step 5) selecting information source access fog wireless network by using the optimized objective function, comprehensively considering parameters such as throughput, average waiting time and the like of a fog computing system, constructing a more practical utility function, realizing an effective terminal access scheme according to the utility function, and being applicable to internet communication.

Description

Terminal access method based on 5G fog computing node
Technical Field
The invention relates to a novel fog node queue iteration strategy scheme, in particular to a terminal access method based on a 5G fog computing node, and belongs to the technical field of internet communication.
Background
The proliferation of high data rate mobile broadband devices, particularly the accelerated adoption of smart phones, has led to an exponential increase in the traffic transmitted over mobile networks, which is a central impetus for 5G research and development efforts. Today we are in the age when 5G research efforts reach the peak, i.e. the first trial point 5G network implementation. The 5G network which is deployed commercially in 2020 is expected to meet the requirement of 500-1000 times of the flow of the existing mobile Internet, and the peak value theoretical transmission rate can reach 10 Gbit/s. Compared to existing network infrastructure and design, future 5G systems require: the intelligent device capable of providing mobile broadband service for terminal users has ubiquitous mobility, a powerful Mobile Cloud Computing (MCC) function, a fog computing function, end-to-end communication, higher network utilization rate and other functions. Most importantly, high quality of service (QoS) support, faster computing power, higher energy utilization efficiency and ultra-low latency are provided.
Fog computing, an important component of future 5G networks, is an internet of things (IoT) -oriented distributed computing infrastructure that can extend computing power and data analytics applications to the "edge" of the network, which enables customers to analyze and manage data locally, thereby obtaining immediate services through connections. The fog calculation has the advantages of extremely low delay, distribution, high mobility and the like, and has high research value.
The method is based on a fog wireless access network (F-RAN) model and a second-order Lyapunov optimization equation, algorithm optimization is carried out in a mode of adding control variables to the second-order Lyapunov equation, and maximum throughput, average queue delay and energy efficiency parameters based on the optimization algorithm are provided.
Disclosure of Invention
The invention aims to provide a terminal access method based on a 5G fog computing node, which comprehensively considers parameters such as throughput, average waiting time and the like of a fog computing system, constructs a more practical utility function and realizes an effective terminal access scheme according to the utility function.
The purpose of the invention is realized as follows: a terminal access method based on a 5G fog computing node comprises the following steps:
step 1) constructing a system model of a fog wireless access node according to an information source, a fog wireless network interface and a processor queue;
step 2) constructing an objective function according to actual data of the information source reaching the fog wireless network interface:
maximization:
Figure BDA0002251500740000021
constraint conditions are as follows: 1.
Figure BDA0002251500740000022
2.
Figure BDA0002251500740000023
3.
Figure BDA0002251500740000024
step 3) constructing a virtual queue according to the system power, and constructing a second-order Lyapunov equation according to the actual queue and the virtual queue;
step 4) selecting an appropriate penalty factor and a control strategy optimization objective function:
and (3) minimizing:
Figure BDA0002251500740000025
constraint conditions are as follows: 1.
Figure BDA0002251500740000026
2.
Figure BDA0002251500740000027
constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure BDA0002251500740000028
Step 5) selecting information source access fog wireless network by using the optimized objective function; by optimizing the objective function, the system dynamically selects the most appropriate access scheme according to the input data volume of each interface at the current moment, the current cached data volume of each cache queue and the selected penalty factor, so that the current moment throughput of the system is maximized, and further, the total throughput of the system is maximized.
As a further limitation of the present invention, step 2) specifically comprises:
step 2-1), defining a queue vector at the interface of the fog wireless network as Q (t) ═ Q1(t),Q2(t),...,QM(t)) at time t {1,2,. } is:
Qm(t+1)＝max(Qm(t)+Am(t)-um(t),0)
wherein the content of the first and second substances,
Figure BDA0002251500740000031
variable representing the arrival rate of the m-th queue, parameter wi,mWeight, u, indicating that the mth queue received information from the ith misty wireless network interfacem(t) is the output rate of service variable for the mth processor queue, M ∈ {1, 2.., M };
step 2-2) defining a time-averaged power vector for each processor queue as
Figure BDA0002251500740000032
Wherein
Figure BDA0002251500740000033
Represents the time-averaged power of the mth processor queue, the time-averaged arrival rate xiThe vector of (t) is defined as
Figure BDA0002251500740000034
Wherein
Figure BDA0002251500740000035
A time-averaged arrival rate representing the time-averaged arrival of the received information to the ith interface;
step 2-3) defining a throughput function, i.e. an objective function, as
Figure BDA0002251500740000036
For each fog node, applying a random utility maximization framework to the flow-based fog computing network model, resulting in the following optimization objective functions:
maximization:
Figure BDA0002251500740000037
restraint stripA piece: 1.
Figure BDA0002251500740000038
2.
Figure BDA0002251500740000039
3.
Figure BDA00022515007400000310
constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure BDA00022515007400000311
Constraint 2 indicates that all queues should be rate stable; constraint 3 indicates that the time-averaged power of the mth queue is not greater than the necessary time-averaged power consumption that the system can provide, where pm(t) represents the power generated by the mth processor queue at time t, and
Figure BDA0002251500740000041
is the necessary time-averaged power consumption.
As a further limitation of the present invention, step 3) specifically comprises:
step 3-1) defines the virtual queue of the system as:
Figure BDA0002251500740000042
Zm(t) is the finite queue length;
step 3-2) further defines the combined queue vector as s (t) ═ q (t), z (t) ], and may obtain a second order lyapunov equation:
Figure BDA0002251500740000043
as a further limitation of the present invention, step 4) specifically comprises:
step 4-1), defining the lyapunov drift one-step transfer condition as Δ (S (t)) -E { L (S (t +1)) -L (S (t))) | S (t)) }, and obtaining a lyapunov drift penalty expression:
Δ(S(t))+V·E{u(αL(t),t)|S(t)}
in the above equation, the "penalty" vector process is defined as
Figure BDA0002251500740000044
Wherein
Figure BDA0002251500740000045
V is a control parameter;
step 4-2) matching with a penalty term V.E { u (alpha)L(t), t) S (t) } optimization yields the following upper bound:
Figure BDA0002251500740000046
wherein B is a group of groups represented bym(t),um(t) and
Figure BDA0002251500740000047
the finite constants involved are expressed as:
Figure BDA0002251500740000051
furthermore, the object of the invention is: by observing the real and virtual queue vectors Q (t), Z (t) and the current state ψ (t), the most suitable control strategy action α is selected at each time tL(t)∈Aψ(t) to minimize the right side of the inequality; in this way, the optimization problem is decoupled and simplified to a separate algorithm;
step 4-3) observing newly arriving x for each queue M e {1, 2.. multidot.M } at each time tm(t), actual queue Qm(t) and virtual queue Zm(t) and selecting an appropriate control strategy αL(t) to minimize:
and (3) minimizing:
Figure BDA0002251500740000052
constraint conditions are as follows: 1.
Figure BDA0002251500740000053
2.
Figure BDA0002251500740000054
constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure BDA0002251500740000055
Compared with the prior art, the invention adopting the technical scheme has the following technical effects: according to the technical scheme, when new data reach the fog node, the system can dynamically allocate a queue space for the new data, and the throughput of the system is maximized under the condition that the requirements are met; compared with the traditional algorithm, the method can effectively improve the system throughput of the fog node while ensuring the system energy utilization rate, thereby improving the service quality of the fog node.
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FIG. 1 is a system model diagram of the present invention.
FIG. 2 is a schematic diagram of a system model of the present invention.
Detailed Description
The technical scheme of the invention is further explained in detail by combining the attached drawings:
the system model of the fog wireless access node proposed by the present invention is shown in fig. 1-2. The system model consists of three main components: an information source, a fog wireless network interface, and a processor queue; the information sources of N (i ═ 1, 2., N) different systems are independently and identically distributed, and the Poisson distribution with the parameter lambda is obeyed; the fog wireless network interface comprises N wireless interfaces which respectively process different kinds of information, and each interface has a packet arrival rate x at a time ti(t),i∈{1,2,...,N}，After each information source reaches different fog wireless network interfaces, the fog nodes process the information and distribute the information to different processor queues; the processor queue contains M (M1, 2.., M) different queues, each having a different service rate vector μj(t), the algorithm of the invention ensures that the information from each interface is processed in the processor queue in time by selecting the optimal distribution criterion at each moment t, so as to achieve the maximum system throughput, the minimum system delay and the optimal energy efficiency; in the invention, a processor queue is modeled through a Lyapunov linear system model, and a queue vector is defined as Q (t) -Q (Q)1(t),Q2(t),...,QM(t)) at time t {1,2,. } is:
Qm(t+1)＝max(Qm(t)+Am(t)-u(t),0) (1)
wherein the content of the first and second substances,
Figure BDA0002251500740000061
variable representing the arrival rate of the m-th queue, parameter wi,mA weight indicating that the mth queue receives information from the ith fog wireless network interface; u. ofm(t) is the output rate of service variable for the mth processor queue, M ∈ {1, 2.., M }; after the information is processed through each queue, the present invention attempts to pass through the throughput μ on each processor queuej(t) summing to obtain the maximum total output service rate for all service queues; the invention defines the time-averaged power vector of each processor queue as
Figure BDA0002251500740000062
Wherein
Figure BDA0002251500740000063
Represents the time-averaged power of the mth processor queue; time-averaged arrival rate xiThe vector of (t) is defined as
Figure BDA0002251500740000064
Wherein
Figure BDA0002251500740000065
A time-averaged arrival rate representing the time-averaged arrival of the received information to the ith interface; in addition, the present invention defines the throughput function of the system as
Figure BDA0002251500740000066
At the same time
Figure BDA0002251500740000067
Is also an objective function to be optimized by the invention; then, for each fog node, the invention applies a random utility maximization framework to the flow-based fog computing network model to obtain the following optimization objective functions:
maximization:
Figure BDA0002251500740000071
constraint conditions are as follows: 1.
Figure BDA0002251500740000072
2.
Figure BDA0002251500740000073
3.
Figure BDA0002251500740000074
constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure BDA0002251500740000075
Constraint 2 indicates that all queues should be rate stable; constraint 3 indicates that the time-averaged power of the mth queue is not greater than the necessary time-averaged power consumption (p), where p is the required time-averaged power consumption that the system can providem(t) represents the power generated by the mth processor queue at time t, and
Figure BDA0002251500740000076
is the necessary time-averaged power consumption.
The invention also obtains the virtual queue of the system as follows:
Figure BDA0002251500740000077
obviously for a limited queue length Zm(t) it is easily demonstrated that the average ratio is stable. Furthermore, the present invention defines the combined queue vector as S (t) ═ Q (t), Z (t)]And a second order lyapunov equation can be obtained:
Figure BDA0002251500740000078
to solve the above optimization problem, the algorithm proposed by the present invention uses the Lyapunov drift penalty and uses a fixed penalty control parameter V. Thus, the lyapunov drift one-step transition condition can be defined as Δ (S (t)) ═ E { L (S (t +1)) -L (S (t)) | S (t)) }, and a lyapunov drift penalty expression can be obtained:
Δ(S(t))+V·E{u(αL(t),t)|S(t)} (5)
in the above equation, the present invention defines the "penalty" vector process as
Figure BDA0002251500740000079
Wherein
Figure BDA00022515007400000710
The invention contemplates that the time average of the penalty procedures is less than (or equal to) some target value u0. These penalties represent the action α by the control strategy at time tL(t) the cost of incoming traffic arrival to the queue incurred. The algorithm of the invention passes the control strategy alpha at each instant tL(t) updating the queue vector Q (t) in search of the upper limit of minimization (5), all possible values of S (t) and all control parameters V (V)>0)。
By squaring (1) and (3), the equations are two according to (4) and (5)Adding edges, adding the punishment term V.E { u (alpha)L(t), t) S (t) } are applied to the two sides of the equation, respectively, it is not difficult to find that expression (5) has the following upper bound for all t:
Figure BDA0002251500740000081
wherein B is a group of groups represented bym(t),um(t) and
Figure BDA0002251500740000082
the finite constants involved are expressed as:
Figure BDA0002251500740000083
furthermore, the object of the invention is: by observing the real and virtual queue vectors Q (t), Z (t) and the current state ψ (t), the most suitable control strategy action α is selected at each time tL(t)∈Aψ(t) to minimize the right side of inequality (6). In this way, the algorithm of the present invention is decoupling the optimization problem discussed above to reduce it to a separate algorithm, as follows:
(1) at each time t, a newly arriving x is observed for each queue M e {1,2m(t), actual queue Qm(t) and virtual queue Zm(t) and selecting an appropriate control strategy αL(t) to minimize:
and (3) minimizing:
Figure BDA0002251500740000084
constraint conditions are as follows: 1.
Figure BDA0002251500740000085
2.
Figure BDA0002251500740000086
(2) the problem of average power allocation: at each time t, the virtual queue Z is observedm(t)，m∈{1,2, a.m } and distributing a power vector p (t) on each interface to obtain a power vector containing an auxiliary variable epsilonm(t) solution:
and (3) minimizing:
Figure BDA0002251500740000091
constraint conditions are as follows:
Figure BDA0002251500740000092
(3) and (3) queue updating: for M ∈ {1, 2., M }, the real queue Q is updated according to formula (1) and formula (3), respectivelym(t) and virtual queue Zm(t)。
The invention defines the time delay as
Figure BDA0002251500740000093
Wherein
Figure BDA0002251500740000094
Representing the time delay caused by the mth processor queue at time t, it can be seen that the time delay is lower when the maximum service rate of the processor queue is larger, or the amount of information arriving at the mth processor queue at time t is smaller.
The value of the algorithm proposed by the present invention is to find the theoretical upper bound of energy (or power) consumption, and the lower bound of battery life for the mist wireless node. It is emphasized that a 5G fixed node (5G Cloud-RAN node) has theoretically unlimited energy, because it uses a power supply unit for power supply and a battery backup Uninterruptible Power Supply (UPS) when there is no power supply, which is of course very rare in the core network. The present invention defines an energy queue as a value having an upper bound
Figure BDA0002251500740000095
Wherein
Figure BDA0002251500740000096
Is the upper bound of energy consumption on the mth energy queue, and the expression is:
Figure BDA0002251500740000097
wherein
Figure BDA0002251500740000098
Is a real queue Qm(t) maximum length, we assume
Figure BDA0002251500740000099
On the other hand, T is the bit period,
Figure BDA00022515007400000910
is the maximum energy that can be provided on the mth radio interface, and
Figure BDA00022515007400000911
is the average of the energy consumption of the mth radio interface, defined as
Figure BDA00022515007400000912
The invention will next demonstrate that: for each bit period T (T)>0) The total energy consumption of each wireless interface m in the 5G mobile fog computing node provided by the invention is expressed by an upper limit expression
Figure BDA00022515007400000913
Determining, i.e. satisfying the following inequality:
Figure BDA0002251500740000101
to demonstrate the above upper limit, the present invention will begin with equation (3). Therefore, the upper limit defined above is applied to the virtual queue (3), i.e. the assumption
Figure BDA0002251500740000102
The present invention can obtain the following inequality:
Figure BDA0002251500740000103
if we start with equation (3) and know that t ≧ 0 at each time, we get the following inequality:
Figure BDA0002251500740000104
now, the present invention uses a differential method at t e { t ∈ { t }0,t0+1,...,t0The + T-1 interval adds the above equation to yield the following difference equation:
Figure BDA0002251500740000105
the present invention assumes
Figure BDA0002251500740000106
Is a constant, convert equation (13) to the following inequality:
Figure BDA0002251500740000107
because the expression on the left side of the inequality (14) must not be greater than
Figure BDA0002251500740000108
So by multiplying the virtual power queue by T
Figure BDA0002251500740000109
To this end we have demonstrated formula (11).
Now, the present invention has demonstrated that the upper bound on the total energy consumption of our proposed system model is not more than
Figure BDA00022515007400001010
This limit is very important for 5G mobile fog computing nodes with energy scarcity and it is proportional to several parameters: total service time of the radio interface, average allocated energyFor each interface) and a value of the maximum network queue backlog value. If the value of the above parameters is lower, the total energy consumption should be smaller and the 5G node battery supply may also be smaller. In this direction, the overall life of the battery will be longer.
In the above, we get the upper bound on the energy consumption per wireless network interface m per time period T as
Figure BDA00022515007400001011
The energy consumption of the entire fog node during the period T is
Figure BDA00022515007400001012
The total energy provided by the fog node battery is assumed to be EbatFrom this, it can be deduced that the battery life of the fog node theory is:
Figure BDA0002251500740000111
if we apply equation (14) to the total energy consumption of the wireless network interface m in equation (15), the total battery life time is determined using the following expression:
Figure BDA0002251500740000112
it may be noted that in the algorithm proposed by the present invention, the lower bound of the battery life of the 5G mobile mist computing node is determined by the right side of the expression (16), therefore, if the available energy of each RAT interface is higher, the battery of the 5G mobile mist computing node will have a longer life, and the battery life is inversely proportional to the following parameters: number of radio network interfaces (M), maximum backlog of queues, duration of bit period T and average power per radio interface
Figure BDA0002251500740000113
The value of (c). Notably, the wireless network interface is unused (suspended) in a particular timeslotShould not be included in the calculation, the 5G mobile fog computing node proposed by the present invention therefore has a longer battery life. (expression (16) is given assuming that all radio interfaces are enabled and all radio interfaces have the same average power draw
Figure BDA0002251500740000114
Derived on the basis of (1). But in the best case, only a few available wireless network interfaces, the algorithm of the present invention selects the most suitable wireless network interface, thus minimizing energy consumption. This is why the battery life time in the proposed 5G mobile node is longer.
It should be noted that the above-mentioned embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.
The above description is only an embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can understand that the modifications or substitutions within the technical scope of the present invention are included in the scope of the present invention, and therefore, the scope of the present invention should be subject to the protection scope of the claims.

Claims (1)

1. A terminal access method based on a 5G fog computing node is characterized by comprising the following steps:
step 1) constructing a system model of a fog wireless access node according to an information source, a fog wireless network interface and a processor queue;
step 2) constructing an objective function according to actual data of the information source reaching the fog wireless network interface:
step 2-1), defining a queue vector at the interface of the fog wireless network as Q (t) ═ Q1(t),Q2(t),...,QM(t)) at time t {1, 2.The queue update expression of is:
Qm(t+1)＝max(Qm(t)+Am(t)-um(t),0)
wherein the content of the first and second substances,
Figure FDA0003121276590000011
variable representing the arrival rate of the m-th queue, parameter wi,mWeight, u, indicating that the mth queue received information from the ith misty wireless network interfacem(t) is the output rate of service variable for the mth processor queue, M ∈ {1, 2.., M };
step 2-2) defining a time-averaged power vector for each processor queue as
Figure FDA0003121276590000012
Wherein
Figure FDA0003121276590000013
Represents the time-averaged power of the mth processor queue, the time-averaged arrival rate xiThe vector of (t) is defined as
Figure FDA0003121276590000014
Wherein
Figure FDA0003121276590000015
A time-averaged arrival rate representing the time-averaged arrival of the received information to the ith interface;
step 2-3) defining a throughput function, i.e. an objective function, as
Figure FDA0003121276590000016
For each fog node, applying a random utility maximization framework to the flow-based fog computing network model, resulting in the following optimization objective functions:
maximization:
Figure FDA0003121276590000017
constraint conditions are as follows: aboutBundle 1.
Figure FDA0003121276590000018
Constraint 2.
Figure FDA0003121276590000019
And (3) constraining.
Figure FDA0003121276590000021
Constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure FDA0003121276590000028
Constraint 2 indicates that the processing efficiency of all queues is stable; constraint 3 indicates that the time-averaged power of the mth queue is not greater than the necessary time-averaged power consumption that the system can provide, where pm(t) represents the power generated by the mth processor queue at time t,
Figure FDA0003121276590000022
represents the upper power bound of the mth queue; while
Figure FDA0003121276590000023
Is the necessary time-averaged power consumption
Step 3) constructing a virtual queue according to the system power, and constructing a second-order Lyapunov equation according to the actual queue and the virtual queue, wherein the method specifically comprises the following steps:
step 3-1) defines the virtual queue of the system as:
Figure FDA0003121276590000024
wherein Zm(t) is the finite queue length;
step 3-2) further defines the combined queue vector as s (t) ═ q (t), z (t) ], and may obtain a second order lyapunov equation:
Figure FDA0003121276590000025
step 4) selecting a proper penalty factor and a control strategy optimization objective function, which specifically comprises the following steps:
step 4-1), defining the lyapunov drift one-step transfer condition as Δ (S (t)) -E { L (S (t +1)) -L (S (t))) | S (t)) }, and obtaining a lyapunov drift penalty expression:
Δ(S(t))+V·E{u(αL(t),t)|S(t)}
in the above equation, the "penalty" vector process is defined as
Figure FDA0003121276590000026
Wherein
Figure FDA0003121276590000027
Representing the actual service rate of the mth queue; v is a control parameter;
step 4-2) matching with a penalty term V.E { u (alpha)L(t, t) | S (t) } optimization yields the following upper bound:
Figure FDA0003121276590000031
wherein B is a group of groups represented bym(t),um(t) and
Figure FDA0003121276590000032
the finite constants involved are expressed as:
Figure FDA0003121276590000033
furthermore, the object of the invention is: by observing the real and virtual queue vectors Q (t), Z (t) and the current state ψ (t), at eachSelecting the most appropriate control strategy action alpha at time tL(t)∈Aψ(t) to minimize the right side of the inequality; in this way, the optimization problem is decoupled and simplified to a separate algorithm;
step 4-3) observing newly arriving x for each queue M e {1, 2.. multidot.M } at each time tm(t), actual queue Qm(t) and virtual queue Zm(t) and selecting an appropriate control strategy αL(t) to minimize:
and (3) minimizing:
Figure FDA0003121276590000034
constraint conditions are as follows: constraint 1.
Figure FDA0003121276590000035
Constraint 2.
Figure FDA0003121276590000036
Constraint 1 indicates that the time-averaged arrival rate of the received information at the mth queue is not greater than the maximum output service rate that can be provided by the mth queue
Figure FDA0003121276590000037
Step 5) selecting information source access fog wireless network by using the optimized objective function; by optimizing the objective function, the system dynamically selects the most appropriate access scheme according to the input data volume of each interface at the current moment, the current cached data volume of each cache queue and the selected penalty factor, so that the current moment throughput of the system is maximized, and further, the total throughput of the system is maximized.
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Publication number Priority date Publication date Assignee Title
CN111954236B (en) * 2020-07-27 2021-11-09 河海大学 Hierarchical edge calculation unloading method based on priority
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107708214A (en) * 2017-09-30 2018-02-16 重庆邮电大学 Network utility based on Liapunov maximizes virtual resource allocation method
CN107708135A (en) * 2017-07-21 2018-02-16 上海交通大学 A kind of resource allocation methods for being applied to mobile edge calculations scene
US9986509B1 (en) * 2017-04-21 2018-05-29 Cisco Technology, Inc. Advance remote failure notification (ARFN)
CN108183733A (en) * 2018-01-03 2018-06-19 中山大学 The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory
CN109343904A (en) * 2018-09-28 2019-02-15 燕山大学 A kind of mist calculating dynamic offloading method based on Lyapunov optimization
CN109936472A (en) * 2017-12-18 2019-06-25 上海无线通信研究中心 The node computing capability calculated towards mist describes method, exchange method and its equipment

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10439890B2 (en) * 2016-10-19 2019-10-08 Tata Consultancy Services Limited Optimal deployment of fog computations in IoT environments

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9986509B1 (en) * 2017-04-21 2018-05-29 Cisco Technology, Inc. Advance remote failure notification (ARFN)
CN107708135A (en) * 2017-07-21 2018-02-16 上海交通大学 A kind of resource allocation methods for being applied to mobile edge calculations scene
CN107708214A (en) * 2017-09-30 2018-02-16 重庆邮电大学 Network utility based on Liapunov maximizes virtual resource allocation method
CN109936472A (en) * 2017-12-18 2019-06-25 上海无线通信研究中心 The node computing capability calculated towards mist describes method, exchange method and its equipment
CN108183733A (en) * 2018-01-03 2018-06-19 中山大学 The beam forming optimization method of online NOMA multiaerial systems based on Lyapunov's theory
CN109343904A (en) * 2018-09-28 2019-02-15 燕山大学 A kind of mist calculating dynamic offloading method based on Lyapunov optimization

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
5G移动通信系统的接入网络架构;项弘禹等;《电信科学》;20180820;第10-18页 *
Do we really need Cloud？Estimating the fog computing capacities in the city of Barcelona;Jordi Garcia等;《2018 IEEE/ACM International Conference on Utility and Cloud Computing Companion (UCC Companion)》;20190110;第290-295页 *
Joint optimization of transmission and processing delay in fog computing access networks;Yichao Chen等;《2017 9th International Conference on Advanced Infocomm Technology (ICAIT)》;20180621;第155-158页 *

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