CN106209618A - A kind of communication mixed networking method and system improving intelligence adapted electric energy effect - Google Patents
A kind of communication mixed networking method and system improving intelligence adapted electric energy effect Download PDFInfo
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- CN106209618A CN106209618A CN201610600060.6A CN201610600060A CN106209618A CN 106209618 A CN106209618 A CN 106209618A CN 201610600060 A CN201610600060 A CN 201610600060A CN 106209618 A CN106209618 A CN 106209618A
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- 238000004891 communication Methods 0.000 title claims abstract description 23
- 230000000694 effects Effects 0.000 title claims abstract description 20
- 230000006855 networking Effects 0.000 title claims abstract description 18
- 239000002245 particle Substances 0.000 claims abstract description 50
- 238000005265 energy consumption Methods 0.000 claims description 37
- 239000011159 matrix material Substances 0.000 claims description 31
- 230000009191 jumping Effects 0.000 claims description 27
- 238000010845 search algorithm Methods 0.000 claims description 2
- 230000005059 dormancy Effects 0.000 description 17
- 238000010586 diagram Methods 0.000 description 13
- 238000005457 optimization Methods 0.000 description 5
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/12—Shortest path evaluation
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/20—Hop count for routing purposes, e.g. TTL
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/22—Alternate routing
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L45/00—Routing or path finding of packets in data switching networks
- H04L45/302—Route determination based on requested QoS
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- 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
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Abstract
The present invention provides a kind of communication mixed networking method and system improving intelligence adapted electric energy effect, the method includes: ask size according to portfolio, utilizes discrete particle cluster algorithm to be followed successively by route requests between source node and destination node in intelligence adapted electric network and carries out energy efficiency priority pathfinding；Route requests between the source node failed for energy efficiency priority pathfinding and destination node, utilizes Depth Priority Algorithm to carry out heavy-route.This system includes energy efficiency priority pathfinding module and heavy-route module.The present invention according to portfolio request size be intelligence adapted electric network between source node and destination node route requests sequence；On the premise of meeting QoS constraint, utilizing discrete particle cluster algorithm is that the most each route requests finds available path as much as possible；Carrying out heavy-route during energy efficiency priority pathfinding failure, use many groups of the Depth Priority Algorithm stochastic generation alternative path less than single route maximum hop count, the path therefrom selecting fitness value minimum carries out heavy-route.
Description
Technical field
The invention belongs to constructing communication network technical field, a kind of communication mixed networking improving intelligence adapted electric energy effect
Method and system.
Background technology
Along with the user's continuous growth to communication requirement, network size is the hugest, and its energy consumption problem is the most increasingly severe.
Network energy consumption problem is increasingly becoming the key factor of restriction cloud computing development.Research finds, in prevailing network, the link of 20% holds
Carrying the flow load of 80%, the design of this explanation major part network is all unreasonable, could not realize load balancing.It addition, it is big
Subnetwork all be use Redundancy Design to meet the network bursting problem such as burst flow, link congestion, but bursting problem is sent out
Raw probability is little, the most therefore wastes substantial amounts of resource.And most network energy-saving algorithm is all merely in order to energy-conservation and set
Meter, after not accounting for dormancy link, whether the performance of network can reduce.Accordingly, it would be desirable to a kind of side real-time, that efficiency is high
Method, to reduce the energy consumption of network.
Software defined network (Software Defined Network, SDN) is a kind of novel innovation mechanism, for electric power
Network virtualization in communication network provides suitable solution.The core concept of SDN is to control from physical network solution decoupling
Aspect controls data plane, defines various communications network interface simultaneously.SDN for network virtualization is one and has suction
The platform of gravitation, owing to each control unit of communication network may operate on a controller rather than at the switch of physics
On.Particularly OpenFlow forwards rule, query flows statistics, topology change notification to provide for packet in switch traffic table
One standard interface.
Currently, there have been some achievements in research about the heuristic solving strategy method of the Optimized model of network energy efficiency.Such as
Adaptive power conservation routing algorithm based on ant group algorithm, the online method for routing of efficiency of multiple constraint, the weight wherein routeing and road
Being another the novel evolutionary computation technique proposed after ant group algorithm by request particle cluster algorithm, result is preferable.But it is former
Beginning particle group optimizing is difficult in the optimization problem at discrete space, such as routing issue directly apply.Therefore, it is necessary to improve or
Be combined with other method.
Visible, seldom have and will improve network energy efficiency and ensure the link optimum rate of utilization restricted problem entirety knot of network
Close and consider, be all that part considers.
Summary of the invention
The deficiency existed for prior art, the present invention provides a kind of communication mixed networking improving intelligence adapted electric energy effect
Method and system.
The technical scheme is that
A kind of communication mixed networking method improving intelligence adapted electric energy effect, including:
Ask size according to portfolio, utilize discrete particle cluster algorithm to be followed successively by source node and mesh in intelligence adapted electric network
Internodal route requests carry out energy efficiency priority pathfinding；
Route requests between the source node failed for energy efficiency priority pathfinding and destination node, utilizes depth-first search to calculate
Method carries out heavy-route, to reduce route requests obstruction number.
Described according to portfolio request size, utilize discrete particle cluster algorithm to be followed successively by source node in intelligence adapted electric network
With the route requests between destination node carries out energy efficiency priority pathfinding, including:
According to portfolio request size be intelligence adapted electric network between source node and destination node route requests sequence；
On the premise of meeting QoS constraint, utilizing discrete particle cluster algorithm is that the most each route requests is found the most
Available path；
Search from available path and meet this route requests correspondence portfolio and ask and make intelligent adapted electric network energy consumption
Increase minimum path；
Energy efficiency priority pathfinding result is obtained according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route
The energy efficiency priority pathfinding result of request route, and updates the link load matrix of intelligence adapted electric network；
If the path found is more than one, then optimizes network delay, from the mulitpath found, i.e. select total jumping figure
Minimum path, as the energy efficiency priority pathfinding result of route requests, carries out road according to the energy efficiency priority pathfinding result of route requests
By, update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
Route requests between the described source node failed for energy efficiency priority pathfinding and destination node, utilizes depth-first to search
Rope algorithm carries out heavy-route, to reduce route requests obstruction number, including:
Use many groups of the Depth Priority Algorithm stochastic generation source node less than single route maximum hop count to purpose
The alternative path of node；
Judge that whether alternative path is less than link optimum rate of utilization threshold value: be, then calculate corresponding the fitting of this alternative path
Response functional value, otherwise, is set to infinity by the fitness value of this alternative path；
The path that fitness value is minimum is selected from alternative path；
Judge that heavy-route is the most successful according to fitness value: if this fitness value is infinitely great, then heavy-route failure, this
Route requests stops, and route requests is blocked number and added one, returns source node next in intelligence adapted electric network and destination node
Between route requests carry out energy efficiency priority pathfinding；Otherwise heavy-route success, utilizes the chain of this routing update intelligence adapted electric network
Road load matrix, it is excellent that return carries out efficiency to the route requests between source node next in intelligence adapted electric network and destination node
First pathfinding.
A kind of communication mixed networking system improving intelligence adapted electric energy effect, including:
Energy efficiency priority pathfinding module, for asking size according to portfolio, utilizes discrete particle cluster algorithm to be followed successively by intelligence
In adapted electric network, the route requests between source node and destination node carries out energy efficiency priority pathfinding；
Heavy-route module, the route requests between the source node failed for energy efficiency priority pathfinding and destination node, profit
Heavy-route is carried out, to reduce route requests obstruction number with Depth Priority Algorithm.
Described energy efficiency priority pathfinding module, including:
Route requests order module, for asking size to be source node and purpose in intelligence adapted electric network according to portfolio
Internodal route requests sorts；
Available path finds module, and on the premise of meeting QoS constraint, it is the most each for utilizing discrete particle cluster algorithm
Route requests finds available path as much as possible；
Path searching module, meets this route requests correspondence portfolio for lookup from available path and asks and make intelligence
Adapted electric network energy consumption can increase minimum path；
Path judge module, for obtaining energy efficiency priority pathfinding result according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route
The energy efficiency priority pathfinding result of request route, and updates the link load matrix of intelligence adapted electric network；
If the path found is more than one, then optimizes network delay, from the mulitpath found, i.e. select total jumping figure
Minimum path, as the energy efficiency priority pathfinding result of route requests, carries out road according to the energy efficiency priority pathfinding result of route requests
By, update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
Described heavy-route module, including:
Alternative path generation module, is used for using many groups of Depth Priority Algorithm stochastic generation to route less than single
The source node of big jumping figure is to the alternative path of destination node；
Alternative path judge module, is used for judging that whether alternative path is less than link optimum rate of utilization threshold value: be then to count
Calculate the fitness function value that this alternative path is corresponding, otherwise, the fitness value of this alternative path is set to infinity；
Path selection module, for selecting the path of fitness value minimum from alternative path；
According to fitness value, heavy-route result judge module, for judging that heavy-route is the most successful: if this fitness value is
Infinity, then heavy-route failure, this route requests stops, and route requests is blocked number and added one, returns intelligence adapted electric network
Route requests between middle next source node and destination node carries out energy efficiency priority pathfinding；Otherwise heavy-route success, utilizes this road
Footpath updates the link load matrix of intelligence adapted electric network, returns source node next in intelligence adapted electric network and purpose joint
Route requests between point carries out energy efficiency priority pathfinding.
Beneficial effect:
The shortcoming existed for prior art, entirety generally solves the problem of the network energy efficiency improved in cloud computing, this
Invention proposes a kind of communication mixed networking method improving intelligence adapted electric energy effect.
On the premise of meeting QoS constraint, the preferential efficiency improving network, utilizes the bit energy consumption parameter of network to weigh
The efficiency of network, wherein the least to represent network energy efficiency the highest for bit energy consumption.To this, according to minimizing net bit energy consumption and request
Total jumping figure carry out energy efficiency priority route, and ensure that the link optimum rate of utilization of network limits by heavy-route strategy, reduce
Number is blocked in request.The present invention is road between source node and destination node in intelligence adapted electric network according to portfolio request size
Sorted by request；On the premise of meeting QoS constraint, utilizing discrete particle cluster algorithm is that the most each route requests is found as far as possible
Many available paths；Search from available path and meet this route requests correspondence portfolio and ask and make intelligent adapted electric network
Energy consumption increases minimum path, if the path found is zero, then and energy efficiency priority pathfinding failure, carry out heavy-route, use the degree of depth
Many groups of first search algorithm stochastic generation route the source node alternative path to destination node of maximum hop count less than single, from
The path selecting fitness value minimum in alternative path carries out heavy-route success.
Accompanying drawing explanation
Fig. 1 is the flow process of the communication mixed networking method improving intelligence adapted electric energy effect in the specific embodiment of the invention
Figure；
Fig. 2 is the step 1 of the communication mixed networking method improving intelligence adapted electric energy effect in the specific embodiment of the invention
Flow chart；
Fig. 3 is the step 2 of the communication mixed networking method improving intelligence adapted electric energy effect in the specific embodiment of the invention
Flow chart；
Fig. 4 is the communication mixed networking system block diagram improving intelligence adapted electric energy effect in the specific embodiment of the invention；
Fig. 5 is that the efficiency of the communication mixed networking system improving intelligence adapted electric energy effect in the specific embodiment of the invention is excellent
First pathfinding module frame chart；
Fig. 6 is the heavy-route of the communication mixed networking system improving intelligence adapted electric energy effect in the specific embodiment of the invention
Module frame chart；
Fig. 7 is the oriented topological diagram of specific embodiment of the invention interior joint number n=4；
Fig. 8 is particle f in the specific embodiment of the inventioni(k) value flow chart；
Fig. 9 is COST239 network topological diagram；
Figure 10 is NSFNET network topological diagram；
Figure 11 is ItalyNET network topological diagram；
Figure 12 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Network total energy consumption correlation curve figure under parameter；
Figure 13 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Net bit energy consumption comparison curve chart under parameter；
Figure 14 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Number correlation curve figure is blocked in request under parameter；
Figure 15 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Dormancy number of links correlation curve figure under parameter；
Figure 16 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Route requests total jumping figure correlation curve figure under parameter；
Figure 17 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
Average link utilization correlation curve figure under parameter；
Figure 18 is high threshold and the route maximum delay in the specific embodiment of the invention at different link utilizations
The total energy consumption correlation curve figure that heterogeneous networks under parameter is opened up；
Figure 19 is the bit energy consumption comparison figure of heterogeneous networks topology in the specific embodiment of the invention；
Figure 20 is the average link utilization comparison diagram of heterogeneous networks topology in the specific embodiment of the invention；
Figure 21 is total jumping figure comparison diagram of heterogeneous networks topology in the specific embodiment of the invention；
Figure 22 is the bit energy consumption comparison figure of three kinds of methods in the specific embodiment of the invention；
Figure 23 is that number comparison diagram is blocked in the request of three kinds of methods in the specific embodiment of the invention；
Figure 24 is the dormancy number of links comparison diagram of three kinds of methods in the specific embodiment of the invention；
Figure 25 is the average link utilization comparison diagram of three kinds of methods in the specific embodiment of the invention；
Figure 26 is the request of three kinds of methods total jumping figure comparison diagram in the specific embodiment of the invention.
Detailed description of the invention
Below in conjunction with the accompanying drawings the detailed description of the invention of the present invention is elaborated.
The symbol that the present invention relates to is defined as follows:
V: all node set in topology
N: intelligence adapted radio network node number
E: all link set in topology
CijLink (i, capacity j)
F(xij): the energy consumption function of link
xijRepresent link (i, flow load j)
α represents the high threshold of link utilization
psD represents the source s link set to the path of purpose d
SD represents that source node s is to destination node d requested service amount
Link represents network topology matrix i.e. link load matrix
TM represents traffic matrix
A kind of communication mixed networking method improving intelligence adapted electric energy effect, as it is shown in figure 1, include:
Step 1, according to portfolio ask size, utilize discrete particle cluster algorithm be followed successively by intelligence adapted electric network in source joint
Route requests between point and destination node carries out energy efficiency priority pathfinding；
Described step 1, as in figure 2 it is shown, include:
Step 1-1, it is route between source node and destination node in intelligence adapted electric network according to portfolio request size
Request sequence；
Step 1-1-1, initialization intelligence adapted electric network parameter, including:
Network state (route requests), routing parameter (route requests blocked state in intelligence adapted electric network), intelligence is joined
Power utilization network nodes n, intelligence adapted electric network network topology matrix (including link load matrix L ink and traffic matrix TM),
Link (i, capacity C j)ij, and flow load x corresponding to this linkij, the high threshold α of link utilization and single route
In maximum hop count maxhop, link load matrix L ink, element is set to 0；
Step 1-1-2, according to portfolio request size from big to small, route requests is sorted and generates route requests queue
R；
Step 1-2, meet QoS constraint on the premise of, utilize discrete particle cluster algorithm be the most each route requests find
Available path as much as possible；
If step 1-2-1 route requests queue R non-NULL, then taking out first route from route requests queue R please
Ask, determine the source node s of route requests, destination node d and portfolio request SD, if route requests queue R be sky, then from
Shot swarm optimization terminates；
As it is shown in fig. 7, the oriented topological diagram that intelligent adapted electric network is nodes n=4 of present embodiment, this topological diagram
In the collection of all nodes share V and represent；If source node s=1 and destination node d=3, then the possibility of the 1st particle in population
Position isWhereinAndThat is X1=
[(Isosorbide-5-Nitrae), (4,3)], this particle position illustrates a feasible path from source node 1 to destination node 3: { 1 → 4 → 3}.
Particle fi(k) sampling process as shown in Figure 8, fiK () defines i-th particle rapidity ViKth dimension should be to which
The current optimal location pbest study of particle, namely carries out speed renewal according to the pbest of this particle.fiTaking of (k)
Value is decided by probability P c.It is to say, for the random number r between the every one-dimensional generation one (0,1) of particle i, if r >=Pc particle
I will learn to the pbest of oneself, i.e. fi(k)=i；Otherwise, particle i will take tournament selection to choose another particle
Pbest learn.
Owing to original particle cluster algorithm is difficult to directly apply on routing issue, therefore population is expanded at discrete space
Exhibition, it is proposed that discrete particle cluster algorithm.
Step 1-2-2, using route requests as particle, initialize particle populations size Popsize i.e. route requests queue R
Middle element number, maximum iteration time Sg；
Step 1-2-3, randomly generate initial feasible solution:
According to random depth-first traversal algorithm, produce the initial position matrix X of each particlei(i=1,2,
...Popsize)；Same method randomly generates the current optimal location matrix pbest of each particlei(i=1,2,
...Popsize)；From link load matrix L ink, randomly choose n bar one way link, and be n bar one way link random assortment
(0,1] between probability, obtain the initial velocity matrix V of each particlei(i=1,2 ... Popsize)；According to fitness function
Fitness(Xi) calculate X respectivelyi(i=1,2 ... Popsize) corresponding fitness function value, adaptive optimal control degree functional value pair
The particle position answered is designated as global optimum position gbest；
Fitness function is expressed asWherein, F (xij) represent chain
The energy consumption function on road, Δ EC (Xi) represent intelligence adapted electric network energy consumption increments,Represent that intelligence is joined
Total jumping figure in power utilization network.And intelligence adapted electric network energy consumption increments is the least, fitness function value is the least；Total jumping figure is more
Few, fitness function value is the least；
Step 1-2-4, each particle is carried out successively speed renewal；
Speed more new formula is as follows:
Wherein,Represent the kth dimension of the velocity vector of i-th particle；fiK () represents i-th particle rapidity ViKth dimension
Should learn to the pbest of which particle,For the kth position of i-th particle, w is that speed updates coefficient, and Popsize is
Particle populations size, coefficient c=2, randkFor the random number produced between (0,1).
Step 1-2-5, each particle is carried out successively location updating；
Location updating employing formula below:
Xi=Xi+Vi
In position updating process, need to consider whether to meet link utilization high threshold α, only meet constraint
Arc just can be selected to update particle position.First, new for particle position is empty.Owing to routing procedure is from source node to purpose
The pathfinding process of node, therefore renewal process is from the beginning of source node s, according to priority, from set SV, SXAnd SEMiddle selection with
Next-hop node (the S that present node is connectedVFor ViIn the node set that is connected with source node s, SXFor XiIn be connected with source node s
Node set, SEFor the node set being connected with source node s in link set E, psdExpression source s is on the path of purpose d
Link set), after selected next-hop node, then select next-hop node for it, by that analogy, until updating to destination node d
Time, one time routing procedure terminates, and end position updates, by new position new_XiIt is assigned to position Xi。
Step 1-2-6, assessment update result: for each particle, judge its more newly obtained location matrix X successivelyi's
Fitness function value Fitness (Xi) whether it is better than the fitness function value Fitness (pbest of current optimal locationi) and the overall situation
Fitness function value Fitness (gbest) of optimal location, is then to update current optimal location pbestiWith global optimum position
Put gbest, otherwise, it is not necessary to update, maintain current optimal location；
Step 1-3, search from available path and meet this route requests correspondence portfolio and ask and make intelligence adapted electric
Network energy consumption increases minimum path；
Judge whether the route result that current global optimum position gbest represents meets constraints and (i.e. meet this road
Asked and make intelligence adapted electric network energy consumption to increase minimum by the corresponding portfolio of request), if it is satisfied, then forward step 1-4 to,
If be unsatisfactory for, then carry out heavy-route；
Step 1-4, the energy efficiency priority pathfinding result that obtains according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route
The energy efficiency priority pathfinding result of request route, and updates the link load matrix of intelligence adapted electric network；
If the path found is more than one, then optimizes network delay, from the mulitpath found, i.e. select total jumping figure
Minimum path, as the energy efficiency priority pathfinding result of route requests, carries out road according to the energy efficiency priority pathfinding result of route requests
By, update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
Step 2, for the route requests between the failed source node of energy efficiency priority pathfinding and destination node, utilize depth-first
Searching algorithm carries out heavy-route, to reduce route requests obstruction number.
Described step 2, as it is shown on figure 3, include:
Step 2-1, employing Depth Priority Algorithm stochastic generation M group are less than the source node of single route maximum hop count
Alternative path X to destination nodei_ backup (i=1,2 ... M)；
Step 2-1-1: arranging present node CN is source node, i.e. CN=s；
Step 2-1-2: present node CN is added to set S, set S and deposits the node accessed；
Step 2-1-3: create set FN, FN=[all nodes being connected with CN]；
Step 2-1-4: if the common factor of the supplementary set of FN Yu S is not empty and set S interior joint number is less than maxhop+1, then with
Machine select in common factor a node as CN, and put it into set S in；Otherwise empty set S so that CN=s, and by it
It is added to gather S；
Step 2-1-5: judge that CN, whether equal to destination node d, is to jump to step 2-1-6, otherwise jumps to step 2-1-
3；
Step 2-1-6: the sequentially each node in output set S, is a source node s alternative road to destination node d
Footpath, as an initial position of particle.
Step 2-2, judge that alternative path is whether less than link optimum rate of utilization threshold value: be then to calculate this alternative path
Corresponding fitness function value, otherwise, is set to infinity by the fitness value of this alternative path；
Step 2-3, the path that selection fitness value is minimum from alternative path；
Step 2-4, judge heavy-route whether success according to fitness value: if this fitness value is infinitely great, then heavy-route
Failure, this route requests stops, and route requests is blocked number and added one, return next source node in intelligence adapted electric network and
Route requests between destination node carries out energy efficiency priority pathfinding；Otherwise heavy-route success, by current route requests from route requests
Queue R deletes, utilizes the link load matrix of this routing update intelligence adapted electric network, return in intelligence adapted electric network
Route requests between next source node and destination node carries out energy efficiency priority pathfinding.
A kind of communication mixed networking system improving intelligence adapted electric energy effect, as shown in Figure 4, including:
Energy efficiency priority pathfinding module, for asking size according to portfolio, utilizes discrete particle cluster algorithm to be followed successively by intelligence
In adapted electric network, the route requests between source node and destination node carries out energy efficiency priority pathfinding；
Heavy-route module, the route requests between the source node failed for energy efficiency priority pathfinding and destination node, profit
Heavy-route is carried out, to reduce route requests obstruction number with Depth Priority Algorithm.
Described energy efficiency priority pathfinding module, as it is shown in figure 5, include:
Route requests order module, for asking size to be source node and purpose in intelligence adapted electric network according to portfolio
Internodal route requests sorts；
Available path finds module, and on the premise of meeting QoS constraint, it is the most each for utilizing discrete particle cluster algorithm
Route requests finds available path as much as possible；
Path searching module, meets this route requests correspondence portfolio for lookup from available path and asks and make intelligence
Adapted electric network energy consumption can increase minimum path；
Path judge module, for obtaining energy efficiency priority pathfinding result according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route
The energy efficiency priority pathfinding result of request route, and updates the link load matrix of intelligence adapted electric network；
If the path found is more than one, then optimizes network delay, from the mulitpath found, i.e. select total jumping figure
Minimum path, as the energy efficiency priority pathfinding result of route requests, carries out road according to the energy efficiency priority pathfinding result of route requests
By, update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
Described heavy-route module, as shown in Figure 6, including:
Alternative path generation module, is used for using many groups of Depth Priority Algorithm stochastic generation to route less than single
The source node of big jumping figure is to the alternative path of destination node；
Alternative path judge module, is used for judging that whether alternative path is less than link optimum rate of utilization threshold value: be then to count
Calculate the fitness function value that this alternative path is corresponding, otherwise, the fitness value of this alternative path is set to infinity；
Path selection module, for selecting the path of fitness value minimum from alternative path；
According to fitness value, heavy-route result judge module, for judging that heavy-route is the most successful: if this fitness value is
Infinity, then heavy-route failure, this route requests stops, and route requests is blocked number and added one, returns intelligence adapted electric network
Route requests between middle next source node and destination node carries out energy efficiency priority pathfinding；Otherwise heavy-route success, utilizes this road
Footpath updates the link load matrix of intelligence adapted electric network, returns source node next in intelligence adapted electric network and purpose joint
Route requests between point carries out energy efficiency priority pathfinding.
Present embodiment uses system for cloud computing COST239, NSFNET and ItalyNET network of three kinds of different scales
The data on flows of topological structure and synthesis analyzes the effectiveness of the inventive method.COST239, NSFNET and ItalyNET network
Topological structure is respectively as shown in Fig. 9,10,11.
In present embodiment, the network under the high threshold and route maximum delay parameter of different link utilizations is total
Energy consumption comparison curve is as shown in figure 12, it is considered to high threshold parameter Alpha of link utilization and route maximum delay parameter
Maxhop is on network performance and the impact of network energy efficiency effect, and wherein Alpha has 80% and 95% two kind of situation, and maxhop includes
There is maximum hop count constraint (maxhop=6) and retrain (maxhop=Inf) two kinds of situations without maximum hop count.Can from Figure 12
Going out, under various boundary conditions, along with the increase of flow average demand, the total energy consumption of network is also continuously increased.
Network ratio under the high threshold and route maximum delay parameter of different link utilizations in present embodiment
Special energy consumption comparison curve as shown in figure 13, it can be observed from fig. 13 that under various boundary conditions, along with flow average demand
Increasing, the bit power consumption of network constantly reduces.All can be seen that from Figure 12 and Figure 13, the total energy consumption value of network when High traffic loads
Height, but bit power consumption values is relatively small, say, that in the case of High traffic loads, more can embody the high energy efficiency of network
Energy.
Request resistance under the high threshold and route maximum delay parameter of different link utilizations in present embodiment
As shown in figure 14, this figure shows plug number correlation curve, under different restraint conditions, although average traffic request is continuously increased,
The inventive method can be successfully completed all of request, and request is blocked number and remained zero.
Dormancy chain under the high threshold and route maximum delay parameter of different link utilizations in present embodiment
As shown in figure 15, this figure show along with the increase of average traffic request way, and under different restraint conditions, the inventive method obtains
The situation that the network link arrived is idle.When link idle, link power consumption is zero, and link is in a dormant state.Can from Figure 12
To find out, under various boundary conditions, along with the increase of flow average demand, the number of link in network dormancy is in gradually lacking to become
Gesture.In the case of having, without hop count constraint, the high threshold of link utilization be network link dormancy base when 95% this
On be also link dormant number when 80% more than the high threshold of link utilization.Complex chart 9 and Figure 10, it can be seen that this
Bright method is when high flow capacity is asked, blocked without request, and still has part of links by dormancy, and method performance still keeps preferable.
In present embodiment, the route under the high threshold and route maximum delay parameter of different link utilizations please
Seek total jumping figure as shown in figure 16；In fig. 16, it can be seen that under identical link utilization thresholds, have maximum jumping about
Total jumping figure during bundle is substantially less than without the maximum total jumping figure jumped when retraining.
Average chain under the high threshold and route maximum delay parameter of different link utilizations in present embodiment
Road utilization rate is as shown in figure 17；Figure 17 describes the link average link utilization of statistics under different restraint condition.Can see
Going out, along with being continuously increased of average traffic request, network opens the average link utilization of link also in increasing trend.And
Have, nothing maximum is jumped in the case of retraining two kinds, and the high threshold of link utilization is that average link utilization when 95% is basic
On to be above the high threshold of link utilization be average link utilization when 80%, and bigger in average traffic request
Time become apparent from.
Different nets under the high threshold and route maximum delay parameter of different link utilizations in present embodiment
The total energy consumption that network is opened up is as shown in figure 18；Figure 18 shows, along with the increase of average traffic request, 3 kinds of different scales network topologies
Total energy consumption all in increase trend；Obviously, on the premise of identical average traffic is asked, network size is the biggest, and total energy consumption is more
Greatly, i.e. the total energy consumption of ItalyNET is more than NSFNET, and the total energy consumption of NSFNET is more than COST239's.
In present embodiment, the bit energy consumption of heterogeneous networks topology is as shown in figure 19, shows with average traffic request
Increase, the situation of change of bit energy consumption.It can be seen that for the network of three kinds of different scales, along with average traffic please
The increase asked, the bit energy consumption of network is all in reducing trend.
In present embodiment, the average link utilization of heterogeneous networks topology is as shown in figure 20；It can be seen that along with averagely
Being continuously increased of portfolio request, the link average utilization of 3 kinds of different scales networks is all in ever-increasing trend.NSFNET
Average link utilization more than ItalyNET, and the average link utilization of ItalyNET is more than COST239's.
In present embodiment, total jumping figure of heterogeneous networks topology compares as shown in figure 21；It can be seen that total jumping figure is along with flat
Being continuously increased in fluctuation status of equal traffic demand；The network that scale is the biggest, its total jumping figure is the biggest, i.e. total jumping figure of ItalyNET
More than NSFNET, and total jumping figure of NSFNET is more than COST239's.
In present embodiment, the bit energy consumption comparison of three kinds of methods is as shown in figure 22；It can be seen that along with flow averagely needs
The increase asked, the bit power consumption of network constantly reduces.Wherein, the network of OSPF (Open Shortest Path First route) method for routing
Bit power consumption values is the highest, next to that GreenOSPF (Green Open Shortest Path First, dormancy route) method,
The inventive method (S-PSO-EERA, Set-based Particle Swarm Optimization based Energy
Efficient Routing Algorithm) net bit power consumption values be respectively less than the first two method for routing, and at flow
Become apparent from when loading relatively low.By to the simulation analysis of three kinds of methods it may be concluded that i.e. the inventive method has higher
Network energy efficiency, and efficiency advantage becomes apparent from when relatively low flow load.
In present embodiment, the request of three kinds of methods blocks number contrast as shown in figure 23；Compare is at same traffic matrix
In the case of, number is blocked in the request of three kinds of methods.Although it can be seen that average traffic request increase, this method,
The request of OSPF method is blocked number and is all remained zero, and GreenOSPF method increases to 100Gbps's in average traffic request
In the case of, create a request and block.
In present embodiment, the dormancy number of links of three kinds of methods contrasts as shown in figure 24；Illustrate and ask with average traffic
Increase, the change of the dormancy number of links of three kinds of methods.Simulation result from figure it can be seen that compared with control methods, the present invention
The network link dormancy number of method is the highest, and in control methods, the link dormant effect of GreenOSPF method is better than OSPF method,
Wherein the link dormant number of GreenOSPF method is fixed, and does not has the link of dormancy in OSPF method.And for the present invention
For method, along with the increase of average traffic request, the dormancy number of links of network is in reducing trend.Wherein, relatively low in load
Time the highest can be with 31 links of dormancy, also can 14 links of dormancy when loading higher.
In present embodiment, the average link utilization of three kinds of methods contrasts as shown in figure 25, the unlatching link of three kinds of methods
Average link utilization with average traffic request increase situation of change.Simulation result from figure this it appears that along with
The increase of average traffic request, the average link utilization of three kinds of methods is all ever-increasing.Due to the inventive method
Dormancy link is more, and the link i.e. opened is less, and therefore the average link utilization of the method is the highest, GreenOSPF method time
It, and OSPF method there is no the link of dormancy, because the link average utilization of the method is minimum.
In present embodiment, the total jumping figure of the request of three kinds of methods contrasts as shown in figure 26, and statistics is the request of three kinds of methods
Total jumping figure.It can be seen that the inventive method is owing to bit energy consumption as main target of optimization, therefore its total jumping figure is above it
His two kinds of methods.It is to say, the inventive method sacrifices secondary optimization aim-total jumping figure, with exchange for main target of optimization-
The raising of efficiency.
Claims (6)
1. the communication mixed networking method improving intelligence adapted electric energy effect, it is characterised in that including:
Ask size according to portfolio, utilize discrete particle cluster algorithm to be followed successively by source node and purpose joint in intelligence adapted electric network
Route requests between point carries out energy efficiency priority pathfinding；
Route requests between the source node failed for energy efficiency priority pathfinding and destination node, utilizes Depth Priority Algorithm to enter
Row heavy-route, to reduce route requests obstruction number.
Method the most according to claim 1, it is characterised in that described according to portfolio request size, utilizes discrete particle
Group's algorithm is followed successively by route requests between source node and destination node in intelligence adapted electric network and carries out energy efficiency priority pathfinding, bag
Include:
According to portfolio request size be intelligence adapted electric network between source node and destination node route requests sequence；
Meet QoS constraint on the premise of, utilize discrete particle cluster algorithm be the most each route requests find as much as possible can
Use path；
From available path search meet this route requests correspondence portfolio ask and makes intelligence adapted electric network energy consumption increase
Minimum path；
Energy efficiency priority pathfinding result is obtained according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route requests
Energy efficiency priority pathfinding result route, update intelligence adapted electric network link load matrix；
If the path found is more than one, then optimize network delay, i.e. select total jumping figure minimum from the mulitpath found
Path as the energy efficiency priority pathfinding result of route requests, route according to the energy efficiency priority pathfinding result of route requests,
Update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
Method the most according to claim 1, it is characterised in that the described source node failed for energy efficiency priority pathfinding and mesh
Internodal route requests, utilize Depth Priority Algorithm to carry out heavy-route, with reduce route requests block number, including:
Use many groups of the Depth Priority Algorithm stochastic generation source node less than single route maximum hop count to destination node
Alternative path；
Judge that whether alternative path is less than link optimum rate of utilization threshold value: be then to calculate the fitness that this alternative path is corresponding
Functional value, otherwise, is set to infinity by the fitness value of this alternative path；
The path that fitness value is minimum is selected from alternative path；
Judge that heavy-route is the most successful according to fitness value: if this fitness value is infinitely great, then heavy-route failure, this route
Request stops, and route requests is blocked number and added one, returns between source node next in intelligence adapted electric network and destination node
Route requests carries out energy efficiency priority pathfinding；Otherwise heavy-route success, utilizes the link of this routing update intelligence adapted electric network to bear
Carrying matrix, return carries out energy efficiency priority to the route requests between source node next in intelligence adapted electric network and destination node and seeks
Road.
4. the communication mixed networking system improving intelligence adapted electric energy effect, it is characterised in that including:
Energy efficiency priority pathfinding module, for asking size according to portfolio, utilizes discrete particle cluster algorithm to be followed successively by intelligence adapted
In electric network, the route requests between source node and destination node carries out energy efficiency priority pathfinding；
Heavy-route module, the route requests between the source node failed for energy efficiency priority pathfinding and destination node, utilize deep
Degree first search algorithm carries out heavy-route, to reduce route requests obstruction number.
System the most according to claim 4, it is characterised in that described energy efficiency priority pathfinding module, including:
Route requests order module, for asking size to be source node and destination node in intelligence adapted electric network according to portfolio
Between route requests sequence；
Available path finds module, and on the premise of meeting QoS constraint, utilizing discrete particle cluster algorithm is respectively to route
Available path as much as possible is found in request；
Path searching module, meets this route requests correspondence portfolio for lookup from available path and asks and make intelligence to be joined
Power utilization network energy consumption increases minimum path；
Path judge module, for obtaining energy efficiency priority pathfinding result according to the number of paths found:
If the path found is one, then the energy efficiency priority pathfinding result of the i.e. route requests of this paths, according to route requests
Energy efficiency priority pathfinding result route, update intelligence adapted electric network link load matrix；
If the path found is more than one, then optimize network delay, i.e. select total jumping figure minimum from the mulitpath found
Path as the energy efficiency priority pathfinding result of route requests, route according to the energy efficiency priority pathfinding result of route requests,
Update the link load matrix of intelligence adapted electric network；
If the path found is zero, then energy efficiency priority pathfinding failure, carries out heavy-route.
System the most according to claim 4, it is characterised in that described heavy-route module, including:
Alternative path generation module, is used for using many groups of Depth Priority Algorithm stochastic generation to jump less than single route maximum
The source node of number is to the alternative path of destination node；
Alternative path judge module, is used for judging that whether alternative path is less than link optimum rate of utilization threshold value: be that then calculating should
The fitness function value that alternative path is corresponding, otherwise, is set to infinity by the fitness value of this alternative path；
Path selection module, for selecting the path of fitness value minimum from alternative path；
According to fitness value, heavy-route result judge module, for judging that heavy-route is the most successful: if this fitness value is infinite
Greatly, then heavy-route failure, this route requests stop, route requests block number add one, return to intelligence adapted electric network under
One route requests between source node and destination node carries out energy efficiency priority pathfinding；Otherwise heavy-route success, utilizes this path more
The link load matrix of new intelligence adapted electric network, returns between source node next in intelligence adapted electric network and destination node
Route requests carry out energy efficiency priority pathfinding.
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