CN113347016A - Virtualization network function migration method based on resource occupation and time delay sensitivity - Google Patents

Virtualization network function migration method based on resource occupation and time delay sensitivity Download PDF

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CN113347016A
CN113347016A CN202110259716.3A CN202110259716A CN113347016A CN 113347016 A CN113347016 A CN 113347016A CN 202110259716 A CN202110259716 A CN 202110259716A CN 113347016 A CN113347016 A CN 113347016A
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CN113347016B (en
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张栋
李陈辉
陈翰泽
朱丹红
林为伟
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Fuzhou University
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Abstract

The invention relates to a virtualization network function migration method based on resource occupation and time delay sensitivity, which comprises the following steps of firstly, acquiring a resource occupation condition through a service monitoring model; secondly, calculating user weight by using a time delay perception model to obtain user division; and finally, improving the migration success rate by using a queuing alternative mechanism. The invention can effectively improve the migration success rate and the request average coverage rate.

Description

Virtualization network function migration method based on resource occupation and time delay sensitivity
Technical Field
The invention belongs to the technical field of mobile communication, and particularly relates to a virtualized network function migration method based on resource occupation and time delay sensitivity.
Background
Mobile Edge Computing (MEC) reduces resource requirements and user-perceived latency by transferring Computing, network, and storage capabilities from the core cloud to the Mobile Edge network nodes. MECs may also utilize a Network Function Virtualization (NFV) platform to help remove the limitations of traditional Network applications. With NFV technology, Virtual Network Functions (VNFs) can be created and placed in real-time to meet different application needs and to optimize management of network, computing and storage resources.
In the edge network, the resources of the MEC server are relatively limited, and the user terminal devices have different requirements on the delay sensitivity. When a certain MEC server receives a large number of requests in a certain time period, part of application services are interrupted due to too high time delay because differentiated services are not considered for applications with different time delay requirements. Therefore, in the face of user requirements and user limitations, how to effectively process the user requests which come later and cannot be processed due to insufficient MEC server resources becomes a problem to be solved urgently.
Disclosure of Invention
In view of this, the present invention provides a method for migrating a virtualized network function based on resource occupation and delay sensitivity, which effectively improves the success rate of migration and the average coverage rate of requests.
In order to achieve the purpose, the invention adopts the following technical scheme:
a virtualization network function migration method based on resource occupation and time delay sensitivity comprises the following steps:
step S1: calculating the current residual resource condition of the server of each edge node in the MEC, and adding the MEC server with insufficient resource residual into a preset waiting sequencing queue;
step S2: for each MEC server in the queue waiting for sorting, calculating the request delay of each user associated with the MEC server and the occupation condition of user resource requirements;
step S3: sequencing each user request associated with the MEC server according to an intelligent matching priority rule;
step S4: selecting an MEC server meeting preset time delay constraint and resource constraint conditions based on an improved particle swarm intelligent algorithm, sequentially calculating the weighted sum of node migration time delay and resource conditions as a fitness function, selecting a migration path with the shortest time delay, and migrating a corresponding user request to a target MEC server;
step S5: and determining the final user request distribution condition according to the obtained optimal solution.
Further, the resource constraint is a virtual network function instance
Figure BDA0002969333510000021
The required resource size does not exceed the resource capacity of the bottom layer edge network node corresponding to the mapping, and a certain VNF instance can only be deployed on a certain edge network node:
Figure BDA0002969333510000022
Figure BDA0002969333510000023
wherein A isikIndicating whether the kth VNF is on the ith MEC,
Figure BDA0002969333510000024
and
Figure BDA0002969333510000025
respectively representing the size of the memory resource and the size of the CPU resource which need to be placed in the kth VNF,
Figure BDA0002969333510000026
and
Figure BDA0002969333510000027
respectively showing the memory resource capacity and the CPU resource capacity of the ith MEC.
Further, the delay constraint is specifically that the delay of the migrated ue requesting to wait is required to be less than the sum of the link delay caused by the migration and the delay caused by the distance between the device and the physical edge node:
Figure BDA0002969333510000031
wherein,
Figure BDA0002969333510000032
representing the time delay between the links ij,
Figure BDA0002969333510000033
and
Figure BDA0002969333510000034
indicating the migration situation of the nth terminal device,
Figure BDA0002969333510000035
indicating that the link ij is enabled,
Figure BDA0002969333510000036
representing the time delay between the user request to the MEC.
Further, the intelligent matching priority rule specifically includes:
(1) when the residual resource capacity of the edge node is insufficient, partial user requests need to be disassociated;
(2) sequencing the user requests according to the set priority formula;
(3) associating the user request with the appropriate edge node;
(4) and outputting the migration condition requested by the user.
Further, the priority formula represents the product of the delay required by the ue request and the resource occupation resulting from the request:
Figure BDA0002969333510000037
further, the step S4 is specifically:
step S41: selecting an MEC server meeting constraint conditions such as time delay and resources, sequentially calculating the weighted sum of the node migration time delay and the resource condition as a fitness function, and taking the fitness function as a global optimal particle;
step S42: selecting the nearest node of the server nodes which are distant from the iteration round as a new particle;
step S43: calculating the weighted sum of the node migration delay and the resource condition, and comparing whether the fitness function value of the node migration delay and the resource condition is higher than that of the global optimal particle, if so, updating the optimal particle, otherwise, not modifying;
step S44: and repeating the actions of S41-S42 until the globally optimal particle converges, namely, the globally optimal particle is not changed by more than 8 continuous particle updates, namely, the final migration target node is obtained.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention reduces the local node load, relieves the bandwidth transmission and effectively reduces the cost of operators;
2. the invention effectively deals with the service interruption of the user caused by the lack of local resources, and simultaneously reduces the time delay expense;
3. the invention effectively improves the migration success rate and the request average coverage rate.
Drawings
FIG. 1 is a schematic illustration of migration in one embodiment of the present invention;
FIG. 2 is a schematic diagram of a user requesting a pre-migration and a post-migration in one embodiment of the invention;
FIG. 3 is a flow chart of the method of the present invention;
fig. 4 is a flow chart of an improved intelligent algorithm for particle swarm in accordance with an embodiment of the invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
Referring to fig. 1, a migration diagram specifically includes:
an MEC server: in a mobile edge network, there are many base stations, which constitute network nodes, and these network nodes all have network function virtualization infrastructure solutions to support VNF placement, and these servers all have certain physical resources to provide services for VNF normal operation.
The terminal equipment: namely, the user plane may specifically include a smart phone, a tablet, a notebook computer, etc., and has various service requests, a maximum delay requirement, and a service satisfaction degree.
Fig. 1 shows a request migration diagram under the MEC. When the MEC server is served with too many requests and the resources are insufficient, some of the requests need to be redirected to other MEC servers, as shown in the figure, request a on MEC server 1 will be distributed to MEC server 2.
In this embodiment, referring to fig. 2, the total resource of each MEC in the figure is 50, where the MEC1 is processing 4 requests from user terminal devices (e.g. camera, mobile computer), and occupying 50 resources, the available resources are left 0. When another user terminal device (such as a camera) requests the MEC1 server to serve the user terminal device, the MEC1 resource shortage will cause the service to be unavailable, and at this time, a part of the user device request which is not sensitive to the delay is required to be migrated to the adjacent MEC server for processing.
Referring to fig. 3, the present embodiment provides a flowchart of a method for migration and placement of a virtualized network function based on mobile edge computing, where the method first obtains resource occupation conditions through a service monitoring model on the premise of satisfying user request constraints and underlying network resource constraints; secondly, calculating user weight by using a time delay perception model to obtain user division; and finally, improving the migration success rate by using a queuing alternative mechanism.
The method comprises the following steps:
step S1: initializing underlying network resources, user requests and associated states;
preferably, in this embodiment, in Mobile Edge Computing (MEC), each MEC node is assumed to have a certain resource capacity
Figure BDA0002969333510000051
Each link has a certain time delay
Figure BDA0002969333510000061
The delay between a user request to the MEC is expressed as
Figure BDA0002969333510000062
Each VNF will occupy a portion of the resources of the MEC server;
server M for each edge node { M ═ M1，m2，...miCalculating the current residual resource condition of the server; adding the MEC server with insufficient resource residue into a preset waiting sequencing queue;
step S2: randomly increasing the number of user requests of a certain MEC node;
for each MEC server in the waiting ordering queue, calculating each user U ═ { U } associated with it1，U2，...，unSize of request delay
Figure BDA0002969333510000063
And user resource demand occupancy
Figure BDA0002969333510000064
Step S3: sequencing each user request associated with the MEC server according to an intelligent matching priority rule;
in this embodiment, preferably, the intelligent matching priority rule method is as follows:
step S31: when the residual resource capacity of the edge node is insufficient, partial user requests need to be disassociated;
step S32: sequencing the user requests according to the set priority formula;
the priority formula represents the product of the delay required by a user equipment request and the resource occupation resulting from the request:
Figure BDA0002969333510000065
the above equation indicates that when the product of the two is larger, the higher the priority of the request, the more likely it is to be selected for migration.
Step S33: associating the user request with the appropriate edge node;
step S34: outputting the migration condition requested by the user;
step S4: selecting an MEC server meeting constraint conditions such as time delay and resources based on an improved particle swarm intelligent algorithm, sequentially calculating the weighted sum of node migration time delay and resource conditions as a fitness function, selecting a migration path with the shortest time delay, and migrating a corresponding user request to a target MEC server;
in this embodiment, preferably, the preset resource constraint requirement is a virtual network function instance
Figure BDA0002969333510000066
The required resource size does not exceed the resource capacity of the bottom layer edge network node corresponding to the mapping, and a certain VNF instance can only be deployed on a certain edge network node:
Figure BDA0002969333510000071
Figure BDA0002969333510000072
in the above formula AikIndicating whether the kth VNF is on the ith MEC,
Figure BDA0002969333510000073
and
Figure BDA0002969333510000074
respectively representing the size of the memory resource and the size of the CPU resource which need to be placed in the kth VNF,
Figure BDA0002969333510000075
and
Figure BDA0002969333510000076
respectively representing the memory resource capacity and the CPU resource capacity of the ith MEC;
in this embodiment, it is preferable that the delay constraint requires that the delay of the request waiting of the migrated ue be less than the sum of the link delay caused by the migration and the delay caused by the distance between the ue and the physical edge node:
Figure BDA0002969333510000077
in the above formula
Figure BDA0002969333510000078
Representing the time delay between the links ij,
Figure BDA0002969333510000079
and
Figure BDA00029693335100000710
indicating the migration situation of the nth terminal device,
Figure BDA00029693335100000711
indicating that the link ij is enabled,
Figure BDA00029693335100000712
represents the time delay between the user request to the MEC;
step S5: judging whether the migration queue is empty or not, if so, indicating that no user request needing to be redirected exists, and if not, migrating the user request to a target MEC server;
step S6: and judging whether the current migration operation fails, if the migration operation succeeds, returning to further judge the resource condition of the server, and if the migration operation fails, adding an alternative request for migration. Determining the final user request distribution condition according to the obtained optimal solution;
fig. 4 is a schematic flow chart of an improved intelligent algorithm for a particle swarm in accordance with the present invention, where the steps of the improved intelligent algorithm for a migration server node are as follows:
step S41: selecting an MEC server meeting constraint conditions such as time delay and resources, sequentially calculating the weighted sum of the node migration time delay and the resource condition as a fitness function, and taking the fitness function as a global optimal particle;
step S42: selecting the nearest node of the server nodes which are distant from the iteration round as a new particle;
step S43: calculating the weighted sum of the node migration delay and the resource condition, and comparing whether the fitness function value of the node migration delay and the resource condition is higher than that of the global optimal particle, if so, updating the optimal particle, otherwise, not modifying;
step S44: repeating the actions of S41-S42 until the global optimal particle converges, namely, the global optimal particle is not changed by more than 8 times of continuous particle updates, namely, the final migration target node is obtained;
as will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.

Claims (6)

1. A virtualization network function migration method based on resource occupation and time delay sensitivity is characterized by comprising the following steps:
step S1, calculating the current residual resource condition of the server of each edge node in the MEC, and adding the MEC server with insufficient resource residual into a preset waiting sorting queue;
step S2, calculating the request delay size and the user resource demand occupation condition of each user associated with each MEC server in the queue waiting for sorting;
step S3, sequencing each user request associated with the MEC server according to the intelligent matching priority rule;
step S4, selecting an MEC server meeting preset time delay constraint and resource constraint conditions based on an improved particle swarm intelligent algorithm, sequentially calculating the weighted sum of node migration time delay and resource conditions as a fitness function, selecting a migration path with the shortest time delay, and migrating a corresponding user request to a target MEC server;
and step S5, determining the final user request distribution situation according to the obtained optimal solution.
2. The method of claim 1, wherein the resource constraint is a virtual network function instance
Figure RE-FDA0003178913180000011
The required resource size does not exceed the resource capacity of the bottom layer edge network node corresponding to the mapping, and a certain VNF instance can only be deployed on a certain edge network node:
Figure RE-FDA0003178913180000012
Figure RE-FDA0003178913180000013
wherein A isikIndicating whether the kth VNF is on the ith MEC,
Figure RE-FDA0003178913180000014
and
Figure RE-FDA0003178913180000015
respectively representing the size of the memory resource and the size of the CPU resource which need to be placed in the kth VNF,
Figure RE-FDA0003178913180000016
and
Figure RE-FDA0003178913180000017
respectively showing the memory resource capacity and the CPU resource capacity of the ith MEC.
3. The method according to claim 1, wherein the delay constraint is that the delay requirement for the migrated ue to wait is less than the sum of the link delay caused by the migration and the delay caused by the distance between the device and the physical edge node:
Figure RE-FDA0003178913180000021
wherein,
Figure RE-FDA0003178913180000022
representing the time delay between the links ij,
Figure RE-FDA0003178913180000023
and
Figure RE-FDA0003178913180000024
indicating the migration situation of the nth terminal device,
Figure RE-FDA0003178913180000025
indicating that the link ij is enabled,
Figure RE-FDA0003178913180000026
representing the time delay between the user request to the MEC.
4. The method for migrating the virtualized network function based on resource occupation and delay sensitivity according to claim 1, wherein the intelligent matching priority rule specifically comprises:
(1) when the residual resource capacity of the edge node is insufficient, partial user requests need to be disassociated;
(2) sequencing the user requests according to the set priority formula;
(3) associating the user request with the appropriate edge node;
(4) and outputting the migration condition requested by the user.
5. The method of claim 1, wherein the priority formula represents a product of a delay required by a user equipment request and a resource occupancy generated by the request:
Figure RE-FDA0003178913180000027
6. the method for migrating a virtualized network function based on resource occupation and delay sensitivity according to claim 1, wherein the step S4 specifically includes:
step S41: selecting an MEC server meeting constraint conditions such as time delay and resources, sequentially calculating the weighted sum of the node migration time delay and the resource condition as a fitness function, and taking the fitness function as a global optimal particle;
step S42: selecting the nearest node of the server nodes which are distant from the iteration round as a new particle;
step S43: calculating the weighted sum of the node migration delay and the resource condition, and comparing whether the fitness function value of the node migration delay and the resource condition is higher than that of the global optimal particle, if so, updating the optimal particle, otherwise, not modifying;
step S44: and repeating the actions of S41-S42 until the globally optimal particle converges, namely, the globally optimal particle is not changed by more than 8 continuous particle updates, namely, the final migration target node is obtained.
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