CN114826937A - Flow sensing method based on size time scale under heaven-earth fusion network - Google Patents
Flow sensing method based on size time scale under heaven-earth fusion network Download PDFInfo
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Abstract
The invention discloses a flow sensing method based on size and time scale under a world fusion network, which aims at the world fusion network environment and provides a world fusion architecture which can sense network resource nodes in a collection region and process flow information, can adapt to the characteristics of the world fusion network to predict the regional flow under the large time scale and can judge whether the resources in the region can meet the resource requirement corresponding to the predicted flow. By means of delay-sensitive cross-node traffic transfer on a small time scale, resource node overload conditions caused by traffic burst on the small time scale can be reduced, and resource utilization rate of idle nodes can be improved by performing queue caching and traffic transfer on delay-tolerant traffic.
Description
Technical Field
The invention belongs to the field of world fusion information networks, and particularly relates to a flow sensing method based on a size time scale under a world fusion network.
Background
In recent years, as a new network architecture fusing a satellite and a ground communication system, a world-ground converged network has gained wide attention from academic circles and industrial circles, and the world-ground converged network is related to national economy and national security development strategies and is an important component of national competitive strength and viability. Owing to the advantages of wide coverage range, high throughput, strong robustness and the like, the world-space fusion network can be applied to a plurality of practical fields of earth observation and mapping, intelligent traffic systems, military missions, homeland security, disaster rescue and the like. Satellites with high throughput are capable of providing seamless wireless access services globally, and the densely deployed terrestrial network infrastructure supports high-speed data access. The convergence of space-based and ground-based networks can bring immeasurable benefits to the future 6G wireless communication system, and provide more applications and services.
For the world-wide converged network, due to the characteristics of self heterogeneity, self-organization, dynamics and the like, the converged network also faces a plurality of challenges such as routing, resource allocation, power control, end-to-end service quality requirements and the like while bringing significant benefits for various services and applications. The fusion of the space-based network and the foundation network can increase the types and the number of services, the conventional traffic perception is carried out on a large time scale, for example, tens of minutes, the network traffic in the area is predicted and perceived under the large time scale, and the scheduling of resources is carried out through a prediction result to meet the resource requirement corresponding to the traffic.
At present, the research on the heaven and earth fusion network flow perception is basically carried out under the condition of large time scale, the flow change under the condition of small time scale is rarely considered, the traditional flow prediction algorithm only aims at a single network, either a satellite network or a ground network, and the research on the heaven and earth fusion network flow prediction is less. General traffic prediction includes two types, namely linear and nonlinear, wherein linear data are fitted by manually setting parameters according to experience, the application range is small, and actual network traffic has the characteristics of nonlinearity, periodicity, burstiness, self-similarity and the like, so that the application range of nonlinear traffic prediction is large.
The traditional flow sensing processing only aims at the condition of large time scale, the flow change of small time scale is rarely considered, and the flow change is generally difficult to predict, so the flow change under the small time scale is often ignored, the sudden change of the flow can cause different results of nodes at different times, the sudden change of the flow can overload to cause congestion when the sudden change of the flow is at the peak value, and then the nodes generate larger service delay, the service quality of the nodes is reduced, or the sudden change of the flow is at the valley bottom, and the nodes are idle to cause waste of node resources. Therefore, how to solve the sudden traffic change in the small time scale is very important for node traffic balance and reasonable resource utilization in the region.
Disclosure of Invention
The invention aims to overcome the defects and provide a flow sensing method based on large and small time scales under a world fusion network, and provides a Markov-based flow prediction method under the large time scale aiming at the characteristics of multidimensional resources, resource dynamics and small time scale flow burstiness of the world fusion network.
In order to achieve the above object, the present invention comprises the steps of:
s1, modeling the heaven and earth fusion network to obtain a satellite ground node resource pool;
s2, predicting arrival of the regional flow rate;
s3, judging whether the satellite ground resources meet the resource requirements of the flow in the region in a large time scale according to the prediction result;
s4, determining precondition parameters for small-time scale flow transfer;
s5, carrying out traffic transfer according to the traffic size, type and resource state;
and S6, carrying out traffic balance and resource state evaluation of the nodes according to the result after traffic transfer.
The specific method of S1 is as follows:
the method comprises the steps of virtualizing a satellite and ground nodes, shielding the dynamic property of the satellite nodes, and covering a group of unchanged virtual satellite nodes and ground nodes in a geographic region within a time slice to obtain a satellite and ground node resource pool.
In S2, a specific method of predicting the arrival of the regional flow rate is as follows:
according to historical satellite ground flow data and periodicity of flow in the region, prediction under a large time scale is carried out, a frequency transfer matrix is obtained according to a state set and the historical flow data, a state transfer matrix is obtained according to the frequency transfer matrix, a Markov transfer chain is further established, and the flow at the next moment is predicted by analyzing the state transfer.
In S3, the method for determining whether the satellite ground resources meet the resource demand of the intra-area traffic within the large time scale according to the prediction result is as follows:
A long ＝C long ,S long ,T long }
wherein ,Along Is the flow H under a large time scale ′ The corresponding resource requirements need to be satisfied:
wherein ,
is the total amount of resources in a large time scale,
is the amount of resources used.
In S4, the preconditions for small-time scale traffic diversion are as follows:
N 0 ＝βN
wherein ,N0 For the nodes that can accept traffic transfer, N is the total number of nodes in the area, and β is the number used to adjust the number of the receiving nodes.
In S5, performing traffic shifting including delay-sensitive DS and delay-tolerant DT;
under the condition of the same traffic, the node firstly meets the resource requirement of the delay-sensitive DS, the rest of the resources change along with the dynamic resource requirement of the delay-sensitive DS, and the resource requirement of the delay-tolerant DT is dynamically supplied by the rest of the resources.
The traffic diversion of the delay-sensitive DS is based on the threshold T i To determine the threshold value T i Determined by the amount of resources of the node:
T i ＝γ·RES t ，
wherein ,Fi Is the current flow, RES, of node i at a small time scale t A ground node resource pool;
when the flow exceeds the threshold value, the flow is switchedThe flow rate of the transfer is F ij ,j∈N 0 To accommodate a node, the traffic of node j after the transfer is:
wherein ni is N-No, F j The original flow of the node is accommodated;
for the delay tolerant type DT related to the resource amount of the current node and the delay sensitive type DS, different kinds of delay tolerant type DT having different resource amount requirement are marked as
Indicating the resource allocated by the node i to the delay tolerant DT with the type k at the time of the time slot t, the incomplete delay tolerant DT is stored in the queue Q (t), and the length of the queue should be controlled because the delay tolerant DT also has a delay requirement:
wherein ,si Is the processing speed of the node i is not less than 0 and not more than s i ≤1，
To handle the existing traffic in the queue at time slot t,
the delay tolerant type DT flow with the type of k coming at the time of the time slot t is obtained;
and when the average processing speed of the node is less than the average flow arrival rate, carrying out flow transfer, wherein the flow of the transferred node j is as follows:
wherein the diverted flow rate is
And i, j epsilon to N is a node with the capacity of receiving traffic transfer.
Through the flow transfer of the delay sensitive DS and the queue cache and the flow transfer of the delay tolerant DT, the purposes of reducing the flow overload number of the nodes in the area, improving the resource utilization of idle nodes and reducing the overload ratio of the nodes in the area are achieved, and the method has the following formula:
wherein ,Rover For node overload ratio in the area, N over The number of overloaded nodes;
the stability of the queue to be guaranteed for the delay tolerant type DT has the following formula:
Q i (t)≤Q T
wherein ,QT The threshold value of the queue length, together with the node processing speed and the traffic arrival speed, determines whether the DT transfers traffic across the nodes.
Compared with the prior art, the heaven and earth fusion architecture provided by the invention aiming at the heaven and earth fusion network environment can sense network resource nodes in the collection area and process flow information, can adapt to the characteristics of the heaven and earth fusion network to predict the flow in the area under a large time scale, and can judge whether the resources in the area can meet the resource requirements corresponding to the predicted flow. By means of delay-sensitive cross-node traffic transfer on a small time scale, resource node overload conditions caused by traffic burst on the small time scale can be reduced, and resource utilization rate of idle nodes can be improved by performing queue caching and traffic transfer on delay-tolerant traffic.
Drawings
FIG. 1 is a diagram of a network architecture of the present invention;
FIG. 2 is a flow chart of flow prediction in a large time scale region according to the present invention;
FIG. 3 is a flow chart of the small time scale flow transfer of the present invention;
FIG. 4 is a schematic diagram of peak clipping and valley filling for flow transfer in accordance with the present invention;
FIG. 5 is a graph of node overload ratio and resource utilization before and after traffic shifting in the present invention;
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1, a flow processing module in the network performs flow prediction on a large time scale, a flow prediction process is shown in fig. 2, cross-node flow transfer on a small time scale is shown in fig. 3, finally, a flow balance state of a node in an area after flow transfer is obtained, and peak clipping and valley filling of the flow transfer are shown in fig. 4.
The invention specifically comprises the following steps:
the control layer is primarily based on the controller of the SDN to obtain information of the network and control and manage the network,
the flow processing layer is mainly responsible for flow prediction of large time scale and flow transfer of small time scale,
the resource layer virtualizes the space-based and ground-based nodes, abstracts multidimensional resources, shields the dynamics of satellite nodes, obtains a satellite ground node resource pool,
the geographical area can be seen in a time slice to be covered by a group of invariable virtual satellite nodes and ground nodes, and the resource pool RES of the satellite and the ground nodes is obtained t ＝{C t ,S t ,T t },0<t is less than or equal to Th, wherein C t For computing resources, S t To store resources, T t For transmission of resources, the resources have a time-to-live, 0<t ≦ Th may indicate that the node is within the geographic area.
Step 2, predicting the flow at the next moment, predicting the arrival of the flow in the region aiming at the characteristic that the flow in the space-ground converged network has periodicity, and predicting the arrival of the flow in the region according to the satellite-ground in the regionPrediction under large time scale is performed by using historical flow data and periodic flow characteristics as shown in fig. 2, the historical flow data is H, a corresponding state set S is divided into {1,2.. S }, and a frequency transfer matrix f is obtained according to the state set and the historical flow data ij Then, a state transition matrix p(s) ═ p is obtained ij (s)，p ij (s)＝P{x s+1 ＝j|x s I represents the probability of j over 1 step with time s in state i, where p ij (s)≥0，∑ j∈s p ij (s) ═ 1, and then a Markov transfer chain { x is established n N 1,2,. k }, and predicting the flow H at the next time by analyzing the state transition ′ If at all
The prediction result is considered to be credible, otherwise, the historical data average value is used
Step 3, determining whether the resource demand is met according to the prediction result, judging whether the satellite ground resources meet the resource demand of the flow in the area within a large time scale, and determining whether the existing resource quantity meets the resource demand corresponding to the flow according to the flow prediction result, wherein the method comprises the following steps:
A long ＝{C long ,S long ,T long }
wherein ,Along Is the flow H under a large time scale ′ Corresponding resource requirements, need to be satisfied
wherein ,
is the total amount of resources in a large time scale,
is the amount of resources used.
Step 4, determining precondition parameters for carrying out small-time-scale flow transfer, considering the complexity of flow transfer under small time scale, needing to set partial nodes with capacity of receiving flow transfer, and setting a parameter N 0 β N, wherein N 0 For the nodes which can accept the flow transfer of the accommodating nodes, N is the total number of the nodes in the area, the parameter beta is used for adjusting the number of the accommodating nodes, and for the delay tolerant flow, because the type can tolerate a certain delay, the adjacent idle nodes are selected as the accommodating nodes;
and 5, implementing flow transfer, namely performing flow transfer according to the size, type and resource state of the flow, and considering two different scenes in the cross-node flow transfer of the small time scale as shown in fig. 3: in the case of the same flow rate, the smaller service delay may result in larger resource requirement, so the node should ensure a lower overload level, so the resource requirement of the delay sensitive DS is satisfied first, the remaining resources vary with the dynamic resource requirement of the delay sensitive DS, and the resource requirement of the delay tolerant DT is dynamically supplied by the remaining resources.
The traffic shifting for the delay sensitive DS is based on a certain threshold T i To determine, T i Determined by the resource amount of the node
T i ＝γ·RES t ，
wherein ,Fi For the current traffic of node i at a small time scale,
when the flow exceeds the threshold value, the flow is transferred, and the transferred flow is F ij ,j∈N 0 To accommodate a node, the flow at node j after the transition is
Wherein ni is N-No, F j The original flow of the node is accommodated;
for the delay tolerant type DT related to the resource amount of the current node and the delay sensitive type DS, different kinds of delay tolerant type DT having different resource amount requirement are marked as
The resource allocated by the node i to the delay tolerant type DT with the type k at the time slot t is shown, the incomplete delay tolerant type DT can be stored in a queue q (t), and as the delay tolerant type DT also has a certain delay requirement, the length of the queue should be controlled:
wherein ,si The processing speed of the node i is more than or equal to s i ≤1，
To handle the existing traffic in the queue at time slot t,
the delay tolerant type DT traffic size of class k arriving at time slot t,
when the average processing speed of the node is less than the average flow arrival rate, the queue is too long and unstable, and the delay requirement cannot be met, the flow transfer is carried out, and the flow of the node j after the transfer is as follows:
wherein the diverted flow rate is
And i, j epsilon to N is a node with the capacity of receiving traffic transfer.
And 6, evaluating the effect after the flow transfer, carrying out flow balance and resource state evaluation on the nodes according to the result after the flow transfer, and reducing the flow overload number of the nodes in the area, improving the resource utilization of idle nodes and reducing the overload ratio of the nodes in the area by carrying out flow transfer on the delay sensitive DS and queue caching and flow transfer of the delay tolerant DT according to the following formula:
subjecttoN(1-β)≤ni<N，
i,j＝1,…,N；
wherein ,Rover For node overload ratio in the area, N over In order to determine the number of nodes that are overloaded,
for the delay tolerant DT, the following formula is used to ensure the stability of the queue
Q i (t)≤Q T ，
i,j＝1,…,N；
wherein ,QT The threshold value of the queue length, together with the node processing speed and the traffic arrival speed, determines whether the delay tolerant DT carries out the traffic transfer across the nodes.
Fig. 5 depicts a node overload ratio and a resource utilization ratio graph before and after traffic transfer, and it can be seen that the node overload ratio before traffic transfer is not high and the resource utilization ratio is also low, the node overload ratio is greatly reduced to 8.4% through traffic transfer, and then the resource utilization ratio is improved to 92.1%, which shows that the present invention can effectively reduce the resource node overload situation caused by traffic burstiness on a small time scale, and simultaneously improve the resource utilization ratio.
Claims (8)
1. A flow sensing method based on size time scale under a world fusion network is characterized by comprising the following steps:
s1, modeling the heaven and earth fusion network to obtain a satellite ground node resource pool;
s2, predicting arrival of the regional flow rate;
s3, judging whether the satellite ground resources meet the resource requirements of the flow in the region in a large time scale according to the prediction result;
s4, determining precondition parameters for small-time scale flow transfer;
s5, carrying out traffic transfer according to the traffic size, type and resource state;
and S6, carrying out traffic balance and resource state evaluation of the nodes according to the result after traffic transfer.
2. The traffic sensing method based on size time scale under the heaven-earth converged network according to claim 1, wherein the specific method of S1 is as follows:
the method comprises the steps of virtualizing a satellite and ground nodes, shielding the dynamic property of the satellite nodes, and covering a group of unchanged virtual satellite nodes and ground nodes in a geographic region within a time slice to obtain a satellite and ground node resource pool.
3. The method for sensing traffic based on size and time scale in the world-wide converged network according to claim 1, wherein in S2, the specific method for predicting the arrival of regional traffic is as follows:
according to historical satellite ground flow data and periodicity of flow in the region, prediction under a large time scale is carried out, a frequency transfer matrix is obtained according to a state set and the historical flow data, a state transfer matrix is obtained according to the frequency transfer matrix, a Markov transfer chain is further established, and the flow at the next moment is predicted by analyzing the state transfer.
4. The method for sensing traffic based on the size and time scale in the space-ground converged network according to claim 1, wherein in S3, the method for determining whether the satellite ground resources in the large time scale meet the resource demand of the traffic in the region according to the prediction result is as follows:
A long ＝{C long ，S long ，T long }
wherein ,Along The resource requirements corresponding to the flow H' under the large time scale need to be met:
wherein ,
is the total amount of resources in a large time scale,
is the amount of resources used.
5. The method for sensing traffic based on the size-time scale in the space-ground converged network according to claim 1, wherein the precondition parameters for the small-time scale traffic migration in S4 are as follows:
N 0 ＝βN
wherein ,N0 For the nodes that can accept traffic transfer, N is the total number of nodes in the area, and β is the number used to adjust the number of the receiving nodes.
6. The method for sensing traffic based on size-time scale in a space-ground converged network of claim 1, wherein in S5, traffic shifting comprises delay-sensitive DS and delay-tolerant DT;
under the condition of the same traffic, the node firstly meets the resource requirement of the delay-sensitive DS, the rest of the resources change along with the dynamic resource requirement of the delay-sensitive DS, and the resource requirement of the delay-tolerant DT is dynamically supplied by the rest of the resources.
7. The method of claim 6, wherein the delay-sensitive DS traffic diversion is based on a threshold T i To determine the threshold value T i Determined by the amount of resources of the node:
T i ＝γ·RES t ，
wherein ,Fi Is the current flow, RES, of node i at a small time scale t A ground node resource pool;
when the flow exceeds the threshold value, the flow is transferred, and the transferred flow is F ij ，j∈N 0 To accommodate a node, the traffic at node j after the transition is:
wherein ni is N-No, F j The original flow of the node is accommodated;
for the delay tolerant type DT related to the resource amount of the current node and the delay sensitive type DS, different kinds of delay tolerant type DT having different resource amount requirement are marked as
Indicating the resources allocated by node i to the delay tolerant type DT of type k at time slot t,the outstanding delay tolerant DT is stored in the queue Q (t), and the length of the queue should be controlled because the delay tolerant DT also has delay requirement:
wherein ,si Is the processing speed of the node i is not less than 0 and not more than s i ≤1，
To handle the existing traffic in the queue at time slot t,
the delay tolerant type DT flow size with the type of k coming at the time slot t;
and when the average processing speed of the node is less than the average flow arrival rate, carrying out flow transfer, wherein the flow of the transferred node j is as follows:
wherein the diverted flow rate is
Is a node with the capability of accepting traffic transfer.
8. The traffic sensing method based on the size and time scale in the space-ground converged network according to claim 6, wherein the traffic overload number of the nodes in the area is reduced, the resource utilization of the idle nodes is improved, and the overload ratio of the nodes in the area is reduced by the traffic transfer of the delay sensitive DS and the queue caching and traffic transfer of the delay tolerant DT, according to the following formula:
wherein ,Rover For node overload ratio in the area, N over The number of overloaded nodes;
the stability of the queue to be guaranteed for the delay tolerant type DT has the following formula:
Q i (t)≤Q T
wherein ,QT The threshold value of the queue length, together with the node processing speed and the traffic arrival speed, determines whether the DT transfers traffic across the nodes.
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| CN111194045A (en) * | 2019-12-28 | 2020-05-22 | 西安交通大学 | Energy-saving method based on user group aggregation behavior model |
| WO2021174735A1 (en) * | 2020-03-03 | 2021-09-10 | 中国科学院计算技术研究所 | Dynamic resource scheduling method for guaranteeing latency slo of latency-sensitive application, and system |
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