CN111404748A - Network resource configuration method based on time coloring Petri net - Google Patents
Network resource configuration method based on time coloring Petri net Download PDFInfo
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- 239000003086 colorant Substances 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 9
- 230000007721 medicinal effect Effects 0.000 description 7
- 238000013468 resource allocation Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000001356 surgical procedure Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 239000013256 coordination polymer Substances 0.000 description 2
- 101100394003 Butyrivibrio fibrisolvens end1 gene Proteins 0.000 description 1
- 101150110503 END3 gene Proteins 0.000 description 1
- 101100174722 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) GAA1 gene Proteins 0.000 description 1
- 101100296979 Saccharomyces cerevisiae (strain ATCC 204508 / S288c) PEP5 gene Proteins 0.000 description 1
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- 238000000968 medical method and process Methods 0.000 description 1
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/14—Network analysis or design
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L47/00—Traffic control in data switching networks
- H04L47/70—Admission control; Resource allocation
- H04L47/78—Architectures of resource allocation
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Abstract
A network resource configuration method based on a time coloring Petri net comprises the following steps: s100: modeling a network to be subjected to resource configuration in a blocking manner through a time coloring petri net; s200: integrating the partitioned models to obtain a final TCPN model; s300: and configuring network resources by adopting the final TCPN model. The method can dynamically simulate the whole distribution process, clearly observe the trend of the resources and the condition of the utilization rate, and can better perfect the whole resource scheduling distribution process by combining the time factor.
Description
Technical Field
The disclosure belongs to the technical field of computer network communication, and particularly relates to a network resource configuration method based on a time coloring Petri network.
Background
With the continuous development of network technology, the internet has become an important infrastructure highly related to national economy and social development, and has a profound influence on improving social productivity, promoting economic society upgrading transformation, creating new economic growth points and employment opportunities and the like.
The existing network resource allocation method needs a great deal of time, resources and labor force. Therefore, it is necessary to coordinate the resources of the parties in the shortest amount of time.
Concepts such as resources, libraries, transitions and the like in a Petri network (Petri Net, PN) can better describe various resources, positions, behaviors and dynamic cooperation relations of the resources, the positions, the behaviors and the dynamic cooperation relations, and can be better used for common phenomena such as synchronization, concurrency, distribution, conflict, resource sharing and the like in a complex system. Thus, the Petri Net model is introduced into the network resource configuration.
Disclosure of Invention
In view of this, the present disclosure provides a network resource configuration method based on a time coloring Petri net, including the following steps:
s100: modeling a network to be subjected to resource configuration in a blocking manner through a time coloring petri net;
s200: integrating the partitioned models to obtain a final TCPN model;
s300: and configuring network resources by adopting the final TCPN model.
By the technical scheme, the method can dynamically simulate the whole distribution process, clearly observe the trend of the resources and the condition of the utilization rate, and can better perfect the whole resource scheduling distribution process by combining the time factor.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a method for configuring network resources based on a time-colored Petri net according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a correspondence between medical activities and medical resources in one embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a TCPN model for classification of medical activity in one embodiment of the present disclosure;
FIG. 4 is a schematic view of a TCPN model illustrating the correspondence between a doctor and a medical activity in one embodiment of the present disclosure;
FIG. 5 is a schematic view of a TCPN model for correspondence between nurses and medical activities in one embodiment of the present disclosure;
FIG. 6 is a schematic view of a TCPN model for correspondence between an ambulance and a medical activity in one embodiment of the present disclosure;
FIG. 7 is a schematic view of a TCPN model for resource allocation throughout a medical procedure in one embodiment of the present disclosure;
FIG. 8 is a schematic diagram of an end of activity state after setting an initial configuration in one embodiment of the present disclosure.
Detailed Description
In one embodiment, referring to fig. 1, a network resource configuration method based on a time coloring Petri net is disclosed, which includes the following steps:
s100: modeling a network to be subjected to resource configuration in a blocking manner through a time coloring petri net;
s200: integrating the partitioned models to obtain a final TCPN model;
s300: and configuring network resources by adopting the final TCPN model.
For the embodiment, the method combines the relevant definition and the triggering rule of the TCPN, and then provides a method for constructing a resource allocation model in the medical process on the basis of the TCPN. And then, dynamically simulating the constructed model, and analyzing and verifying the whole scheduling process. And finally, obtaining an optimal resource allocation strategy model through continuous optimization.
The method for constructing the resource configuration model comprises the steps of firstly establishing an integral activity classification, and then constructing an integral scheduling model according to a configuration principle between resources and activities.
The method can dynamically simulate the whole distribution process, clearly observe the trend of the resources and the condition of the utilization rate, and can better perfect the whole resource scheduling distribution process by combining the time factor.
In another embodiment, the method of claim 1, wherein the time-coloring Petri nets in the step S100 is specifically defined as a time-coloring Petri net being a nine-tuple TCPN ═ (P, T, a, ∑, V, C, G, E, I), wherein:
1) the user is a finite set of libraries, represented by circles;
2) t is a finite set of transitions T, such that
Represented by a rectangle;
3)
is a set of directed arcs, represented by directed arcs;
4) ∑ is a limited set of non-empty colors;
5) v is a finite set of Type variables, such that Type [ V ] ∈∑ is for all variables V ∈ V, where the color set is time-stamped;
6) p → ∑ is a function of the set of colors specified for each library;
7)G：T→EXPRVis a predicate function that specifies a Boolean expression for each transition t, such that Type [ G (t)]＝BooI；
8)E：A→EXPRVIs an arc expression function that assigns an arc expression to each arc a, such that Type [ E (a)]＝C(p)MSWhere p is the library connected to arc a, MS represents the multiple set;
9)
is an identification initialization function, let Type [ I (p)]＝C(p)MSWherein MS means a superset.
For the purposes of this embodiment, the formalized representation of the CPN is the basis for defining various behavioral attributes and analytical methods.
In another embodiment, the step of modeling the block in the step S100 further includes a step of giving a data type definition of the model.
For this embodiment, the color set is defined as a data type, corresponding to the actual network transaction system and the variables or constants used to model the process.
In another embodiment, the data type definition is in the CP L M L language established based on the functional programming language standard M L.
For this embodiment, the CP L M L (Community Party of New pal Marxist-L eniist) language was built based on the functional programming language standard M L (SM L), which was extended by defining the construction of color sets and declarative variables.
In another embodiment, a time delay function is added to the activity transition in the blocking modeling in the step S100.
For this embodiment, the addition of a time delay function may ensure that activity occurs within a time interval.
In another embodiment, the integration in step S200 is specifically:
by merging the common location transitions or libraries of the individual models that are blocked.
In another embodiment, fig. 2 shows the allocation mechanism between resources and activities in a medical procedure, and we can see that one operation requires one doctor and five nurses, one emergency requires one doctor, and one emergency requires one ambulance, one nurse and one doctor. Such a configuration scheme is abstracted in connection with real-life cases. In our invention, the doctor can assume two different roles, one being the primary doctor and the other being the outpatient doctor, while the nurse has only one role and can participate in a wide variety of activities.
Fig. 3 is a medical activity assignment model constructed using a time-colored Petri net (TCPN). The following are data elements that define some TCPN for the activities in fig. 3:
closet NO＝int timed;
closet DATA＝string timed；
closet NOxDATA＝product NO*DATA timed；
var n：NO;
var d：DATA；
wherein 2' (1) ("surgery") @0 represents that two operations are waiting in the system, the type number is 1, and the timestamp is 0. In fig. 3, there are a total of 11 activity sequences, the occurrence of which is random. Then, type classification is performed by transition T1, and when n is 1, it is transferred to the "surgery" place, when n is 2, it is transferred to the "first aid" place, and when n is 3, it is transferred to the "emergency" place.
In another embodiment, fig. 4-6 are TCPN models of correspondence between doctors, nurses, and ambulances and medical activities. In these models, we add a time delay function to the activity transition to ensure that the activity occurs within a time interval, and the data is defined as follows:
var n1，n2，n3：NO；
fun Delayl()＝discrete(20，30)；
fun Delay2()＝discrete(10，15)；
fun Delay3()＝discrete(5，10)；
in fig. 4, when the doctor numbered 1, that is, n1 is 1, is required to perform the operation, the transition "surgery" is triggered, and the trigger duration is required to be between (20, 30). The emergency treatment only needs to be carried out until the number of the doctors is more than 0, namely, all the doctors can carry out emergency treatment work, and the duration of the emergency treatment is between (10, 15). The emergency call requires number 2, i.e. n3 ═ 2, and the duration is between (5, 10). The principle of the model of fig. 5 and 6 is the same as that of fig. 4.
In another embodiment, FIG. 7 is a TCPN model of our overall medical procedure resource allocation. It is obtained by merging the common location transitions or libraries of fig. 3-6. In this model, the initial identifications are:
M0(cloud platform)＝2`(1，“surgery”)@0++3`(2，“first aid”)@0++6`(3，“emergency”)@0；
M0(ambulance)＝5`1@0；M0(nurse)＝10`2@0；
M0(surgeon)＝3`1@0++2`2@0；
fig. 8 is an activity end state M after the initial configuration is set for fig. 7. And the number of tokens in the model does not decrease or increase before and after occurrence, i.e., | M0 (closed platform) | M (END1) | + | M (END2) | + | M (END3) |; | M0(ambulance) | ═ M (ambulance) |; | M0(nurse) | ═ M (nurse) |; | M0 (subgeon) | M0 (subgeon) |. This indicates that the model is terminable and bounded.
In another embodiment, an analysis is performed on the model results. The utilization rate of the resource is analyzed firstly, and for the resource of 'ambulance', the utilization rate is 60%, and the total number of tokens is five, three participate in the activity, and the other two do not participate. The utilization of the other two "nurse" and "sureon" is 130% and 160%, respectively. Through analysis, the resource utilization rate of the system is relatively high.
The second is the temporal analysis of the activity being performed. The generation process of the model is concurrent and random, and is very consistent with the occurrence situation of activities in real life. In FIG. 8, the system has reached its final state, in repository locations "ambulance", "nurse" and "purgeon", the time stamps indicate the total time that the resource has been executing throughout the medical procedure, and in the three termination repository locations "END 1", "END 2" and "END 3", the total time that an activity has started to ended. His average load and the efficiency with which the activity occurs can be derived by calculating the ratio of the time each resource performs in each activity to the total time for that resource, as shown in table 1.
TABLE 1
From this table 1, it can be seen that the utilization rate of each resource is the same as that analyzed before, and from fig. 8, it can be seen that the execution time difference of each activity is not very large and is within the corresponding time difference. Therefore, the final TCPN model is proved to be useful, the dynamic scheduling of resources can be well realized, and the waiting time of tasks is reduced.
Although the embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments and application fields, and the above-described embodiments are illustrative, instructive, and not restrictive. Those skilled in the art, having the benefit of this disclosure, may effect numerous modifications thereto without departing from the scope of the invention as defined by the appended claims.
Claims (6)
1. A network resource configuration method based on a time coloring Petri net comprises the following steps:
s100: modeling a network to be subjected to resource configuration in a blocking manner through a time coloring petri net;
s200: integrating the partitioned models to obtain a final TCPN model;
s300: and configuring network resources by adopting the final TCPN model.
2. The method according to claim 1, wherein the time-coloring Petri nets in the step S100 is specifically defined as a time-coloring Petri net being a nine-tuple TCPN (P, T, a, ∑, V, C, G, E, I), wherein preferably:
1) p is a finite set of libraries, represented by circles;
2) t is a finite set of transitions T, such that
Represented by a rectangle;
3)
is a set of directed arcs, represented by directed arcs;
4) ∑ is a limited set of non-empty colors;
5) v is a finite set of Type variables, such that Type [ V ] ∈∑ is for all variables V ∈ V, where the color set is time-stamped;
6) p → ∑ is a function of the set of colors specified for each library;
7)G：T→EXPRVis a predicate function that specifies a Boolean expression for each transition t, such that Type [ G (t)]＝Bool；
8)E：A→EXPRVIs an arc expression function that assigns an arc expression to each arc a, such that Type [ E (a)]＝C(p)MSWhere p is the library connected to arc a, MS represents the multiple set;
9)I：
is an identification initialization function, let Type [ I (p)]＝C(p)MSWherein MS means a superset.
3. The method of claim 2, wherein the step of S100 further comprises the step of giving a data type definition of the model before the modeling of the blocking.
4. The method of claim 3 wherein the data type definition is a CPI M L language based on a functional programming language standard M L.
5. The method of claim 2, wherein a time delay function is added to the activity transition in the blocking modeling in the step S100.
6. The method according to claim 2, wherein the step S200 is integrated by:
merging through the common position transition or the library of the blocked models.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289205A (en) * | 2011-09-09 | 2011-12-21 | 河海大学常州校区 | Modelling method for reconfigurable assembly system on basis of Agent timed colored Petri net |
| US20170316129A1 (en) * | 2016-04-28 | 2017-11-02 | Schneider Electric Industries Sas | Method for generating a petri net simulation model of an industrial control system |
| CN110737901A (en) * | 2019-10-11 | 2020-01-31 | 陕西师范大学 | Logic vulnerability analysis method for network transaction service interaction process in design stage |
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Patent Citations (3)
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|---|---|---|---|---|
| CN102289205A (en) * | 2011-09-09 | 2011-12-21 | 河海大学常州校区 | Modelling method for reconfigurable assembly system on basis of Agent timed colored Petri net |
| US20170316129A1 (en) * | 2016-04-28 | 2017-11-02 | Schneider Electric Industries Sas | Method for generating a petri net simulation model of an industrial control system |
| CN110737901A (en) * | 2019-10-11 | 2020-01-31 | 陕西师范大学 | Logic vulnerability analysis method for network transaction service interaction process in design stage |
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| OLATUNDE T. BARUWA等: "Deadlock-Free Scheduling Method for Flexible Manufacturing Systems Based on Timed Colored Petri Nets and Anytime Heuristic Search", 《IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS》 * |
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Application publication date: 20200710 |