CN114173379B - A multi-user computing offloading method based on 5G private network splitter - Google Patents
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Abstract
The invention belongs to the field of wireless communication, and particularly relates to a multi-user computing and unloading method based on a 5G private network shunt; the method comprises the following steps: constructing a multi-user computing unloading frame based on a 5G private network shunt; constructing an objective function of the combined unloading total time delay and the resource allocation balance degree according to the multi-user computing unloading framework; solving an objective function of the combined unloading total time delay and the resource allocation balance degree to obtain an objective function optimal solution; determining an unloading decision by a user according to the optimal solution of the objective function; the user performs task unloading according to the determined unloading decision; the invention has good expansibility and safety, effectively utilizes the bandwidth and throughput of each UE device, enhances the network data processing capability, and improves the flexibility and usability of the network; the method can effectively reduce the average unloading time delay and the average transmission time delay of multiple users, and balance the workload of each mobile edge terminal device or each edge server, thereby having good economic benefit.
Description
Technical Field
The invention belongs to the field of wireless communication, and particularly relates to a multi-user computing and unloading method based on a 5G private network shunt.
Background
In recent years, mobile user devices (mobile phones, tablet computers, etc.) are becoming an important tool for learning, entertainment, social networking and business development, with a rapid increase in mobile data traffic, and this increase is expected to continue in the future. As mobile data traffic increases rapidly, the amount of computation and data exchange between mobile user equipment and the cloud is also increasing dramatically. To address this situation, mobile edge computing (Mobile Edge Computing, MEC) technology has been introduced in the industry to supplement and enrich cloud computing. Because the mobile edge computing technology directly deploys a mobile edge computing server (Mobile Edge Computing Server, MECS) on the base station, the mobile user equipment needing computing offloading does not have to select a remote cloud server, but can adopt a nearby policy, namely offloading computing load to the edge server nearby, or directly offloading the computing load to a terminal node with other idle resource capability in the local, so that the mobile user can accept a short-range cloud computing service in the coverage area of a wireless access network, and the user enjoys high-quality network experience, namely, the mobile edge computing extends computing resources and storage resources from a core wide area network to an edge network.
The problems encountered by mobile user equipment currently offloading local computing tasks to MECS are: the data transfer of the offloading process results in a non-negligible delay between the mobile device and the MECS, and it remains difficult to co-process how the multi-user different resource requirements are handled in case the multi-mobile user needs to compute the offloading. In the related art, most methods predict the processing time of each task on each candidate MECS using linear regression, while offloading the mobile user's computing tasks to the appropriate MECS based on the previously observed states of the MECS. However, when the number of mobile users needing to be calculated and offloaded is large and there are a plurality of MECS that can accept the offloaded service, how to solve the problems of multi-user total delay minimization and multi-MECS load balancing at the same time still lacks a practical solution at present.
Disclosure of Invention
In view of this, the present invention provides a multi-user computing and unloading method based on a 5G private network splitter, which includes: .
S1: constructing a multi-user computing unloading frame based on a 5G private network shunt; the 5G private network splitter comprises user plane function equipment UPF and a small mobile edge computing platform miniMEP;
s2: constructing an objective function of the combined unloading total time delay and the resource allocation balance degree according to the multi-user computing unloading framework;
s3: solving an objective function of the combined unloading total time delay and the resource allocation balance degree to obtain an objective function optimal solution; determining an unloading decision by a user according to the optimal solution of the objective function;
s4: and the user performs task unloading according to the determined unloading decision.
Preferably, constructing the multi-user computing offload framework based on the 5G private network splitter comprises constructing a computing offload total delay framework and constructing a computing resource allocation balance framework.
Further, constructing a computing offload total delay framework includes:
miniMEP is used as a mobile edge computing cooperative server, and the resource available state of each idle service node is recorded; the available states of the computing resources and the storage resources of the service node j are represented by a joint resource pair (p j,cj), wherein p j represents the computing resources of the service node, and c j represents the storage resources of the service node; defining an intensive computing task of the mobile user k as T k, and representing the requirement of the mobile user k on resources by using a joint resource pair (z k,ek), wherein the computing resource provided by a service node for the task T k is z k, and the storage resource provided by the service node for the task T k is e k;
every t time, mobile user k sends its own joint resource demand pair (z k,ek) to miniMEP;
miniMEP calculates the total time delay for offloading each mobile user based on the remaining computing resources of each service node, the remaining storage resources, and the combined resource demand pair for the mobile user (z k,ek).
Further, the total time delay formula of the unloading is calculated as follows:
Wherein f 1 (x, y) represents an objective function of total time delay of unloading, K represents the total number of users, M represents the total number of service nodes, d kj represents a computing resource allowance of the mobile edge computing collaboration server, z i represents a computing resource applied by a user, e i represents a storage resource applied by a user, J represents a service node set, and x kj represents an unloading judgment parameter.
Further, the computing resource allocation balance framework includes:
Defining a concentration degree parameter u for representing the occupation of the resources of each service node by the unloading decision, and defining a vector A= (a 1,a2,a3...aM) for representing the cumulative load of the resources of each five-Fu node; where a j represents the normalized cumulative workload of service node j;
and calculating the resource allocation balance degree according to the concentration degree parameter and the vector of the accumulated load of the resources of each service node.
Further, each service node resource cumulative load a j is defined as:
Where a j represents the normalized cumulative workload of service node j, K 1 represents the degree of importance for the computing resource usage, K 2 represents the degree of importance for the storage resource usage, K represents the total number of users, and x kj represents the offload decision parameter.
Further, the formula for calculating the balance degree of resource allocation is as follows:
Where f 2 (x, y) represents an objective function of the resource allocation balance, M represents the total number of service nodes, y j represents a busy decision parameter, a j represents an element in a vector of the cumulative load of the resources of each service node, The average value of the cumulative load of the resources of each service node is represented, and v represents the mean square error of the cumulative load of the resources of each service node.
Preferably, the objective function of the combined total time delay and the resource allocation balance degree is:
min[f(x,y)]＝min[w1f1(x,y)+w2f2(x,y)]
C1：0≤w1≤1,0≤w2≤1；
C2：w1+w2＝1
C3：
C4：
Wherein f 1 (x, y) represents an objective function of total time delay of unloading, f 2 (x, y) represents an objective function of balance degree of resource allocation, w 1 represents a weight value allocated to the total time delay of unloading, w 2 represents a weight value allocated to the resource by the balance degree, K represents a total number of users, M represents a total number of service nodes, z k represents a computing resource provided by the service node for task T k, x kj represents an unloading determination parameter, p j represents a computing resource of the service node, y j represents a busy determination parameter, e k represents a storage resource provided by the service node for task T k, and c j represents a storage resource of the service node.
Preferably, the process of solving the objective function of the combined unloading total delay and the resource allocation balance degree comprises the following steps: solving an objective function by adopting a weighted resource optimization algorithm, wherein the step of solving comprises the following steps:
S1: determining the weight w 1 of the objective function f 1 (x, y) and the weight w 2 of the function f 2 (x, y);
S2: preprocessing an objective function and inequality constraint conditions according to the weight w 1 and the weight w 2 to obtain a coefficient matrix x of each variable in the objective function;
s3: traversing elements in the coefficient matrix x, and taking the elements in the coefficient matrix as intcon values in sequence;
s4: initializing a coefficient matrix A of the inequality constraint condition and a constraint vector b of the inequality constraint condition; initializing a constraint vector beq of the equality constraint coefficient matrix Aeq and the equality constraint; the optimization interval lb of the variable x takes an all-zero vector, and ub takes an all-1 vector;
s5: and (3) according to the coefficient matrix A of the initialized inequality constraint condition, the constraint vector b of the initialized inequality constraint condition, the coefficient matrix Aeq of the initialized equality constraint condition, the constraint vectors beq of the initialized equality constraint condition and the values of intcon, invoking intlinprog () function to implement linear programming on the objective function f (x, y), wherein the finally obtained x is the optimal solution xopt.
Further, the formula for solving the objective function by adopting the weighted resource optimization algorithm is as follows:
[x,f(x,y)]＝intlinprog(f,intcon,A,b,Aeq,beq,lb,ub)
A×x≤b
Aeq×x＝beq
lb≤x≤ub
Wherein x represents a coefficient matrix of variables in the objective function, f (x, y) is the objective function, intlinprog () is a function for performing linear programming on the objective function, intcon represents a position of an integer decision variable in x, a represents a coefficient matrix of an inequality constraint condition, b represents a constraint vector of the inequality constraint condition, aeq represents an equality constraint condition coefficient matrix, beq represents an equality constraint condition constraint vector, lb represents a constraint interval lower limit of the variable x, and ub represents a constraint interval upper limit of the variable x.
The beneficial effects of the invention are as follows: the invention provides a multi-user computing and unloading method based on UPF equipment, mini MEP and other 5G private network shunts, which has good expansibility and safety, ensures high availability of industrial Internet equipment through a cluster formed by a plurality of edge terminal equipment with idle resources, effectively utilizes the bandwidth and throughput of each UE equipment through load balancing, strengthens network data processing capacity, and improves the flexibility and availability of the network; the method can effectively reduce the average unloading time delay and the average transmission time delay of multiple users, and balance the workload of each mobile edge terminal device or each edge server, thereby having good economic benefit.
Drawings
FIG. 1 is a block diagram of a system frame in accordance with the present invention;
FIG. 2 is a flow chart of a multi-user computing offload method incorporating UPFs and miniMEP in accordance with the present invention;
FIG. 3 is a flow chart of constructing a linear optimization objective function in the present invention;
FIG. 4 is a flowchart of the weighted resource optimization algorithm of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a multi-user computing and unloading method based on a 5G private network splitter, wherein the structure of a system framework of the invention is shown in figure 1, and mainly comprises an integral framework, a user plane function device UPF (User Plane Function), a small mobile edge computing platform miniMEP (mini Mobile Edge Platform) and the position of a local terminal device in a suitable 5G scene; the base station bound with miniUPF is used for acquiring the data information of the industrial Internet equipment transmitted by the CPE and the task information to be unloaded by the local equipment, and transmitting the data information to the mini UPF for data screening processing; the miniUPF equipment is used for receiving and acquiring data information transmitted by the base station, screening and judging, receiving the data packets conforming to the rules into the factory edge cloud, collecting the load condition of each CPE by the mini MEP, carrying out corresponding processing, and forwarding the data packets not conforming to the load condition to the core network, thereby reducing the time delay of accessing the local network which must pass through the core network. The mini MEP is used as an edge collaboration server for collaboration of mobile edge terminals and servers, gathers the offloading tasks and related information sent from the current user terminal equipment, judges which edge terminal equipment and servers in the local network have sufficient computing and storage resources, and judges which edge terminal equipment and service are best to offload to after the computation of an objective function, thereby solving the problem that the current terminal equipment has insufficient computing task capacity.
The overall flow for realizing user unloading according to UPF and miniMEP in the invention is shown in figure 2, in particular, as shown in figure 3, the multi-user computing unloading method based on the 5G private network splitter in the invention comprises the following steps:
s1: constructing a multi-user computing unloading frame based on a 5G private network shunt; the 5G private network splitter includes a UPF device and a small mobile edge computing platform miniMEP;
S2: constructing an objective function of the combined unloading total time delay and the resource allocation balance degree according to the multi-user computing unloading framework; the unloading total time delay comprises unloading time delay and transmission time delay;
s3: solving an objective function of the combined unloading total time delay and the resource allocation balance degree to obtain an objective function optimal solution; determining an unloading decision by a user according to the optimal solution of the objective function;
s4: and the user performs task unloading according to the determined unloading decision.
Constructing a multi-user computing and unloading framework based on a 5G private network shunt comprises constructing a computing and unloading total time delay framework and constructing a computing resource allocation balance degree framework; the calculation unloading framework based on the 5G private network splitter can enable user data to directly enter a local enterprise network through UPF (unified packet flow) without passing through a core network, so that transmission delay is reduced, and the safety of the whole unloading process is improved; constructing a computing offload aggregate latency framework includes: miniMEP is used as a mobile edge computing cooperative server, and the resource available state of each idle service node is recorded; the available states of the computing resources and the storage resources of the service node j are represented by a joint resource pair (p j,cj), wherein p j represents the computing resources of the service node, and c j represents the storage resources of the service node; defining an intensive computing task of the mobile user k as T k, and representing the requirement of the mobile user k on resources by using a joint resource pair (z k,ek), wherein the computing resource provided by a service node for the task T k is z k, and the storage resource provided by the service node for the task T k is e k; the service node comprises a mobile edge terminal and an edge server; every t time, at the beginning of each t time period, mobile user k sends its own joint resource demand pair (z k,ek) to miniMEP; miniMEP calculates the total offload delay for each mobile user based on the remaining computing resources of each serving node, the remaining storage resources, and the joint resource demand pair of the mobile user (z k,ek).
The computing resource margin d kj of the mobile edge computing collaboration server is:
Where z i denotes the computing resources applied by the user, e i denotes the storage resources applied by the user, and J denotes the set of service nodes.
In order to represent the service and served relationship between the mobile user and the mobile edge terminal and edge server, a matrix x=x i1×M is introduced to characterize the overall offloading decision, the matrix is in the row j column, the element X kj of the matrix is defined as an offloading decision parameter, and it is represented whether the mobile user K offloads the computing task to the service node j, where K e K, j e M. K is the total number of mobile users, M is the total number of service nodes, and the unloading judgment parameter x kj is assigned as follows:
It is assumed that a mobile terminal can only offload computing tasks to at most one edge terminal node or edge server, so that at most one element in each row of matrix X has a value of 1, and the remaining elements in this row are all 0. Further, a vector y=y i1×M is introduced to identify whether the service node j provides offload services for the mobile user, its element Y i (j e M) is defined as a busy decision parameter, which indicates whether the service node j provides offload services for the mobile user, and the busy decision parameter is assigned as:
Constructing a computing resource allocation balance framework includes:
to evaluate the load balance of the offloading decision, two parameters are defined: one is a concentration degree parameter u characterizing that the offloading scheme occupies a service node, and the other is a vector a= (a 1,a2,a3...aM) characterizing the cumulative load of edge terminal nodes or edge server resources. The element a j (j epsilon M) and the parameter u of A are normalized parameters, the numerical value is in the range of [0,1], and the definition of the concentration degree parameter u is as follows:
Element a j in vector a= (a 1,a2,a3...aM) of cumulative load of each service node resource is defined as:
Where a j (j∈m) represents the normalized cumulative workload of the service node j (comprehensively considering the computing resource and the storage resource usage load), the normalized resource cumulative loads a 1,a2,a3...aM of all M service nodes form a resource cumulative load vector a= (a 1,a2,a3...aM);k1 represents the importance of the computing resource usage, k 2 represents the importance of the storage resource usage, the weights k 1 and k 2 satisfy 0.ltoreq.k 1,k2.ltoreq.1, and k 1+k2 =1, and preferably, the computing resource and the storage resource usage of the edge terminal node or the edge server are the same, that is, k 1＝k2 =0.5.
The constructing of the objective function of the combined unloading total time delay and the resource allocation balance degree comprises the following steps:
Firstly, minimizing the total computing and unloading time delay of a mobile user, improving the mobile user experience, secondly balancing the resource use load on a service node, and avoiding the resource on the service node from being consumed too early by an unbalanced computing and unloading scheme, so that the optimization of computing and unloading decision is the optimization of two targets of the total computing and unloading time delay and the resource allocation balance degree, the optimization is converted into the linear optimization problem based on integers, and an objective function of the total computing and unloading time delay and the resource allocation balance degree is constructed:
min[f(x,y)]＝min[w1f1(x,y)+w2f2(x,y)]
C1：0≤w1≤1,0≤w2≤1；
C2：w1+w2＝1
C3：
C4：
Where f 1 (x, y) represents the objective function of the total offload delay, f 2 (x, y) represents the objective function of the resource allocation balance, w 1 represents the weight value allocated to the total offload delay, w 2 represents the weight value allocated to the resource utilization balance, The average value of the cumulative load of the resources of each service node is represented, and v represents the mean square error of the cumulative load of the resources of each service node.
The method for solving the objective function of the combined unloading total time delay and the resource allocation balance degree adopts a weighted resource optimization algorithm to solve the objective function, and comprises the following steps:
For a fully distributed computing offload scheme, f 2 (x, y) gets the smallest possible value of 0 when all edge terminals or edge servers have exactly the same proportion of resource usage load; as the difference of the usage load proportion of different edge terminals or edge servers becomes larger, and the distribution of the edge terminals or edge servers occupied by the unloading calculation tends to be concentrated, the value of f 2 (x, y) also increases, the asymptotically reaches 1, and the resource usage unbalance degree is highest at the moment. The constraint on the resource load of each edge terminal or edge server is simple, i.e. the sum of its resources allocated to the mobile users must not exceed its total resource upper limit. The functions f 1(x,y)、f2 (x, y) are aggregated together by using a linear weighting method to obtain an objective function f (x, y) for representing the whole optimization problem, and when f (x, y) obtains the minimum value, the total unloading time delay of the calculation unloading scheme and the resource allocation balance degree of the service node are indicated to be totally optimal; as shown in fig. 4, the step of solving includes:
S1: determining the weight w 1 of the objective function f 1 (x, y) and the weight w 2 of the function f 2 (x, y);
S2: preprocessing an objective function and inequality constraint conditions according to the weight w 1 and the weight w 2 to obtain a coefficient matrix x of each variable in the objective function;
s3: traversing elements in the coefficient matrix x, and taking the elements in the coefficient matrix as intcon values in sequence;
s4: initializing a coefficient matrix A of the inequality constraint condition and a constraint vector b of the inequality constraint condition; initializing a constraint vector beq of the equality constraint coefficient matrix Aeq and the equality constraint; the optimization interval lb of the variable x takes an all-zero vector, and ub takes an all-1 vector;
s5: and (3) according to the coefficient matrix A of the initialized inequality constraint condition, the constraint vector b of the initialized inequality constraint condition, the coefficient matrix Aeq of the initialized equality constraint condition, the constraint vectors beq of the initialized equality constraint condition and the values of intcon, invoking intlinprog () function to implement linear programming on the objective function f (x, y), wherein the finally obtained x is the optimal solution xopt.
The formula for solving the objective function by adopting the weighted resource optimization algorithm is as follows:
[x,f(x,y)]＝intlinprog(f,intcon,A,b,Aeq,beq,lb,ub)
A×x≤b
Aeq×x＝beq
lb≤x≤ub
Wherein x represents a coefficient matrix of variables in the objective function, f (x, y) is the objective function, intlinprog () is a function for performing linear programming on the objective function, intcon represents a position of an integer decision variable in x, a represents a coefficient matrix of an inequality constraint condition, b represents a constraint vector of the inequality constraint condition, aeq represents an equality constraint condition coefficient matrix, beq represents an equality constraint condition constraint vector, lb represents a constraint interval lower limit of the variable x, and ub represents a constraint interval upper limit of the variable x.
The optimal solution xopt is the optimal solution of the objective function combining the total unloading time delay and the resource allocation balance degree, and then the system guides each mobile terminal to implement unloading according to the unloading scheme corresponding to the xopt.
The invention provides a multi-user computing and unloading method based on UPF equipment, mini MEP and other 5G private network shunts, which has good expansibility and safety, ensures high availability of industrial Internet equipment through a cluster formed by a plurality of edge terminal equipment with idle resources, effectively utilizes the bandwidth and throughput of each UE equipment through load balancing, strengthens network data processing capacity, and improves the flexibility and availability of the network; the method can effectively reduce the average unloading time delay and the average transmission time delay of multiple users, and balance the workload of each mobile edge terminal device or each edge server, thereby having good economic benefit.
While the foregoing is directed to embodiments, aspects and advantages of the present invention, other and further details of the invention may be had by the foregoing description, it will be understood that the foregoing embodiments are merely exemplary of the invention, and that any changes, substitutions, alterations, etc. which may be made herein without departing from the spirit and principles of the invention.
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