CN108964817B - Heterogeneous network joint computing unloading and resource allocation method - Google Patents

Heterogeneous network joint computing unloading and resource allocation method Download PDF

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CN108964817B
CN108964817B CN201810949323.3A CN201810949323A CN108964817B CN 108964817 B CN108964817 B CN 108964817B CN 201810949323 A CN201810949323 A CN 201810949323A CN 108964817 B CN108964817 B CN 108964817B
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CN108964817A (en
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陈前斌
陈泓
赵冬梅
柴蓉
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Chongqing University of Post and Telecommunications
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/391—Modelling the propagation channel
    • H04B17/3912—Simulation models, e.g. distribution of spectral power density or received signal strength indicator [RSSI] for a given geographic region
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/382—Monitoring; Testing of propagation channels for resource allocation, admission control or handover
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services

Abstract

The invention relates to a heterogeneous network joint computation unloading and resource allocation method, and belongs to the technical field of wireless communication. The method comprises the following steps: s1: modeling user computing task characteristics; s2: modeling users to calculate unloading decision variables and limiting conditions thereof; s3: modeling a user to calculate local processing energy consumption and completion time of a task; s4: the modeling user calculates task unloading completion time; s5: modeling and calculating the energy consumption for completing task unloading by a user; s6: modeling the total energy consumption of the system; s7: modeling user computing task completion time and limiting conditions thereof; s8: and determining a calculation unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system. The method can reasonably utilize wireless resources under the condition of meeting the maximum time delay tolerance of the user calculation task, and realizes the minimization of the total energy consumption of the system by optimally designing calculation unloading and resource allocation strategies.

Description

Heterogeneous network joint computing unloading and resource allocation method
Technical Field
The invention belongs to the technical field of wireless communication, relates to the technical field of heterogeneous network resource allocation, and particularly relates to a heterogeneous network joint computation unloading and resource allocation method.
Background
With the high-speed development of computer and communication technologies, user service demands are becoming diversified, and next-generation communication networks are developing towards the trend of isomerization and gradually going towards interconnection. To meet the market demand, Radio Access Technologies (RATs) of various kinds have been developed, ranging from cellular networks to local area networks, public networks to professional networks, and single service networks to multimedia networks, and each has different features and service providing capabilities. Since a single wireless access technology cannot completely meet the service requirements of different users, a communication network in the future will realize efficient heterogeneous fusion of multiple RATs, and provide diversified network services for the users.
The mobile edge computing technology is considered as a considerable prospect mode in the next generation wireless network, and by moving cloud computing capability to the vicinity of the mobile equipment, a Radio Access Network (RANs) is endowed with strong computing capability, so that computing service can be provided for the mobile equipment at any time and any place. The mobile edge computing technique can provide low latency, high bandwidth, and computational flexibility in the computational offload process. As one of typical applications of mobile edge computing technology, micro cloud has been rapidly developed in recent years, and by deploying a micro cloud server at a RANs access point, mobile users in a network can be covered, and computing offload services can be provided for the users. Because the local execution of the user computing task needs higher computing expense, and the execution of the user task by the cloud side by adopting the computing unloading scheme relates to communication expense, how to divide the computing task and determine the task unloading scheme is realized to realize the efficient utilization of wireless network resources, and the optimization of system computing and communication expense is a current hot research topic.
In recent years, the problem of heterogeneous network joint computation offloading and resource allocation has been studied in literature. Documents t.t.nguyen and b.l.l.long.joint compensation and resource allocation in a closed based wireless communication network [ C ]. IEEE Global communication Conference, Dec 2017, pp.1-6, propose a joint computation offload and resource allocation optimization strategy for a two-layer multi-cell multi-user system, and achieve the goal of minimizing the maximum weighted energy consumption under the constraint condition of satisfying bandwidth, computation resources and tolerable delay. The documents j.zhang, w.xia, f.yan, and l.shell.joint calculation and allocation optimization in heterogeneous networks with mobile edge calculation [ J ]. IEEE Access, vol.6, pp.19324-19337,2018, for a mobile edge calculation system of a heterogeneous network, research on uplink subchannel allocation, uplink transmission power allocation and calculation resource scheduling problems of a mobile device, and propose a distributed optimization strategy for joint calculation offloading and resource allocation.
In the above researches, a resource allocation strategy with an optimal corresponding performance function is determined by modeling a specific network performance function based on an optimization theory, but the existing researches rarely comprehensively consider the problems of diversity, multiple access characteristics, network resource state difference and the like of a heterogeneous network, and the network comprehensive performance optimization is difficult to realize.
Disclosure of Invention
In view of this, an object of the present invention is to provide a method for joint computation offloading and resource allocation in a heterogeneous network, where for a heterogeneous network scenario including 1 macro Base Station and multiple small Base stations, it is assumed that all Base Stations (BS) deploy a certain number of micro cloud servers, factors such as user computation task characteristics, user terminal and server processing capabilities are comprehensively considered, total energy consumption of a modeling system is an optimization target, and a joint computation offloading and resource allocation optimization strategy is implemented.
In order to achieve the purpose, the invention provides the following technical scheme:
a heterogeneous network joint computing unloading and resource allocation method specifically comprises the following steps:
s1: modeling user computing task characteristics;
s2: modeling users to calculate unloading decision variables and limiting conditions thereof;
s3: modeling a user to calculate local processing energy consumption and completion time of a task;
s4: the modeling user calculates task unloading completion time;
s5: modeling and calculating the energy consumption for completing task unloading by a user;
s6: modeling the total energy consumption of the system;
s7: modeling user computing task completion time and limiting conditions thereof;
s8: and determining a calculation unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system.
Further, the step S1 specifically includes: suppose each user generates a computing task at a time that may need to be offloaded to completion, let dmRepresents the size of input data, which may include program code, input files, etc., needed to perform the computing task for user m; let cmRepresenting the computational load of the computational task of user m; order to
Figure BDA0001771081400000021
And M is more than or equal to 1 and less than or equal to M, wherein M is the total number of users in the network.
Further, the step S2 specifically includes:
assuming that all base stations deploy a certain number of micro cloud servers, let the BSnDenotes the nth base station, δm,nE {0,1} represents whether the computation task of user m is offloaded to BSnThe offload decision variable, δ, of the cloudlet completion computation m,n1 indicates that the computational tasks of user m are offloaded to the BSnThe micro cloud server completes the calculation, otherwise, deltam,n＝0；
Assuming that the computing task of each user can be only offloaded to the micro cloud server of one BS at most to complete the computation, deltam,nShould satisfy
Figure BDA0001771081400000022
Order SnIs BSnThe number of micro cloud servers on the BS is unloaded to the BS at the same timenCannot exceed its total number of micro cloud servers, i.e. deltam,nShould satisfy
Figure BDA0001771081400000031
N is more than or equal to 1 and less than or equal to N, wherein N is the total number of the BSs in the network.
Further, the step S3 specifically includes: modeling user computing task local processing energy consumption
Figure BDA0001771081400000032
And completion time
Figure BDA0001771081400000033
According to the formula
Figure BDA0001771081400000034
The computing task of computing user m handles energy consumption locally, wherein,
Figure BDA0001771081400000035
representing the battery power consumed by user m to process the computational task locally,
Figure BDA0001771081400000036
indicating completion of user m's local processing of computing tasksTime; according to the formula
Figure BDA0001771081400000037
The completion time for user m to locally process the computing task is calculated, wherein,
Figure BDA0001771081400000038
is the local processing rate of user m.
Further, the step S4 specifically includes: modeling user computation task offload completion time
Figure BDA0001771081400000039
According to the formula
Figure BDA00017710814000000310
Calculating a user calculated task offload completion time, wherein,
Figure BDA00017710814000000311
indicating that user m offloads computing tasks to BSnThe wireless link transmission time of the micro cloud server,
Figure BDA00017710814000000312
representing the computational tasks of user m at the BSnComputing time of the micro cloud server.
Further, the modeling user m offloads the computation task to the BSnWireless link transmission time of micro cloud server
Figure BDA00017710814000000313
Comprises the following steps:
Figure BDA00017710814000000314
wherein R ism,nIndicating access to a BSnThe data transmission rate of user m; according to the formula
Figure BDA00017710814000000315
Calculating access BSnWherein W represents the subchannel bandwidth, Pm,nIndicating access to a BSnBy usingTransmission power of household m, gm,nAnd sigma2Respectively representing users m and BSnChannel gain and noise power in between;
modeling computational tasks of user m at BSnComputing time of micro cloud server
Figure BDA00017710814000000316
Comprises the following steps:
Figure BDA00017710814000000317
wherein f isnRepresents BSnThe computing power of the micro cloud server.
Further, the step S5 specifically includes: modeling user computing task offload completion energy consumption
Figure BDA00017710814000000318
According to the formula
Figure BDA00017710814000000319
Calculating user computational task offload completion energy consumption, wherein,
Figure BDA00017710814000000320
indicating that user m offloads computing tasks to BSnThe transmission energy consumption of the micro cloud server is reduced,
Figure BDA00017710814000000321
representing the computational tasks of user m at the BSnThe computing energy consumption of the micro cloud server; according to the formula
Figure BDA00017710814000000322
Compute user m offloads compute tasks to BSnThe transmission energy consumption of the micro cloud server is reduced; according to the formula
Figure BDA00017710814000000323
The calculation task of calculating user m is at BSnThe computing power consumption of the micro cloud server of (1), wherein,
Figure BDA00017710814000000324
represents BSnThe data computation power consumption of the micro cloud server.
Further, the step S6 specifically includes: the total energy consumption E of the modeling system, which is the energy required to complete all the user calculation tasks in the network, i.e. the total energy consumption of the system
Figure BDA0001771081400000041
Further, the step S7 specifically includes: according to the formula
Figure BDA0001771081400000042
Calculating the completion time of the calculation task of the user m by meeting the requirement
Figure BDA0001771081400000043
Further, the step S8 specifically includes: comprehensively considering the user computing task characteristics and the computing unloading limiting conditions, determining a computing unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system, and recording
Figure BDA0001771081400000044
The invention has the beneficial effects that: the method can reasonably utilize wireless resources under the condition of meeting the maximum time delay tolerance of the user calculation task, and realizes the minimization of the total energy consumption of the system by optimally designing calculation unloading and resource allocation strategies.
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In order to make the object, technical scheme and beneficial effect of the invention more clear, the invention provides the following drawings for explanation:
FIG. 1 is a schematic diagram of a heterogeneous network scenario with a micro cloud server deployed;
FIG. 2 is a schematic flow chart of the method of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a heterogeneous network scenario in which a micro cloud server is deployed, where multiple small base stations in the heterogeneous network are located in a network coverage area of a macro base station, multiple users exist in the heterogeneous network, and the users are located in network coverage areas of multiple BSs, and according to computing task characteristics of the users, the micro cloud server offloaded to an appropriate BS may be selected to process a computing task; the total energy consumption of the modeling system is the energy required by all user calculation tasks in the network, and the optimal strategy of joint calculation unloading and resource allocation is realized on the basis of the minimum total energy consumption of the system.
Fig. 2 is a schematic flow chart of the method of the present invention, and as shown in fig. 2, the method for joint computation offload and resource allocation of heterogeneous networks specifically includes the following steps:
1) modeling user computing task characteristics:
assuming that each user generates a computing task which may need to be unloaded and completed at a certain time, the computing task characteristics of the users are modeled, specifically: let dmRepresents the size of input data, which may include program code, input files, etc., needed to perform the computing task for user m; let cmRepresenting the computational load of the computational task of user m; order to
Figure BDA0001771081400000045
And M is more than or equal to 1 and less than or equal to M, wherein M is the total number of users in the network.
2) Constructing user calculation unloading decision variables, and modeling a limiting condition:
assuming that all base stations deploy a certain number of micro cloud servers, constructing a user computing unloading decision variable, specifically: let BSnDenotes the nth base station, δm,nE {0,1} represents whether the computation task of user m is offloaded to BSnThe offload decision variable, δ, of the cloudlet completion computation m,n1 indicates that the computational tasks of user m are offloaded to the BSnThe micro cloud server completes the calculation, otherwise, deltam,n0; assuming that the computing task of each user can be only offloaded to the micro cloud server of one BS at most to complete the computation, deltam,nShould satisfy
Figure BDA0001771081400000051
Order SnIs BSnThe number of micro cloud servers on the BS is unloaded to the BS at the same timenCannot exceed its total number of micro cloud servers, i.e. deltam,nShould satisfy
Figure BDA0001771081400000052
N is more than or equal to 1 and less than or equal to N, wherein N is the total number of the BSs in the network.
3) Modeling user computing task local processing energy consumption and completion time:
modeling user computing task local processing energy consumption
Figure BDA0001771081400000053
And completion time
Figure BDA0001771081400000054
According to the formula
Figure BDA0001771081400000055
The computing task of computing user m handles energy consumption locally, wherein,
Figure BDA0001771081400000056
representing the battery power consumed by user m to process the computational task locally,
Figure BDA0001771081400000057
representing the completion time of the local processing calculation task of the user m; according to the formula
Figure BDA0001771081400000058
The completion time for user m to locally process the computing task is calculated, wherein,
Figure BDA0001771081400000059
is the local processing rate of user m.
4) The modeling user calculates the task unloading completion time:
modeling user computation task offload completion time
Figure BDA00017710814000000510
According to the formula
Figure BDA00017710814000000511
Calculating a user calculated task offload completion time, wherein,
Figure BDA00017710814000000512
indicating that user m offloads computing tasks to BSnThe wireless link transmission time of the micro cloud server,
Figure BDA00017710814000000513
representing the computational tasks of user m at the BSnComputing time of the micro cloud server. Further, the modeling user m offloads the computation task to the BSnWireless link transmission time of micro cloud server
Figure BDA00017710814000000514
Comprises the following steps:
Figure BDA00017710814000000515
wherein R ism,nFor accessing BSnThe data transmission rate of user m; according to the formula
Figure BDA00017710814000000516
Calculating access BSnWhere W is the subchannel bandwidth, Pm,nFor accessing BSnOf user m, gm,nAnd sigma2Respectively representing users m and BSnChannel gain and noise power in between; modeling computational tasks of user m at BSnComputing time of micro cloud server
Figure BDA00017710814000000517
Comprises the following steps:
Figure BDA00017710814000000518
wherein f isnIs BSnThe computing power of the micro cloud server.
5) Modeling user computing task unloading completion energy consumption:
modeling user computing task offload completion energy consumption
Figure BDA00017710814000000519
According to the formula
Figure BDA00017710814000000520
Calculating user computational task offload completion energy consumption, wherein,
Figure BDA00017710814000000521
indicating that user m offloads computing tasks to BSnThe transmission energy consumption of the micro cloud server is reduced,
Figure BDA00017710814000000522
representing the computational tasks of user m at the BSnThe computing energy consumption of the micro cloud server; according to the formula
Figure BDA00017710814000000523
Compute user m offloads compute tasks to BSnThe transmission energy consumption of the micro cloud server is reduced; according to the formula
Figure BDA0001771081400000061
The calculation task of calculating user m is at BSnThe computing power consumption of the micro cloud server of (1), wherein,
Figure BDA0001771081400000066
represents BSnThe data computation power consumption of the micro cloud server.
6) Modeling system total energy consumption:
the total energy consumption E of the modeling system is the energy required to complete all the user's computational tasks in the network, i.e. the energy required
Figure BDA0001771081400000062
7) The modeling user calculates the task completion time and the limiting conditions thereof:
the modeling user calculates task completion time and limiting conditions, and specifically comprises the following steps: according to the formula
Figure BDA0001771081400000063
Calculating the completion time of the calculation task of the user m by meeting the requirement
Figure BDA0001771081400000064
8) Determining a calculation unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system:
comprehensively considering the user computing task characteristics and the computing unloading limiting conditions, determining a computing unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system, and recording
Figure BDA0001771081400000065
Finally, it is noted that the above-mentioned preferred embodiments illustrate rather than limit the invention, and that, although the invention has been described in detail with reference to the above-mentioned preferred embodiments, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the scope of the invention as defined by the appended claims.

Claims (4)

1. A heterogeneous network joint computation unloading and resource allocation method is characterized by specifically comprising the following steps:
s1: modeling user computing task characteristics, specifically comprising: suppose each user generates a computing task at a time that may need to be offloaded to completion, let dmRepresents the size of input data required to perform the computing task for user m; let cmRepresenting the computational load of the computational task of user m; order to
Figure FDA0002712887750000011
Representing the maximum delay tolerance of the computing task of the user M, wherein M is more than or equal to 1 and less than or equal to M, wherein M is the total number of users in the network;
s2: modeling user computing offload blocksThe strategy variables and the limiting conditions thereof specifically comprise: assuming that all base stations deploy a certain number of micro cloud servers, let the BSnDenotes the nth base station, δm,nE {0,1} represents whether the computation task of user m is offloaded to BSnThe offload decision variable, δ, of the cloudlet completion computationm,n1 indicates that the computational tasks of user m are offloaded to the BSnThe micro cloud server completes the calculation, otherwise, deltam,n＝0；
Assuming that the computing task of each user can be only offloaded to the micro cloud server of one BS at most to complete the computation, deltam,nShould satisfy
Figure FDA0002712887750000012
Order SnIs BSnThe number of micro cloud servers on the BS is unloaded to the BS at the same timenCannot exceed its total number of micro cloud servers, i.e. deltam,nShould satisfy
Figure FDA0002712887750000013
Wherein N is the total number of BSs in the network;
s3: modeling user computing task local processing energy consumption
Figure FDA0002712887750000014
And completion time
Figure FDA0002712887750000015
According to the formula
Figure FDA0002712887750000016
The computing task of computing user m handles energy consumption locally, wherein,
Figure FDA0002712887750000017
representing the battery power consumed by user m to process the computational task locally,
Figure FDA0002712887750000018
representing user m processing computing tasks locallyCompletion time of (d); according to the formula
Figure FDA0002712887750000019
The completion time for user m to locally process the computing task is calculated, wherein,
Figure FDA00027128877500000110
the local processing rate for user m;
s4: modeling user computation task offload completion time
Figure FDA00027128877500000111
According to the formula
Figure FDA00027128877500000112
Calculating a user calculated task offload completion time, wherein,
Figure FDA00027128877500000113
indicating that user m offloads computing tasks to BSnThe wireless link transmission time of the micro cloud server,
Figure FDA00027128877500000114
indicating that user m offloads computing tasks to BSnThe wireless link transmission time of the micro cloud server is calculated according to the following formula:
Figure FDA00027128877500000115
wherein R ism,nIndicating access to a BSnThe data transmission rate of user m; according to the formula
Figure FDA00027128877500000116
Calculating access BSnWherein W represents the subchannel bandwidth, Pm,nIndicating access to a BSnOf user m, gm,nAnd sigma2Respectively representing users m and BSnChannel gain and noise power in between;
modelingUser m's computational tasks at BSnComputing time of micro cloud server
Figure FDA00027128877500000117
Comprises the following steps:
Figure FDA00027128877500000118
wherein f isnRepresents BSnThe computing power of the micro cloud server;
s5: modeling user computing task offload completion energy consumption
Figure FDA0002712887750000021
According to the formula
Figure FDA0002712887750000022
Calculating user computational task offload completion energy consumption, wherein,
Figure FDA0002712887750000023
indicating that user m offloads computing tasks to BSnThe transmission energy consumption of the micro cloud server is reduced,
Figure FDA0002712887750000024
representing the computational tasks of user m at the BSnThe computing energy consumption of the micro cloud server; according to the formula
Figure FDA0002712887750000025
Compute user m offloads compute tasks to BSnThe transmission energy consumption of the micro cloud server is reduced; according to the formula
Figure FDA0002712887750000026
The calculation task of calculating user m is at BSnThe computing power consumption of the micro cloud server of (1), wherein,
Figure FDA0002712887750000027
represents BSnThe data computing power consumption of the micro cloud server;
s6: modeling the total energy consumption of the system;
s7: modeling user computing task completion time and limiting conditions thereof;
s8: and determining a calculation unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system.
2. The method for joint computation offload and resource allocation for heterogeneous networks according to claim 1, wherein the step S6 specifically includes: the total energy consumption E of the modeling system, which is the energy required to complete all the user calculation tasks in the network, i.e. the total energy consumption of the system
Figure FDA0002712887750000028
3. The method for joint computation offload and resource allocation for heterogeneous networks according to claim 2, wherein the step S7 specifically includes: according to the formula
Figure FDA0002712887750000029
Calculating the completion time of the calculation task of the user m by meeting the requirement
Figure FDA00027128877500000210
4. The method for joint computation offload and resource allocation for heterogeneous networks according to claim 3, wherein the step S7 specifically includes: comprehensively considering the user computing task characteristics and the computing unloading limiting conditions, determining a computing unloading and resource allocation optimization strategy based on the minimization of the total energy consumption of the system, and recording
Figure FDA00027128877500000211
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