CN111698703A - Network reliability optimization method based on service priority and load balance - Google Patents

Network reliability optimization method based on service priority and load balance Download PDF

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CN111698703A
CN111698703A CN202010450336.3A CN202010450336A CN111698703A CN 111698703 A CN111698703 A CN 111698703A CN 202010450336 A CN202010450336 A CN 202010450336A CN 111698703 A CN111698703 A CN 111698703A
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network
virtual network
virtual
physical
physical network
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CN111698703B (en
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胡文建
苏汉
张益辉
赵会峰
何利平
李霞
张颖
陈瑞华
郭家伟
李旭东
杨宇皓
徐良燕
孙静
陈方
赵灿
王琳
杨阳
郭思炎
王聪
孙莹晖
张郁
张伟
王代远
谷超
刘保安
吴涛
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State Grid Corp of China SGCC
Shijiazhuang Power Supply Co of State Grid Hebei Electric Power Co Ltd
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State Grid Corp of China SGCC
Shijiazhuang Power Supply Co of State Grid Hebei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W24/00—Supervisory, monitoring or testing arrangements
    • H04W24/02—Arrangements for optimising operational condition
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/50—Allocation or scheduling criteria for wireless resources
    • H04W72/56—Allocation or scheduling criteria for wireless resources based on priority criteria
    • H04W72/566—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient
    • H04W72/569—Allocation or scheduling criteria for wireless resources based on priority criteria of the information or information source or recipient of the traffic information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
    • 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 network reliability optimization method based on service priority and load balancing, which is applied to a virtualized wireless sensor network, and the step of updating the resource allocation mapping of the virtual network in the virtualized wireless sensor network comprises the following steps: marking a virtual network needing migration at a first moment, and calculating the load balancing rate of a physical network layer at the moment; and at the second moment, if the load balancing rate of the physical network layer is less than the load balancing rate at the first moment after the new resource allocation mapping of the virtual network needing migration is executed, executing the new resource allocation mapping of the virtual network. The invention optimizes the network reliability based on the analysis of the service priority and the load balancing theory, thereby improving the service quality of the virtualized wireless sensor network and prolonging the service time.

Description

Network reliability optimization method based on service priority and load balance
Technical Field
The invention relates to the field of resource management of wireless sensor networks, in particular to a virtualized network reliability optimization method based on service priority and load balancing.
Background
With the rapid development and application of the internet of things (IoT), Sensor Networks (WSNs), represented by Wireless Sensor Networks (WSNs), have been applied to various fields such as industry, medical treatment, traffic, and environment. The sensor network is a network formed by freely organizing and combining sensor nodes with computing, communication and sensing capabilities. Because the wireless sensor network is widely distributed, the power supply of the sensor nodes is generally provided by the power supply carried by the sensor nodes.
In order to maximize the resource utilization rate of the sensor node and ensure the normal operation of services on the sensing network, the resource allocation and reliability optimization of the wireless sensing network have become a research focus. Because the virtualization technology can effectively improve the utilization rate of network resources and the reliability of network services, a virtualization-based wireless virtualization Sensor network technology (virtualized wireless Sensor Networks) has been proposed and is applied in a wider range. In a virtualized wireless sensor network environment, an existing sensor network is divided into a physical network layer and a virtual network layer. The physical network nodes of the physical network layer comprise physical sensors and provide physical network resources for the virtual network of the virtual network layer. Each virtual network of the virtual network layer leases the resources of the physical network layer according to the service requirements, thereby realizing the operation service of various services on the virtual network. Regarding the resource allocation and network reliability optimization problem of the sensor network, the existing research can be divided into a traditional network environment and a network virtualization environment.
Under the traditional network environment, two strategies of route protocol optimization and fault quick recovery are mainly adopted. In the aspect of routing protocol optimization, a document [ NIXIU, Chenyang, routing protocol [ J ] for data transmission delay optimization in a wireless sensor network, computer application, 2019,40(1):196 plus 201 ] aims at solving the problem of reliable transmission of data packets in the wireless sensor network, and data transmission efficiency is analyzed based on a network detection technology, so that routing optimization is performed on a transmission link with a high packet loss rate. In the literature [ Wangjunhong, Chensheng, Chenmeiyuan ] WSNs real-time performance and reliability optimization research [ J ] in the intelligent power distribution network based on the fuzzy cognitive map, 2016,29(2):213-219.] aiming at the problem of high failure rate of the intelligent power grid, the reliability optimization of the wireless sensor network is realized by adjusting routing paths and network parameters. The document [ crown, royal yao, self-powered wireless sensor network routing algorithm [ J ] based on cluster head optimization, computer application, 2018,6(1721):1725 and 1736 ] aims at solving the problem of low data transmission success rate in the wireless sensor network, and optimizes a cluster head selection mechanism in a grouping algorithm, thereby ensuring the reliability of the sensor network in each group.
Under the network virtualization environment, two strategies of optimal resource allocation for improving the utilization rate and resource allocation considering survivability are mainly included. The optimized service scheduling sequence is solved by adopting an exhaustive search method in documents [ Delgado C, canals M, Ort I n J, et al. Joint application control and network sliding in virtual sensor networks [ J ]. IEEEInternet of reasons Journal,2017,5(1):28-43 ], so that the service quality of the sensor network is improved. Documents [ Soualah O, Aitsaadi N, Fajjari I.A novel reactive virtual Network based on gate the same [ J ]. IEEE Transactions on Network and Service Management,2017,14(3): 569) 585 ] are used for solving the problem of low efficiency in access control in the virtual sensor Network, and Network slicing and admission control technologies are combined, so that the flexibility of access control of the sensor Network is improved. In terms of a virtual resource mapping algorithm considering survivability, document [10] solves the problem of link failure in a link redundancy manner. The document [ Shahriar N, Chowdhury S R, Ahmed R, actual, virtual network virtualization through joint capacity allocation and allocation [ J ]. IEEE Journal on Selected Areas in Communications,2018,36(3): 502-.
There have been studies to optimize resources during the resource allocation process and in the event of a failure. When the resource is allocated, the reserved resource is adopted, which easily causes resource waste. And optimizing resources under the condition of failure, and influencing the service quality of the virtual network. In addition, due to the imbalance of resource allocation, part of the physical network nodes are easy to exhaust energy of the physical network nodes due to long-time service of the virtual network, and the physical network layer is unavailable. Therefore, it is necessary to perform balanced configuration on resources carrying virtual services on the physical network nodes, so as to ensure stable operation of the physical network layer.
Disclosure of Invention
The invention aims to provide an optimization method of a virtualized wireless sensor network, which is used for optimizing the network reliability based on the analysis of business priority and load balancing theory, thereby improving the service quality of the virtualized wireless sensor network and prolonging the service time.
The technical scheme provided by the invention is a network reliability optimization method based on service priority and load balancing, and the step of updating the resource allocation mapping of a virtual network in a virtualized wireless sensor network comprises the following steps: marking a virtual network needing migration at a first moment, and calculating the load balancing rate of a physical network layer at the moment; and at the second moment, if the load balancing rate of the physical network layer is less than the load balancing rate at the first moment after the new resource allocation mapping of the virtual network needing migration is executed, executing the new resource allocation mapping of the virtual network.
Preferably, the method for tagging a virtual network requiring migration includes: and at a moment, selecting the physical network nodes with the remaining life cycles smaller than a threshold value in the physical network layer, and marking the virtual network corresponding to each virtual service borne by the physical network nodes as the virtual network needing migration.
A further improvement is that, for a plurality of virtual networks needing migration, new resource allocation mapping is preferentially provided for the virtual network with the lowest delay requirement value.
A further improvement consists in attempting to perform migration upon obtaining a new resource allocation map for a virtual network that needs migration.
A further improvement is that for a virtual network that needs migration, the new resource allocation map is obtained by solving the shortest path at the physical network layer. Meanwhile, for the new resource allocation mapping, the time delay between the corresponding virtual network nodes can be judged according to the hop count of the path between the physical network nodes, so as to judge whether the new resource allocation mapping meets the time delay requirement condition of the virtual network.
A further improvement is that for a virtual network that needs migration, if the path hop count of its new resource allocation map is greater than the delay requirement value of the virtual network, its new resource allocation map is not executed.
In a further improvement, the step of updating the resource allocation map of the virtual network in the virtualized wireless sensor network comprises:
s100, at one moment, acquiring the condition that the remaining life cycle in the physical network layer of the virtualized wireless sensor network is less than a threshold valuePhysical network nodes and marking the virtual networks carried by the physical network nodes as virtual networks needing migration; obtaining the load balance coefficient of the physical network layer at the moment
Figure BDA0002507464800000031
S200, traversing all virtual networks needing migration, and distributing new resource distribution mapping obtained by a shortest path algorithm for the virtual networks from the virtual network with the lowest time delay requirement value; if the new resource allocation mapping meets the delay requirement of the virtual network and the load balancing requirement of the physical network layer, migrating the virtual network according to the new resource allocation mapping, otherwise, not migrating the virtual network;
and S300, repeatedly executing the steps S100 and S200, and counting as one-time optimization, wherein if the repeated times exceed a preset optimization time threshold value or no virtual network needing migration exists, the optimization process is ended.
A further refinement consists in that the step S100 comprises the following steps:
s101, for any physical network node ni∈ N, calculating its remaining life cycle
Figure BDA0002507464800000041
S102, remaining life cycle
Figure BDA0002507464800000042
Less than a life cycle threshold
Figure BDA0002507464800000043
Putting the set theta into the physical network nodes;
s103, putting the virtual network corresponding to the virtual service loaded on each physical network node in the set theta into a virtual network set omega, and marking the virtual network as a virtual network to be migrated;
s104, calculating the current load balancing coefficient of the physical network layer and assigning the current load balancing coefficient to the physical network layer
Figure BDA0002507464800000044
In a further refinement, the step S200 includes the following steps:
s201, traversing each virtual network in the set omega, and calculating the time delay requirement of each virtual network, namely for any virtual network in the set omega
Figure BDA0002507464800000045
Having a delay requirement Hx；
S202, arranging each virtual network in the set omega in an ascending order according to the value of the time delay requirement of each virtual network to obtain the set omegaord；
S203, selecting a set omegaordA virtual network in
Figure BDA0002507464800000046
The initialization loop variable x is 1, i.e. the first time this step is performed, the selection set Ω should be selectedordThe virtual network with the minimum value of the medium delay requirement increases x by one in sequence before executing the step each time;
s204, aiming at the current virtual network
Figure BDA0002507464800000047
The virtual service of (2) uses Dijkstra algorithm to solve the shortest path in order to obtain a new virtual network for bearing the current
Figure BDA0002507464800000048
And calculating the path hop count of the topology structure
Figure BDA0002507464800000049
S205, judging the current virtual network
Figure BDA00025074648000000410
Whether or not it is less than its delay requirement Hx. If so, executing step S203;
s206, calculating the virtual network layer of the physical network layerLoad balancing coefficient η under pseudo-net topology structurereloadDetermine whether or not to satisfy
Figure BDA00025074648000000411
If yes, jumping to step S208;
s207, if other paths exist in the Dijkstra algorithm solution in the step 204, selecting one of the paths with the shortest path as the current virtual network
Figure BDA00025074648000000412
Mapping the topological structure of (1), and returning to the step S205;
s208, migration is executed, and the selected virtual network is
Figure BDA00025074648000000413
And mapping to the path of the current topological structure, and returning to the step S203.
In a further refinement, the step S300 comprises the following steps:
s301, judging whether the optimization time threshold T is exceeded or not, and if the optimization time threshold T is exceeded, ending;
s302, for each physical node ni∈ N, evaluating whether its remaining life cycle is less than a threshold
Figure BDA00025074648000000414
If yes, ending; if not, step S102 is executed.
In order to realize load balance of wireless sensor Network resources and ensure that the life cycle of a physical node is as long as possible, thereby ensuring the service quality of a virtual Network, the invention provides a Network reliability optimization method (NROA-SPoLB) based on service priority and load balance. The algorithm comprises three steps of S100 selecting a virtual network needing to be migrated, S200 migrating the virtual network, and S300 evaluating whether the life cycles of all physical nodes meet a threshold value. In step S100, all virtual nodes of the physical node which do not satisfy the life cycle are selected by selecting the physical node which needs to be optimizedAnd migrating the service. This has the advantage of reserving more space for the physical network nodes and preventing their resources from being exhausted when remapping. In step S200, for the virtual service that needs to be migrated, the shorter the time delay is, the more the virtual service needs to be migrated first, so as to obtain a shorter link and meet the time delay requirement. In step S300, an optimization time threshold T is set to limit the execution times of the algorithm, so as to prevent excessive calculation, which results in excessive calculation resource overhead. Secondly, judge all physical nodes ni∈ N whether the lifecycle satisfies a threshold
Figure BDA0002507464800000051
And the excessive use of part of physical node resources caused by migration can be effectively prevented.
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The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
FIG. 1 is a flow chart of a network reliability optimization method based on service priority and load balancing, and
FIG. 2 is a schematic diagram of the effect of the number of virtual networks on the percentage of active nodes in an embodiment that includes a comparison method, an
FIG. 3 is a graphical illustration of the impact of physical network node size on effective node fraction in an embodiment involving a comparison method.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In the scheme of the invention, in order to solve the problem of low reliability of the wireless sensor equipment, the invention provides a network reliability optimization method based on service priority and load balancing.
In order to realize load balancing of wireless sensor Network resources and ensure that the life cycle of a physical node is as long as possible, thereby ensuring the service quality of a virtual Network, a Network reliability optimization method (NROA-SPoLB) based on service priority and load balancing, which is proposed by the present invention, is shown in fig. 1. The method includes steps S100 to S300:
s100, selecting a virtual network to be migrated;
s200, migrating the virtual networks in sequence;
and S300, if the virtual network needing migration exists, repeating S100 and S200, otherwise, ending.
In step S100, all virtual services of physical nodes that do not satisfy the lifecycle are migrated by selecting physical nodes that need to be optimized. This has the advantage of reserving more space for the physical network nodes and preventing their resources from being exhausted when remapping.
In step S200, for the virtual service that needs to be migrated, the shorter the time delay is, the more the virtual service needs to be migrated first, so as to obtain a shorter link and meet the time delay requirement.
In step S300, an optimization time threshold T is set to limit the execution times of the algorithm, so as to prevent excessive calculation, which results in excessive calculation resource overhead. Secondly, judge all physical nodes ni∈ N whether the lifecycle satisfies a threshold
Figure BDA0002507464800000061
And the excessive use of part of physical node resources caused by migration can be effectively prevented.
The method of each embodiment of the invention is based on a network virtualization environment, in the network virtualization environment, a wireless sensor network is divided into a physical network layer and a virtual network layer after being virtualized to form a virtualized wireless sensor network, and the virtual network layer simultaneously provides a plurality of virtual networks to provide services for a plurality of virtual services. Chinese patent CN110933728A discloses a mapping device for a virtualized wireless sensor network, wherein a control layer thereof is equivalent to a virtual network layer of the present invention, and a sensing infrastructure layer thereof is equivalent to a physical network layer of the present invention. The embodiment of the invention, which runs on a cloud computing Resource management platform, belongs to a configuration method of Resource Organizational Service (ROS), wherein:
the structural topology of the physical network layer is represented using an undirected graph G (N, L). Where N represents a set of physical network nodes and E represents a set of physical network links. For any physical network node ni∈ N, including at least CPU computing power C (N)i) Node position P (x)i,yi) Residual energy of
Figure BDA0002507464800000062
Three self attributes, subscript i, denote the index number of the physical network node. L is the set of physical network links between physical network nodes, for a physical network node niWith physical network node njPhysical network link l betweenij∈ L containing at least a bandwidth B (L)ij) This self-attribute, where i ≠ j, lij＝lji。
Structure topology using undirected graph G of virtual network provided by virtual network layerv(Nv,Lv) And (4) showing. Wherein N isvRepresenting a set of virtual network nodes, LvRepresenting a set of virtual network links. For a virtual network node
Figure BDA0002507464800000071
Which at least includes its own attribute of CPU computing power
Figure BDA0002507464800000072
For a virtual network node
Figure BDA0002507464800000073
And virtual network node
Figure BDA0002507464800000074
Virtual network link between
Figure BDA0002507464800000075
Which includes self-attributes including at least bandwidth
Figure BDA0002507464800000076
In various embodiments of the present invention, in response to a virtual network request directed to a particular virtual service, a virtual network is generated at the virtual network layer to serve the virtual service, the virtual network having a delay constraint to describe the quality of service of the virtual network. In one embodiment of the invention, as the generated M virtual networks responding to the M virtual network requests, use is made of
Figure BDA0002507464800000077
Representing a set of M virtual networks, wherein the time delay limiting conditions corresponding to each virtual network in the set are K in number, and the set of the K time delay limiting conditions is represented as
Figure BDA0002507464800000078
And is
Figure BDA0002507464800000079
Obviously, M.gtoreq.K. Migration of virtual networks, i.e. a virtual network in response to a request for the same virtual network, under an external condition
Figure BDA00025074648000000710
The actual resource allocation mapping at the physical network layer changes, so that the attributes of the physical network nodes or physical network links in the topology structure change without changing the topology structure of the physical network layer.
In various embodiments of the present invention, in response to a virtual network request, at the time of allocating physical network layer resources for a virtual network, that is, allocating CPU resources of physical network nodes for a plurality of virtual network nodes thereof, and/or allocating bandwidth resources of physical network links for a plurality of virtual network links thereof, for a physical network node niUsing Cinit(ni) Indicating its initial CPU computing power, before that time, i.e. before the allocation is not completed, using Cused(ni) Indicating that it has used CPU computing power after that time, i.e. after the allocation is complete, for a physical network link lijUsing Binit(lij) Indicating its initial bandwidth, before this moment, i.e. before allocation is not completed, using Bleft(lij) Indicating its remaining bandwidth after that time, i.e., after the allocation is complete.
In the embodiments of the present invention, for a physical network node, the remaining energy is used as a standard for measuring the life cycle of the physical network node, and the larger the remaining life cycle is, the better the reliability of the physical network node for bearing the virtual network service is. In one embodiment of the invention, the physical network node n is described using formula (1)i∈ N remaining Life cycle at a particular time t
Figure BDA00025074648000000711
Figure BDA0002507464800000081
Wherein,
Figure BDA0002507464800000082
representing the total energy consumption of the physical network node before time t,
Figure BDA0002507464800000083
representing the remaining energy of the physical network node at time t. As can be seen from equation (1), the remaining life cycle of a physical network node at a time is inversely proportional to its total energy consumption and directly proportional to its remaining energy.
Figure BDA0002507464800000084
Energy consumption E taking the value of historical transmitted data before time tsendEnergy consumption E associated with receiving datareceiveAnd (4) summing.
Exemplary oneEnergy consumption E for transmitting k-bit data by physical network nodesendCalculated using equation (2).
Figure BDA0002507464800000085
Energy consumption E for receiving k bits of datareceiveCalculated using equation (3).
Ereceive＝kE0(3)
Wherein E is0Representing the radio frequency energy consumption coefficient, related to the physical properties of the physical network node itself; pre represents an indicator vector for adjusting the topological distance dijIn relation to the exponential increase in energy consumption, a threshold value d is set for a physical network node0Topological distance dijHaving a specified exponent pre below the threshold and a greater exponent pre above the threshold, e.g. when d isij<d0When, the preferred pre is 2; when d isij≥d0When this is the case, pre is preferably 4. d0Is dijThe threshold value of (a), preferably,
Figure BDA0002507464800000086
fsrepresenting the energy attenuation coefficient and representing the multipath attenuation coefficient. dijRepresenting a physical network node ni∈ N and its next-hop physical network node Nj∈ N, preferably the distance is euclidean, using equation (4).
Figure BDA0002507464800000087
Based on the above exemplary description, one can obtain
Figure BDA0002507464800000088
One calculation process of (2) can be as shown in equation (5).
Figure BDA0002507464800000089
One aspect of the inventive concept is that to ensure that each physical network node has a large life cycle at all times, the traffic needs to be evenly distributed over the physical network nodes to avoid premature failure of some of the physical network nodes due to excessive energy consumption, η is used in one embodiment of the inventionreloadThe load balancing rate of a physical network layer at a time t is expressed and calculated by using the formula (6).
Figure BDA0002507464800000091
Wherein,
Figure BDA0002507464800000092
representing the maximum and minimum values of the energy remaining in all of its physical network nodes, respectively, from equation (6), it can be seen that the load balancing η for the physical network layer at a particular time isreloadThe more the value is greater than 1, the more the value is close to 1, the more balanced the residual energy of all the physical network nodes is, whether the energy consumption of each physical network node is balanced can be judged through the formula, and generally, the load balancing rate η of the physical network layer under the same resource allocation strategyreloadIs substantially stable. Similarly, use of
Figure BDA0002507464800000093
And the load balancing rate of the physical network layer after the resource allocation strategy is implemented last time is represented. When in use
Figure BDA0002507464800000094
Figure BDA0002507464800000095
And the current resource allocation strategy is expressed, so that the load balance of the network resources of the physical network layer can be better met.
One aspect of the inventive concept is that the communication time, or the time delay T, from one virtual network node to another virtual network node in the virtual network xxBelonging to the time dimensionThe degree is inconvenient to calculate, and in order to correlate the time delay of one virtual network with the attribute of a physical network layer, the time delay T between the nodes of the virtual network is determined by the inventionxConversion to hop count H between physical network nodes to which it is mappedx. In one embodiment of the invention, a virtual network
Figure BDA0002507464800000096
Hop count of HxCalculated using equation (7).
Figure BDA0002507464800000097
Wherein,
Figure BDA0002507464800000098
and the average value of the data processing time and the transmission time of each virtual network link in the virtual network x is represented.
In a complete embodiment, based on the above embodiments, steps S100 to S300 of the method of the present invention are implemented by the following specific steps.
The embodiment is realized by a resource arrangement module running on a cloud computing platform, the cloud computing platform is connected with and allocates resources of physical network nodes of a wireless sensor network and provides virtualized wireless sensor network services for a plurality of services, wherein the resource arrangement module adjusts a topological structure of a physical network layer of the virtualized wireless sensor network according to a virtual network request so as to ensure effective node occupation ratio of each physical network node of the physical network layer.
In this embodiment, based on a response or API call, the cloud computing platform provides a topology G (N, E) of a physical network layer to the resource orchestration module, and in the topology state, all services actually carried by the physical network layer correspond to M virtual networks, and a set of all virtual networks is
Figure BDA0002507464800000099
The resource arrangement module provides the cloud computing platform with the topological structure of the physical network layer through the methodThe new resource allocation strategy G '(N', E '), G' (N ', E') has the same topological structure as G (N, E), but the physical network nodes and the physical network links have different attributes, so that the cloud computing platform adjusts the physical network layer to provide the updated virtualized wireless sensor network service.
In particular, the resource orchestration module is configured to provide the new topology G ' (N ', E ') according to the following steps.
S100, according to a topological structure G (N, E) of a physical network layer, extracting the virtual network to be migrated from all the virtual networks carried by the topological structure.
Exemplarily, the present step includes the following sub-steps S101 to S104. Wherein,
s101, for any physical network node ni∈ N, calculating its remaining life cycle using equation (1)
Figure BDA0002507464800000101
S102, enabling the remaining life cycle to be smaller than the life cycle threshold value
Figure BDA0002507464800000102
Putting the set theta into the physical network nodes; there may be different lifecycle thresholds for different physical network nodes; the same physical network node, at different times, may have different lifecycle thresholds.
S103, putting the virtual network corresponding to the service borne on each physical network node in the set theta into a virtual network set omega, and marking the virtual network as a virtual network to be migrated;
s104, calculating the current load balancing coefficient of the physical network layer by using the formula (6), and assigning the current load balancing coefficient to the physical network layer
Figure BDA0002507464800000103
And S200, migrating the virtual network.
Exemplarily, the present step includes the following sub-steps S201 to S208. Wherein,
s201, traversing each virtual network in the set omega, and calculating the virtual networks by using a formula (7)Delay requirements, i.e. for any virtual network within the set Ω
Figure BDA0002507464800000104
Having a delay requirement Hx；
S202, arranging each virtual network in the set omega in an ascending order according to the time delay requirement of each virtual network to obtain the set omegaord；
S203, selecting a set omegaordA virtual network in
Figure BDA0002507464800000105
The initialization loop variable x is 1, i.e. the first time this step is performed, the selection set Ω should be selectedordWhen the step is executed each time later, x is sequentially increased by one;
s204, aiming at the current virtual network
Figure BDA0002507464800000106
Using Dijkstra algorithm to solve shortest path so as to obtain a new virtual network
Figure BDA0002507464800000107
And calculating the path hop count of the topology structure
Figure BDA0002507464800000108
The topological structure comprises a plurality of physical network links for all the physical network nodes bearing the virtual service;
s205, judging the current virtual network under the selected path
Figure BDA0002507464800000111
Whether or not it is less than its delay requirement Hx. If not, migration fails, and migration is not performed, and the step S203 is executed;
s206, calculating η load balancing coefficient of physical network layer under new topological structure of virtual networkreloadI.e. new resources in the virtual networkJudging whether the load balance coefficient under the source distribution mapping meets the requirement
Figure BDA0002507464800000112
Figure BDA0002507464800000113
If yes, jumping to step S208;
s207, if other paths exist in the Dijkstra algorithm solution in the step 204, selecting one of the paths with the shortest path as the current virtual network
Figure BDA0002507464800000114
Mapping the topological structure of (1), and returning to the step S205;
s208, migration is executed, and the selected virtual network is
Figure BDA0002507464800000115
Mapping to the path of the current topological structure, and returning to the step S203; the virtual network is considered to be a latency sensitive virtual network.
S300, evaluating whether the life cycles of all the physical nodes meet a threshold value; and repeating the steps S100 and S200, counting as one-time optimization, and if the repetition times exceed a preset optimization time threshold or no virtual network needing migration exists, ending the optimization process.
Exemplarily, the present step includes the following sub-steps S301 to S302. Wherein,
s301, judging whether the optimization time threshold T is exceeded or not, and if the optimization time threshold T is exceeded, ending;
s302, for each physical node ni∈ N, evaluating whether the life cycle satisfies a threshold
Figure BDA0002507464800000116
If yes, ending; if not, the set theta to be optimized is put in, and if the set theta is not empty, the step S102 is returned.
By adopting the method embodiments, the invention provides the following different specific environments as a plurality of specific embodiments to further illustrate the effects brought by the invention.
In various embodiments, considering that a maximum recovery available node Algorithm (mraa) is a relatively typical network reliability optimization method, the Algorithm of the present invention is compared with the Algorithm MRANA. And the MRANA algorithm selects all unavailable nodes and remaps the virtual network on the unavailable nodes by adopting a shortest path method. In the aspect of evaluation indexes, the effective node occupation ratio of a physical network layer is adopted for evaluation. The physical network effective node occupation ratio refers to the occupation ratio of the number of the physical network nodes with the remaining life cycles larger than the threshold value in the total number of the physical network nodes.
Detailed description of the preferred embodiment
In this embodiment, in the virtual network layer, the number of virtual networks is increased from 50 to 100, and the virtual network layer is used for simulating the influence of the number of virtual network requests of different scales on the performance of the algorithm. For each virtual network, the number of virtual network nodes is subject to the uniform distribution of (2, 4), and the CPU resource of each virtual network node is subject to the uniform distribution of (1, 5). The bandwidth resources of each virtual network link are subject to a uniform distribution of (1, 3). In terms of parameter setting, the remaining lifetime threshold of a physical network node is set to 10%, with a value set to 100,fsis set to 10, E0The value of (1) is set to be 50, the initial energy of the node is set to be 100, and the transmission energy consumption of 1bit data is 0.02.
In this embodiment, the number of physical network nodes is kept to 200, and a comparison result of the virtual network number on the effect of the effective nodes is shown in fig. 2. In the figure, the X-axis indicates that the number of virtual nets increases from 50 to 100. It can be known from the figure that with the increase of the number of virtual networks, the occupation ratios of the effective nodes under the two algorithms are increased, which shows that the number of virtual networks is increased, and the two algorithms can optimize more physical network nodes. Compared with the two algorithms, the algorithm has stronger network optimization capability, which shows that the algorithm can better optimize network resources.
Detailed description of the invention
In the embodiment, the number of physical network nodes of the physical network layer in the wireless sensor network is increased from 100 to 500, and the CPU resource of each physical network node is subject to uniform distribution (40, 50). The bandwidth resources of each physical network link are subject to a uniform distribution (20, 30).
In this embodiment, the number of virtual networks is kept to 70, and a comparison result of the influence of the physical network size on the effective nodes is shown in fig. 3. In the figure, the X-axis represents the network topology where the number of physical network nodes increases from 100 to 500. It can be known from the figure that with the increase of the number of the physical network nodes, the occupation ratios of the effective nodes under the two algorithms are increased, which shows that the number of the physical network nodes is increased, and the physical network resources which can be selected by the virtual network are rapidly increased, thereby better realizing the optimization of the network resources. Compared with two algorithms, the algorithm has stronger network optimization capability.

Claims (10)

1. A network reliability optimization method based on service priority and load balance is characterized in that the step of updating the resource allocation mapping of a virtual network in a virtualized wireless sensor network comprises the following steps: marking a virtual network needing migration at a first moment, and calculating the load balancing rate of a physical network layer at the moment; and at the second moment, if the load balancing rate of the physical network layer is less than the load balancing rate at the first moment after the new resource allocation mapping of the virtual network needing migration is executed, executing the new resource allocation mapping of the virtual network.
2. The method for network reliability optimization according to claim 1, wherein the method for tagging virtual networks that need migration is as follows: and at a moment, selecting the physical network nodes with the remaining life cycles smaller than a threshold value in the physical network layer, and marking the virtual network corresponding to each virtual service borne by the physical network nodes as the virtual network needing migration.
3. The network reliability optimization method according to claim 1 or 2, characterized in that: and for a plurality of virtual networks needing migration, preferentially providing a new resource allocation mapping for the virtual network with the lowest time delay requirement value.
4. The network reliability optimization method of claim 3, wherein: for a virtual network needing migration, the migration is tried after a new resource allocation mapping is obtained.
5. The method of claim 4, wherein: for a virtual network needing migration, the new resource allocation mapping is obtained by solving the shortest path at the physical network layer.
6. The method of claim 5, wherein: and for a virtual network needing migration, if the path hop number of the new resource allocation mapping is greater than the time delay requirement value of the virtual network, the new resource allocation mapping is not executed.
7. The method of claim 6, wherein the step of updating the resource allocation map of the virtual network in the virtualized wireless sensor network comprises:
s100, at a moment, acquiring physical network nodes with the remaining life cycle less than a threshold value in a physical network layer of the virtualized wireless sensor network, and marking virtual networks carried by the physical network nodes as virtual networks needing migration; obtaining the load balance coefficient of the physical network layer at the moment
Figure FDA0002507464790000011
S200, traversing all virtual networks needing migration, and distributing new resource distribution mapping obtained by a shortest path algorithm for the virtual networks from the virtual network with the lowest time delay requirement value; if the new resource allocation mapping meets the delay requirement of the virtual network and the load balancing requirement of the physical network layer, migrating the virtual network according to the new resource allocation mapping, otherwise, not migrating the virtual network;
and S300, repeatedly executing the steps S100 and S200, and counting as one-time optimization, wherein if the repeated times exceed a preset optimization time threshold value or no virtual network needing migration exists, the optimization process is ended.
8. The method of claim 7, wherein: the step S100 includes the steps of:
s101, for any physical network node ni∈ N, calculating its remaining life cycle
Figure FDA0002507464790000021
S102, remaining life cycle
Figure FDA0002507464790000022
Less than a life cycle threshold
Figure FDA0002507464790000023
Putting the set theta into the physical network nodes;
s103, putting the virtual network corresponding to the virtual service loaded on each physical network node in the set theta into a virtual network set omega, and marking the virtual network as a virtual network to be migrated;
s104, calculating the current load balancing coefficient of the physical network layer and assigning the current load balancing coefficient to the physical network layer
Figure FDA0002507464790000024
9. The method of claim 8, wherein: the step S200 includes the steps of:
s201, traversing each virtual network in the set omega, and calculating the time delay requirement of each virtual network, namely for any virtual network in the set omega
Figure FDA0002507464790000025
Having a delay requirement Hx；
S202, aiming at each virtual network in the set omegaAnd performing ascending arrangement according to the value of the time delay requirement of each virtual network to obtain a set omegaord；
S203, selecting a set omegaordA virtual network in
Figure FDA0002507464790000026
The initialization loop variable x is 1, i.e. the first time this step is performed, the selection set Ω should be selectedordThe virtual network with the minimum value of the medium delay requirement increases x by one in sequence before executing the step each time;
s204, aiming at the current virtual network
Figure FDA0002507464790000027
The virtual service of (2) uses Dijkstra algorithm to solve the shortest path in order to obtain a new virtual network for bearing the current
Figure FDA0002507464790000028
And calculating the path hop count of the topology structure
Figure FDA0002507464790000029
S205, judging the current virtual network
Figure FDA00025074647900000210
Whether or not it is less than its delay requirement Hx. If so, executing step S203;
s206, calculating η load balancing coefficient of physical network layer under new topological structure of virtual networkreloadDetermine whether or not to satisfy
Figure FDA00025074647900000211
If yes, jumping to step S208;
s207, if other paths exist in the Dijkstra algorithm solution in the step 204, selecting one of the paths with the shortest path as the current virtual network
Figure FDA00025074647900000212
Mapping the topological structure of (1), and returning to the step S205;
s208, migration is executed, and the selected virtual network is
Figure FDA00025074647900000213
And mapping to the path of the current topological structure, and returning to the step S203.
10. The method of claim 9, wherein: the step S300 includes the steps of:
s301, judging whether the optimization time threshold T is exceeded or not, and if the optimization time threshold T is exceeded, ending;
s302, for each physical node ni∈ N, evaluating whether its remaining life cycle is less than a threshold
Figure FDA0002507464790000031
If yes, ending; if not, step S102 is executed.
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