CN101360319B - Resource reservation method and apparatus based on traffic - Google Patents
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Abstract
The invention discloses a resource reserving method based on service volume, which includes following steps: establishes at least one set according to the prior D day`s service volume before the nth time span of the (D+1)th day to be forecasted and the prior (n-1)th time span`s service volume of the (D+1)th day; and then divides all the service volume in each set into at least one category; then forecasts the resource needed in the nth time span of the (D+1)th day to be forecasted according to the category; and finally, reserves the resource according to the forecasted result. The invention also discloses a resource reserving device based on service volume, which includes a construction module, a classification module and a forecast module. Through the method and the device, the invention can realize reasonable resource reservation, therefore can reduce the session switch dropping rate and farthest improve the throughput of the system.
Description
Technical Field
The present invention relates to the field of communications, and in particular, to a resource reservation technique in the field of communications, and in particular, to a method and an apparatus for resource reservation based on traffic.
Background
The resource reservation method has been proposed for a long time and is the most widely used access control scheme. In an early single-service network environment, i.e. a network environment in which only one service is transmitted in the network, the system needs to reserve certain resources for the handover session to ensure that the handover session drop rate of the system is lower than a certain level.
In a conventional resource reservation policy, the number of resource reservations is determined before the system starts operating and does not change with fluctuations in network traffic, and thus such a policy is also referred to as a static resource reservation policy, which may be referred to as a static policy for short. Although static policies are easy to implement, with the development of mobile services, it is difficult to efficiently allocate increasingly tight air interface resources, and the quality of service (QoS) requirements of various services cannot be met. Therefore, a dynamic resource reservation policy in which the number of resource reservations can dynamically change with a network environment is considered as one of important technologies in wireless resource management of a future wireless communication system, and various access control schemes based on dynamic resource reservation are also successively proposed.
The currently proposed dynamic resource reservation strategies can be roughly divided into two categories: cooperative and non-cooperative strategies. The cooperative policy may also be referred to as a distributed policy, and the non-cooperative policy may also be referred to as a local policy.
The basic idea of the cooperation strategy is: when the ongoing service in the local cell may move to a cell adjacent to the local cell or other cells on the user moving route, in order to avoid the interruption of the user's call, the cells in the system may exchange the service information (such as the service load condition, the user's mobility information, etc.) of the cells to determine the number of resource reservations, so as to implement the optimal access control.
The collaborative policies can be divided into two categories again: interactive strategies and predictive strategies. The interactive strategy dynamically adjusts the number of resource reservations mainly according to the recent access control results (such as the change conditions of the call drop rate and the blocking rate). Early dynamic resource reservation techniques mostly belong to interactive strategies. The predictive strategy is mainly to calculate and adjust the number of resource reservations in advance by assuming a service model or applying a predictive model so as to meet the QoS requirements of the service. However, in the existing predictive strategies, the assumed model is usually provided only for the convenience of mathematical analysis, and has a large gap with the application scenario in the actual system, so that the effect of the predictive strategies in the actual application is not ideal.
Disclosure of Invention
In view of this, the embodiments of the present invention provide a method and an apparatus for reserving resources based on traffic volume, so that a system can reserve reasonable resources for a new service.
In order to achieve the above purpose, the technical solution in the embodiment of the present invention is realized as follows:
a method for traffic-based resource reservation, the method comprising the steps of:
A. constructing at least one set according to the traffic volume of the previous D days before the nth time period of the D +1 th day to be predicted; d and n are natural numbers;
B. dividing all the traffic in each set into at least one class according to a clustering algorithm;
C. according to the class, predicting the class to which the traffic of the nth time period of the D +1 th day to be predicted belongs; predicting resources required by the nth time period of the D +1 th day to be predicted according to the predicted class;
D. and reserving resources according to the prediction result.
An embodiment of the present invention further provides a device for resource reservation based on traffic, where the device includes: the system comprises a construction module, a classification module, a prediction module and a resource reservation module;
the construction module is used for constructing a set according to the received traffic information and sending the constructed set to the classification module;
the classification module is used for classifying the traffic in the received set according to a clustering algorithm and sending the set and the classification result to the prediction module;
the prediction module is used for predicting the class to which the traffic of the time period to be predicted belongs according to the received set and the classification result; predicting the resources of the time period to be predicted according to the predicted class, and sending the prediction result to the resource reservation module;
and the resource reservation module reserves resources according to the received prediction result and outputs the information of the reserved resources.
In summary, the embodiments of the present invention provide a method and an apparatus for reserving resources based on traffic volume, where the method may determine the number of reserved resources in the nth time period of day D +1 according to the traffic volume of the previous day D, so as to reduce the call drop rate of the handover session through reasonable resource reservation and improve the system throughput to the maximum extent.
The resource reservation method based on the traffic provided by the embodiment of the invention does not depend on a specific service mathematical model, and is easy for practical engineering application; the mode classification calculation involved in the method can be carried out when the system is idle, such as in the morning, and the calculation resources of the system can be fully utilized; in addition, because the method also uses a clustering algorithm with a self-learning function, the method has the self-learning function and can track the slow change of the business occurrence mode.
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Fig. 1 is a flowchart of a traffic-based resource reservation method in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a traffic-based resource reservation apparatus in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a resource reservation method and a device based on traffic, and the method can determine the reserved resource quantity of the nth time period of the D +1 day according to the traffic of the previous D days. The affiliated traffic comprises data information such as data flow, channel utilization rate or service load and the like; the resources include various physical or non-physical resources such as bandwidth resources, channel resources, code channel resources in a wired system, or air interface resources, time slot resources in a wireless system.
Fig. 1 is a flowchart of a traffic-based resource reservation method in an embodiment of the present invention. As shown in fig. 1, the resource reservation method in the embodiment of the present invention includes the following steps:
Specifically, 24 hours of each day can be divided into N equal-length time segments Δ t equal to 24/N, and the traffic generated by the system in the jth time segment on the ith day can be denoted as xi，j(i 1, 2.., D + 1; j 1, 2.., N), where D is a known number of days that traffic has occurred. Knowing all traffic before the nth time period on day D +1, all traffic x on day D before may be consideredi，jDividing the data into N sets, and dividing the traffic volume of the time period of the same time every day, namely all the traffic volume x of the previous D daysi，jSubscript j in (1) is the same as traffic xi，jA set is formed. For example, when j is 5, the set Y5 can be obtained as { x ═ xi，5}＝{x1，5，x2，5，x3，5，...，xD，5D elements (i.e., traffic) in total. By analogy, N sets can be obtained, denoted as:
Yj＝{xi，j|1≤i≤D}，j＝1，2，...，N
wherein, D, N and N are natural numbers.
In particular, each set Y may be individually grouped using some clustering algorithm, such as k-means clustering algorithm, etcjAll elements x ini，jIs divided into CjClass II with Zj，cSet of representations YjInner CjA class C of the classes, wherein j is 1, 2j. For example, a set Y consisting of traffic occurring during the 3 rd time period of the day may be used3All elements x ini，3Is divided into C3A class I, wherein Z3，1Set of representations Y3Inner C3The first in a class1 class.
And 103, predicting the reserved resources required by the time period to be predicted.
After the classification in step 102, the traffic x to be predicted in the nth time period of day D +1D+1，nBelong to the set YnInner CnClass c of classes Zn，cProbability p ofn，cThe calculation can be made using the following formula:
wherein, | Zn，cI represents the above CnClass c of classes Zn，cThe number of elements contained in (a).
In addition, k nearest neighbor algorithm in clustering algorithm can be usedCalculating the above class Zn，cOf the distribution of the individual elements (i.e. traffic volumes) of (a) a probability density function fn，c(x)，c＝1，2，...，Cn. Wherein x is the set YnIs divided into class Zn，cElement (b), i.e., traffic; probability density function fn，c(x) Representing traffic x in set YnClass c ofn，cThe probability of occurrence of (c).
Since there is a correlation between the traffic at the current time and the traffic at the next time, the learning of the traffic characteristics can be performed by constructing a time-interval delay set to establish the correlation. Therefore, for each set Zn，cA corresponding set of time delays H may be constructedn，c. Definition element xi，nThe time period delay operator T is as follows: txi，n＝xi，n-1Then H isn，c＝{Txi，n|xi，n∈Zn，c}＝Zn-1，cThat is, Hn，cThe elements in (A) are composed of all the elements belonging to Zn，cThe element of (b) corresponds to the element of the previous period. Wherein, the time delay operator T is a function of n and changes along with the change of n; further, when n is 1, xi，n-1＝xi，0Representing the traffic volume for the last time period of day i-1.
Estimating H using k-nearest neighbor algorithmn，cCan obtain Hn，cProbability density function g of medium element distributionn，c(x)，c＝1，2，...，Cn. And Zn，cProbability density function fn，c(x) Similarly, gn，c(x) Indicating that traffic x is in set Hn，cThe probability of occurrence of (c).
Thus, the amount of traffic x that may occur during the nth time period on day D +1 may be consideredD+1，nPredicting the class to which it is likely to belong, i.e. determining the traffic xD+1，nTo which class Z it is possible to belongn，c. In making the above prediction, different criteria, such as a minimum error probability criterion, may be used, so that:
wherein x isD+1，n-1Representing the amount of traffic occurring at the (n-1) th time period on day D +1, <math><mrow> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>≤</mo> <mi>c</mi> <mo>≤</mo> <msub> <mi>C</mi> <mi>n</mi> </msub> </mrow> </munder> <mrow> <mo>{</mo> <msub> <mi>p</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <msub> <mi>g</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>D</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </mrow></math> represents from CnClass is selected such that pn，cgn，c(xD+1，n-1) The largest value of (a), arg is an operator, indicating that the condition is satisfied <math><mrow> <munder> <mi>max</mi> <mrow> <mn>1</mn> <mo>≤</mo> <mi>c</mi> <mo>≤</mo> <msub> <mi>C</mi> <mi>n</mi> </msub> </mrow> </munder> <mrow> <mo>{</mo> <msub> <mi>p</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <msub> <mi>g</mi> <mrow> <mi>n</mi> <mo>,</mo> <mi>c</mi> </mrow> </msub> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>D</mi> <mo>+</mo> <mn>1</mn> <mo>,</mo> <mi>n</mi> <mo>-</mo> <mn>1</mn> </mrow> </msub> <mo>)</mo> </mrow> <mo>}</mo> </mrow> </mrow></math> The set of class numbers c. Since the condition is to take the largest number c, there is only one class number c in the set, and the value of this c can be assigned to the variable to the left of the equal sign by the equal signTherefore, according to the above formula, the traffic xD+1，nBelong to the class
Knowing the traffic xD+1，nThe class is
Then, the probability density function in the class can be determinedAnd the maximum allowable probability r of a dropped session occurringdropObtaining the predicted quantity of the resources required by the nth time period on the D +1 th day
The value of this predictor can be calculated by:
wherein,
represents: selecting a minimum value a such that the value a satisfies the condition:
therefore, the value a is such that the probability of the occurrence of the session drop is lower than the maximum allowable probability r of the occurrence of the session dropdropMinimum traffic volume of (c); further, arg is an operation symbol indicating that a condition is satisfied
The set of a of (a). Since the above condition is the minimum value a, there is only one a in the set, and the value of a can be assigned to the predicted amount of resources required for the nth time period on day D +1 to the left of the equal sign by the equal sign
And step 104, reserving resources according to the prediction result. I.e. based on the above-mentioned pre-measured quantitiesThe number of required resources, and the system reserves the corresponding amount of resources;
the above-described method of resource reservation may be directed to a service having a high priority, such as a handover service. That is, the above-mentioned method for reserving resources can be used to reserve a corresponding amount of resources for the high-priority service. After the resource reservation is completed, if a new service needs to be accessed to the system, firstly, judging whether the new service is a high-priority service, if so, accessing the new service as long as enough resources in the system can meet the requirement of the new service, and otherwise, refusing to access the new service; if the new service is a low-priority service, the new service is accessed if enough resources in the system can meet the requirement of the new service except the reserved resources, otherwise, the new service is refused to be accessed.
Of course, the above method for resource reservation can also be used for services with non-high priority, that is, the above method for resource reservation can also be used to reserve a corresponding amount of resources for services with non-high priority.
Fig. 2 is a schematic diagram of a traffic-based resource reservation apparatus in an embodiment of the present invention. As shown in fig. 2, the resource reservation apparatus 200 in the embodiment of the present invention includes: a construction module 201, a classification module 202, a prediction module 203 and a resource reservation module 204. The constructing module 201 is connected with the classifying module 202, and is configured to receive externally input traffic information, construct a set according to the received traffic information, and send the constructed set to the classifying module 202; the classification module 202 is connected to the construction modules 201 and 203, respectively, and is configured to receive the sets sent by the construction modules, classify elements (i.e., traffic) in the received sets, and send the sets and classification results to the prediction module 203; the prediction module 203 is connected to the classification module 202 and the resource reservation module 204, and is configured to calculate and predict resources of a time period to be predicted according to the received set and the classification result, and send the prediction result to the resource reservation module 204; and the resource reservation module 204 is configured to reserve resources according to the received prediction result, output information of the reserved resources, and complete resource reservation according to the information of the reserved resources by the system.
Wherein, the above-mentioned construction module 201 further comprises: a time period partitioning module 205 and a set construction module 206. The time period dividing module 205 is configured to divide 24 hours of each day into N time periods with equal lengths, and send the time period dividing result to the set constructing module 206; the set constructing module 206 is configured to construct a set according to the received time period division result and the traffic information, and send the constructed set to the classifying module 202.
The aforementioned classification module 202 further includes: a clustering module 207 and a delay module 208. The clustering module 207 is configured to divide all traffic in each received set into at least one class according to a clustering method, and send a classification result to the time delay module 208 and the prediction module 203; the time delay module 208 is configured to perform the same classification on the set to which the time period before the time period to be predicted belongs according to the received classification result, and send the classification result to the prediction module 203.
The prediction module 203 further comprises: a calculation module 209, a class prediction module 210, and a resource prediction module 211. The calculating module 209 is configured to calculate, according to the received classification result, a first probability that traffic to be generated in the time period to be predicted belongs to each class in the set to which the time period to be predicted belongs, and a second probability that each traffic appears in each class in the set corresponding to a time period before the time period to be predicted; sending the first probability and the second probability to the class prediction module 210; the class prediction module 210 is configured to predict a class to which the traffic of the time period to be predicted belongs according to the received first probability and the second probability; sending the prediction result to the resource prediction module 211; the resource predicting module 211 is configured to predict the resource to be reserved according to the received prediction result, and send the predicted resource to be reserved to the resource reserving module 204.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for traffic based resource reservation, characterized in that the method comprises the steps of:
A. constructing at least one set according to the traffic volume of the previous D days before the nth time period of the D +1 th day to be predicted; d and n are natural numbers;
B. dividing all the traffic in each set into at least one class according to a clustering algorithm;
C. according to the class, predicting the class to which the traffic of the nth time period of the D +1 th day to be predicted belongs; predicting resources required by the nth time period of the D +1 th day to be predicted according to the predicted class;
D. and reserving resources according to the prediction result.
2. The method of claim 1, wherein step a comprises:
a1, dividing each day into N time periods with equal length, wherein N is a natural number;
a2, traffic x of D days before the nth time period of D +1 day to be predictedi，jDividing into N sets, where i 1, 2., D, j 1, 2., N; all the above traffic xi，jAnd in the middle, the same time period traffic with the same subscript j is used as the same set.
3. The method of claim 1, wherein step B comprises:
dividing all the traffic in each set into at least one class according to a clustering method; and (3) carrying out the same classification on the set to which the (n-1) th time period belongs and the set to which the nth time period belongs according to a clustering method.
4. The method of claim 1, wherein the predicting the class to which the traffic volume of the nth time period on day D +1 to be predicted belongs comprises:
c11, calculating a first probability that the traffic volume to be predicted in the nth time period on the D +1 th day belongs to each class in the set to which the nth time period belongs;
c12, calculating a second probability of each traffic volume in each class in the set to which the (n-1) th time period belongs;
and C13, predicting the class to which the traffic of the nth time period of day D +1 to be predicted belongs according to the first probability and the second probability.
5. The method according to claim 4, wherein the resource required to be reserved for predicting the nth time period of day D +1 to be predicted in step C is:
satisfies the conditions
A minimum traffic volume of; wherein r isdropThe maximum allowable probability of a dropped session;
represents a class to which traffic of the nth time period of day D +1 to be predicted belongs
A probability density function of the respective traffic volumes in (a).
6. The method of claim 4, wherein step C13 comprises:
c131, calculating the product of the two probabilities according to the first probability and the second probability;
and C132, taking the class corresponding to the maximum value of the product calculated in the step C131 as the class to which the traffic of the nth time period on the D +1 th day to be predicted belongs.
7. An apparatus for traffic-based resource reservation, the apparatus comprising: the system comprises a construction module, a classification module, a prediction module and a resource reservation module;
the construction module is used for constructing a set according to the received traffic information and sending the constructed set to the classification module;
the classification module is used for classifying the traffic in the received set according to a clustering algorithm and sending the set and the classification result to the prediction module;
the prediction module is used for predicting the class to which the traffic of the time period to be predicted belongs according to the received set and the classification result; predicting the resources of the time period to be predicted according to the predicted class, and sending the prediction result to the resource reservation module;
and the resource reservation module reserves resources according to the received prediction result and outputs the information of the reserved resources.
8. The apparatus of claim 7, wherein the configuration module comprises: the device comprises a time period dividing module and a set constructing module;
the time period dividing module is used for dividing each day into N time periods with equal length and sending the time period dividing result to the set constructing module;
the set constructing module is used for constructing a set according to the received time period division result and the traffic information and sending the constructed set to the classifying module.
9. The apparatus of claim 7, wherein the classification module comprises: a clustering module and a time delay module;
the clustering module is used for dividing all the received traffic in each set into at least one class according to a clustering method and sending a classification result to the time delay module and the prediction module;
and the time delay module is used for classifying the set of the time period to be predicted to which the previous time period belongs according to the received classification result, and sending the classification result to the prediction module.
10. The apparatus of claim 7, wherein the prediction module comprises: the system comprises a calculation module, a class prediction module and a resource prediction module;
the calculation module is used for calculating a first probability that the traffic volume to be generated in the time period to be predicted belongs to each class in the set to which the time period to be predicted belongs and a second probability that each traffic volume appears in each class in the set corresponding to the time period before the time period to be predicted according to the received classification result; sending the first probability and the second probability to the class prediction module;
the class prediction module is used for predicting the class to which the traffic of the time period to be predicted belongs according to the received first probability and the second probability; sending the prediction result to a resource prediction module;
and the resource prediction module is used for predicting the resources required to be reserved according to the received prediction result and sending the predicted resources required to be reserved to the resource reservation module.
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| CN105472631A (en) * | 2014-09-02 | 2016-04-06 | 中兴通讯股份有限公司 | Service data quantity and/or resource data quantity prediction method and prediction system |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6365577B1 (en) * | 1998-10-26 | 2002-04-02 | Avi Biopharma, Inc. | p53 antisense agent and method |
| CN1450753A (en) * | 2002-04-11 | 2003-10-22 | 三星电子株式会社 | Data communication method using resouce reservation |
| CN1678120A (en) * | 2005-05-10 | 2005-10-05 | 北京交通大学 | Resource reservation intelligent call admission control method and apparatus |
| WO2006127328A1 (en) * | 2005-05-24 | 2006-11-30 | Cisco Technology, Inc. | System and method for providing bandwidth reservations in a resource reservation setup protocol, rsvp, environment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6365577B1 (en) * | 1998-10-26 | 2002-04-02 | Avi Biopharma, Inc. | p53 antisense agent and method |
| CN1450753A (en) * | 2002-04-11 | 2003-10-22 | 三星电子株式会社 | Data communication method using resouce reservation |
| CN1678120A (en) * | 2005-05-10 | 2005-10-05 | 北京交通大学 | Resource reservation intelligent call admission control method and apparatus |
| WO2006127328A1 (en) * | 2005-05-24 | 2006-11-30 | Cisco Technology, Inc. | System and method for providing bandwidth reservations in a resource reservation setup protocol, rsvp, environment |
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