CN115002783A - Industrial Internet of things resource dynamic allocation method based on network slice - Google Patents
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Abstract
The invention discloses a network slice-based dynamic allocation method for resources of an industrial internet of things, which utilizes the computing power and service provided by an MEC node as a relay to timely compute and process uploaded large-scale data on an edge side as required on the premise of ensuring the large-scale connection requirement of mMTC slices and real-time uploading of data of underlying equipment, thereby effectively reducing the bandwidth occupation of the data uploaded to a cloud end, providing released bandwidth resources to URLLC slices with higher time delay requirement, reducing the data uploading time delay, improving the bandwidth utilization rate in a network, and simultaneously optimizing the computing power consumption of the mMTC equipment for data processing on the MEC node so as to meet the QoS requirements of low-delay business and mass connection business in industry.
Description
Technical Field
The invention relates to the technical field of differentiated service communication in industrial scenes, in particular to a network slice-based dynamic allocation method for resources of an industrial internet of things.
Background
Network slicing is a key technology of a high-speed wireless network nowadays, a physical network which exists in reality can be divided into a plurality of virtual networks which are independent from each other and different in type, and corresponding network functions and network resources are allocated to the virtual networks according to QoS (quality of service) requirements of different services, such as delay height, bandwidth size and other indexes, so that the goal of providing customized services for differentiated services is achieved. According to the service classification in the application scene, the network slices can be divided into three major categories, including a large connection requirement mtc (mass machine type communication), an ultra-low delay requirement URLLC (ultra-reliable low delay communication) and a large capacity requirement eMBB (large bandwidth communication). This problem can be solved by merging edge computation techniques in order to achieve specific functionality for different network slices.
The edge computing is an open platform which is physically close to the network edge side of a data source and integrates network, computing, storage and application core capabilities, and a computing mode of edge intelligent service is provided nearby. The internet of things is vigorously developed in various fields, and the times of all things interconnection are gradually increased.
In the era of the internet of things, along with the development of services, a large number of devices are connected to a network, the devices belong to different fields and have differentiated QoS requirements, for example, in the field of industrial internet of things, tasks such as automatic device motion control and fault alarm require real-time reliable data transmission, and network delay needs to reach millisecond level; and tasks such as industrial measurement and control, process perception and the like require large-scale and low-power transmission.
With the development of the internet of things, almost all electronic devices can be connected to the internet and generate massive data, and transmitting such massive data from the local to the cloud end is a huge challenge to network bandwidth. Therefore, it is necessary to use the network slicing technique to slice the services with different QoS requirements, and use the computing and storing services to dynamically adjust the network bandwidth resource allocation among the slices by merging the edge computing technique.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a network slice-based industrial internet of things resource dynamic allocation method, which meets the QoS requirements of URLLC and mMTC two machine communication services, and dynamically allocates limited bandwidth resources in a network between the two services according to the dynamic QoS requirements, so that the utilization rate of the limited bandwidth resources in the network is maximized.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a network slice-based industrial Internet of things resource dynamic allocation method comprises the following steps:
step S1, building a three-layer bidirectional communication network architecture: the three-layer bidirectional communication network architecture comprises a comprehensive management terminal, a cloud center and mutually independent and isolated URLLC slices and mMTC slices which are established by different QoS requirements, wherein the URLLC slices comprise U URLLC devices and K FAPs; the mMTC slice comprises M mMTC devices, N subcarriers and an MEC node;
step S2, where the set of U URLLC devices is represented as P {1, 2, 3., U }, and the set of K FAP devices is represented as Q {1, 2, 3., K }, where U ∈ P and K ∈ Q, an upload rate R when the U-th URLLC device selects the K-th FAP for data transmission is calculated u，k So as to calculate the data uploading time delay t of each URLLC device u ；
Step S3, where M mtc device sets are denoted as T ═ 1, 2, 3., M }, and N subcarrier sets are denoted as Y ═ 1, 2, 3., N }, where M ∈ T and N ∈ Y, a data upload rate R of each mtc device on any subcarrier N is calculated m，n So as to calculate the calculated energy consumption E of each mMTC device in the MEC node through the nth subcarrier m And obtaining the total calculated energy consumption sigma m∈M E m ；
Step S4, establishing a bandwidth release model in the mMTC slice by using the computing power of the MEC node;
step S5, after the calculation processing of the MEC node, the bandwidth of the subcarrier carried and utilized by each mtc device is
Thereby obtaining the data uploading rate R of each mMTC device at the moment * m，n ；
Step S6, under the condition that the total system bandwidth is limited, bandwidth resources are dynamically adjusted between URLLC slices and mMTC slices as required according to service requirements sent by the URLLC slices and the mMTC slices to the comprehensive management terminal respectively;
and step S7, respectively giving an optimization objective function according to QoS requirements of the URLLC slices and the mMTC slices, converting a resource distribution problem formed between the URLLC slices and the mMTC slices into a layered game problem, and obtaining a balanced game solution by researching the game.
The technical scheme of the invention is further improved as follows: in the step S2, the uploading rate R when the uth URLLC device selects the kth FAP for data transmission u，k Expressed as:
wherein σ 2 As noise power, B uk 、p uk 、h uk Respectively selecting the total bandwidth, transmission power and channel gain of the kth FAP for the u URLLC equipment when data are uploaded;
the data uploading speed expression of each URLLC device is as follows:
R u (x uk )＝x uk R u，k ，
wherein, x is defined uk E {0, 1} is a Boolean variable, when the ujth URLLC device selects the kth FAP for data transmission, x uk 1, otherwise x uk ＝0；
Data uploading delay t of each URLLC device u Expression ofThe formula is as follows:
wherein s is u The size of the data content uploaded for URLLC devices.
The technical scheme of the invention is further improved as follows: in the step S3, each mtc device uploads the data rate R on any subcarrier n m，n The expression of (a) is:
wherein, B n Is the bandwidth of each subcarrier, p mn And h mn Respectively transmitting power and channel gain when the mth mtc device uploads data on any subcarrier n;
the data uploading speed expression of the mth mtc equipment is:
R m (x mn )＝x mn R m，n ，
wherein, x is defined mn E {0, 1} is a Boolean variable, x mn 1 means that the mth mtc device is a flag variable allocated at any nth subcarrier, otherwise, x mn ＝0；
Calculating energy consumption E of each mMTC device in MEC node through nth subcarrier m The expression is as follows:
E m ＝x mn s m c m αf i 2
s m expressed as the size of data quantity that needs to be uploaded by the mth mMTC device, c m Expressed as computational complexity per bit of data, α is an adjustment parameter for the computational power of the MEC node, f i 2 Computing power for the MEC node.
The technical scheme of the invention is further improved as follows: the bandwidth release model expression in step S4 is:
wherein, B nM For the bandwidth released after each mMTC device is processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth.
The technical scheme of the invention is further improved as follows: the data upload rate R of each mtc device at this time after the calculation processing of the MEC node in step S5 * m，n The expression is as follows:
the technical scheme of the invention is further improved as follows: in step S7, the optimization objective of the URLLC slice is to minimize data upload delay, and the expression of the corresponding optimization objective function is:
the constraint indicates that the time delay of the URLLC device in data uploading through the selected FAP is not more than the maximum time delay which can be tolerated by the URLLC device
On the premise that the optimization target of the mMTC slice is to ensure real-time uploading of equipment data, the calculation power consumption of the equipment for data processing at the MEC node is minimized, and the expression of a corresponding optimization objective function is as follows:
the constraint (b1) indicates that the energy consumption of the MEC node for data processing must not exceed the maximum energy consumption that the system can tolerate
The constraint (b2) indicates that the transmission rate of the uploaded data of each mMTC device after the data processing of the MEC node is not lower than the lowest tolerable transmission rate
The technical scheme of the invention is further improved as follows: in the step S7, an interaction between the URLLC slice and the mtc slice is modeled as a Stackelberg game, the URLLC slice is used as a follower, the mtc slice is used as a leader, the URLLC slice as the follower determines the amount of released bandwidth obtained from the mtc slice to minimize data upload delay, the mtc slice makes a decision according to a data upload delay request sent by the URLLC slice, and determines the computation capability of uploaded data in the MEC node while optimizing computation power consumption on the premise of ensuring that the upload rate of each device data in the mtc slice is greater than the minimum rate limit, an optimal bandwidth allocation solution is obtained by studying the game between the URLLC slice and the mtc slice, and the Qos requirements of the URLLC slice and the mtc slice are balanced under the game equilibrium solution.
Due to the adoption of the technical scheme, the invention has the technical progress that:
1. the invention breaks through the single technical route of the traditional mobile communication facing to the large bandwidth communication, utilizes the network slicing technology and the edge computing technology to formulate a resource allocation scheme aiming at the services of 'mass machine type communication' and 'ultra-reliable low-time-delay communication' facing to the vertical industry in the current industrial high-speed wireless network such as 5G, reduces the economic cost and improves the resource utilization rate;
2. according to the invention, the computation capability and service provided by the MEC node used as the relay are utilized, the computation processing is carried out on the uploaded large-scale data at the edge side as required on the premise of ensuring the large-scale connection requirement of the mMTC slice and the real-time uploading of the data of the underlying equipment, the bandwidth occupation of the data uploaded to the cloud is effectively reduced, the released bandwidth resource is provided for the URLLC slice with higher requirement on time delay, the data uploading time delay is reduced, the bandwidth utilization rate in the network is improved, and the computation power consumption of the mMTC equipment for data processing at the MEC node is optimized, so that the QoS requirements of low-delay business and massive connection business in the industry are met.
Drawings
FIG. 1 is a three-layer two-way communication network slice architecture diagram constructed in accordance with the present invention;
fig. 2 is a flow chart of bandwidth resource allocation according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following examples:
the invention provides a dynamic resource allocation method between network slices combined with an edge computing technology, which is developed by aiming at the differentiated QoS (quality of service) requirements of each slice in an industrial scene through dynamic virtual resource allocation to meet the service requirements of users and improve the economic benefit of a network under the condition of limited system bandwidth resources.
The invention is further described with reference to the accompanying drawings:
step S1, building a three-layer bidirectional communication network architecture: as shown in fig. 1, a three-tier bidirectional communication network architecture includes a comprehensive management terminal, a cloud center, and mutually independent and isolated URLLC slices and mtc slices established by different QoS requirements, where the URLLC slices include U URLLC devices and K FAP, and FAP with cache and computing capabilities are sunk to the edge of a network and deployed in an area close to a bottom layer device, thereby improving the situation of high delay caused by the device directly uploading data to the comprehensive management terminal in a long distance. The mMTC slice comprises M mMTC devices, N subcarriers and an MEC node, the MEC node is used for compressing ultrahigh bandwidth occupation required by large-scale equipment when uploading data, the MEC node is used as a data forwarding relay, only a small amount of data is uploaded after the data uploaded by bottom-layer equipment is calculated, and bandwidth occupation of the data uploaded to a cloud end is effectively reduced.
Step S2, where the set of U URLLC devices is represented as P {1, 2, 3., U }, and the set of K FAP devices is represented as Q {1, 2, 3., K }, where U ∈ P and K ∈ Q, an upload rate R when the U-th URLLC device selects the K-th FAP for data transmission is calculated u，k Thereby calculating the data uploading time delay t of each URLLC device u ；
Wherein the uploading rate R when the kth FAP is selected by the u URLLC device for data transmission u，k Expressed as:
wherein σ 2 As the noise power, B uk 、p uk 、h uk Respectively selecting the total bandwidth, transmission power and channel gain of the kth FAP for the u URLLC equipment when data are uploaded;
the data uploading speed expression of each URLLC device is as follows:
R u (x uk )＝x uk R u，k ，
since multiple FAPs are contained in a URLLC slice, and each device can select any suitable FAP as a relay node, x is defined uk E {0, 1} is a Boolean variable, when the u-th URLLC device selects the k-th FAP for data transmission, x uk 1, otherwise x uk ＝0；
Data uploading delay t of each URLLC device u The expression is as follows:
wherein s is u Uploading large data content for URLLC deviceIs small.
Step S3, where M mtc device sets are denoted as T ═ 1, 2, 3., M }, and N subcarrier sets are denoted as Y ═ 1, 2, 3., N }, where M ∈ T and N ∈ Y, a data upload rate R of each mtc device on any subcarrier N is calculated m，n Therefore, the calculation energy consumption Em of each mMTC device in the MEC node through the nth subcarrier is calculated, and the total calculation energy consumption Sigma is obtained m∈M E m ；
Wherein, each mMTC device has a data uploading rate R on any subcarrier n m，n The expression of (c) is:
wherein, B n Is the bandwidth of each subcarrier, p mn And h mn Respectively transmitting power and channel gain when the mth mtc device uploads data on any subcarrier n;
the data uploading speed expression of the mth mtc equipment is:
R m (x mn )＝x mn R m，n ，
wherein, x is defined mn E {0, 1} is a Boolean variable, x mn 1 means that the mth mtc device is a flag variable allocated to any nth subcarrier, otherwise x mn ＝0；
Calculating energy consumption E of each mMTC device in MEC node through nth subcarrier m The expression is as follows:
E m ＝x mn s m c m αf i 2
s m expressed as the size of data quantity that needs to be uploaded by the mth mMTC device, c m Expressed as computational complexity per bit of data, α is an adjustment parameter for the computational power of the MEC node, f i 2 Computing power for the MEC node.
Step S4, in the mMTC slice, a bandwidth release model is established by using the computing power of the MEC node, and the expression of the bandwidth release model is as follows:
wherein, B nM For the bandwidth released after each mMTC device is processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth.
Step S5, after the calculation processing of the MEC node, the bandwidth of the subcarrier carried and utilized by each mtc device is
Thereby obtaining the data uploading rate R of each mMTC device at the moment * m，n The expression is:
step S6, under the condition that the total bandwidth of the system is limited, dynamically adjusting bandwidth resources between URLLC slices and mtc slices as needed according to the service requirements sent by the URLLC slices and mtc slices to the integrated management terminal, as shown in fig. 2, assuming that the total amount of bandwidth pre-allocated to the URLLC slices is B1 and the total amount of bandwidth allocated to the mtc slices is B2, after performing calculation processing by the MEC node serving as a relay in the mtc slices, on the premise of ensuring the lowest upload rate of the mtc slice bottom layer devices, the total amount of bandwidth released is B3, and according to the service request sent by the URLLC slices, the released bandwidth resources are provided to the URLLC slices for use, so as to reduce the data upload delay and improve the bandwidth utilization in the network.
Step S7, respectively providing an optimization objective function according to QoS requirements of the URLLC slice and the mtc slice, where the optimization objective of the URLLC slice is to minimize data upload delay, and the expression of the corresponding optimization objective function is:
the constraint indicates that the time delay of the URLLC device in data uploading through the selected FAP is not more than the maximum time delay which can be tolerated by the URLLC device
On the premise of ensuring real-time uploading of equipment data, the optimization target of the mMTC slice minimizes the calculation power consumption of the equipment for data processing at the MEC node, and the expression of the corresponding optimization target function is as follows:
the constraint (b1) indicates that the energy consumption of the MEC node for data processing must not exceed the maximum energy consumption that the system can tolerate
The constraint (b2) indicates that the transmission rate of the uploaded data of each mMTC device after the data processing of the MEC node is not lower than the lowest tolerable transmission rate
The method comprises the steps of converting a resource allocation problem formed between URLLC slices and mMTC slices into a layered game problem, modeling interaction between the URLLC slices and the mMTC slices into a Stackelberg game, taking the URLLC slices as followers and the mMTC slices as leaders, determining the quantity of released bandwidth obtained from the mMTC slices by the URLLC slices as the followers so as to minimize data uploading delay, making a decision by the mMTC slices according to data uploading delay requests sent by the URLLC slices, determining the computing capability of uploaded data in an MEC node and optimizing computing power consumption on the premise of ensuring that the data uploading rate of each device in the mMTC slices is greater than the limit of the lowest rate, obtaining an optimal bandwidth allocation solution by researching the game between the URLLC slices and the mMTC slices, and balancing the requirements of the URLLC slices and the mMTC slices under the game equilibrium solution.
Claims (7)
1. A network slice-based industrial Internet of things resource dynamic allocation method is characterized by comprising the following steps: the method comprises the following steps:
step S1, building a three-layer bidirectional communication network architecture: the three-layer bidirectional communication network architecture comprises a comprehensive management terminal, a cloud center and mutually independent and isolated URLLC slices and mMTC slices which are established by different QoS requirements, wherein the URLLC slices comprise U URLLC devices and K FAPs; the mMTC slice comprises M mMTC devices, N subcarriers and an MEC node;
step S2, the U sets of URLLC devices are denoted as P ═ 1, 2, 3., U }, and the K sets of FAP are denoted as Q ═ 1, 2, 3., K }, where U belongs to P and K belongs to Q, and an upload rate R when the U-th URLLC device selects the K-th FAP for data transmission is calculated u，k Thereby calculating the data uploading time delay t of each URLLC device u ；
Step S3, where M mtc device sets are denoted as T ═ 1, 2, 3., M }, and N subcarrier sets are denoted as Y ═ 1, 2, 3., N }, where M ∈ T and N ∈ Y, a data upload rate R of each mtc device on any subcarrier N is calculated m，n So as to calculate the calculated energy consumption E of each mMTC device in the MEC node through the nth subcarrier m And obtaining the total calculated energy consumption sigma m∈ M E m ；
Step S4, establishing a bandwidth release model in the mMTC slice by using the computing power of the MEC node;
step S5, calculation processing after passing through MEC nodeThen, each mMTC device carries and utilizes the sub-carrier bandwidth of
Thereby obtaining the data uploading rate R of each mMTC device at the moment * m，n ；
Step S6, under the condition that the total system bandwidth is limited, according to the service requirements respectively sent to the comprehensive management terminal by the URLLC slices and the mMTC slices, the bandwidth resources are dynamically adjusted between the URLLC slices and the mMTC slices as required;
and step S7, respectively giving an optimization objective function according to QoS requirements of the URLLC slices and the mMTC slices, converting a resource allocation problem formed between the URLLC slices and the mMTC slices into a layered game problem, and obtaining a game equilibrium solution by researching the game.
2. The method for dynamically allocating resources of the internet of things based on the network slice as claimed in claim 1, wherein: in step S2, when the uth URLLC device selects the kth FAP for data transmission, the upload rate ru.k is expressed as:
wherein σ 2 As the noise power, B uk 、p uk 、h uk Respectively selecting the total bandwidth, transmission power and channel gain of the kth FAP for the u URLLC equipment when data are uploaded;
the data uploading speed expression of each URLLC device is as follows:
R u (x uk )＝x uk R u，k ，
wherein, x is defined uk E {0, 1} is a Boolean variable, when the u-th URLLC device selects the k-th FAP for data transmission, x uk 1, otherwise x uk ＝0；
Data uploading delay t of each URLLC device u The expression is as follows:
wherein s is u The size of the data content uploaded for URLLC devices.
3. The method for dynamically allocating resources of the industrial internet of things based on the network slice as claimed in claim 2, wherein: in the step S3, each mtc device uploads the data rate R on any subcarrier n m，n The expression of (a) is:
wherein, B n Is the bandwidth of each subcarrier, p mn And h mn Respectively transmitting power and channel gain when the mth mtc device uploads data on any subcarrier n;
the data uploading speed expression of the mth mtc equipment is:
R m (x mn )＝x mn R m，n ，
wherein, x is defined mn E {0, 1} is a Boolean variable, x mn 1 means that the mth mtc device is a flag variable allocated to any nth subcarrier, otherwise x mn ＝0；
The expression of the calculated energy consumption Em of each mtc device in the MEC node via the nth subcarrier is:
E m ＝x mn s m c m αf i 2 ，
s m expressed as the size of data quantity that needs to be uploaded by the mth mMTC device, c m Expressed as computational complexity per bit of data, α is an adjustment parameter for the computational power of the MEC node, f i 2 Computing power for the MEC node.
4. The method for dynamically allocating resources of the industrial internet of things based on the network slice as claimed in claim 3, wherein: the bandwidth release model expression in step S4 is:
wherein, B nM For the bandwidth released after each mMTC device is processed by the MEC node, K and omega are model parameters, and theta is an adjustment parameter of the MEC node bandwidth.
5. The method for dynamically allocating resources of the industrial internet of things based on the network slice as claimed in claim 4, wherein: the data upload rate R of each mtc device at this time after the calculation processing of the MEC node in step S5 * m，n The expression is as follows:
6. the method for dynamically allocating resources of the industrial internet of things based on the network slice as claimed in claim 5, wherein: in step S7, the optimization objective of the URLLC slice is to minimize data upload delay, and the expression of the corresponding optimization objective function is:
the constraint indicates that the time delay of the URLLC device in data uploading through the selected FAP is not more than the maximum time delay which can be tolerated by the URLLC device
On the premise of ensuring real-time uploading of equipment data, the optimization target of the mMTC slice minimizes the calculation power consumption of the equipment for data processing at the MEC node, and the expression of the corresponding optimization target function is as follows:
the constraint (b1) indicates that the energy consumption of the MEC node for data processing must not exceed the maximum energy consumption that the system can tolerate
The constraint (b2) indicates that the transmission rate of the uploaded data of each mMTC device after the data processing of the MEC node is not lower than the lowest tolerable transmission rate
7. The method for dynamically allocating resources of the industrial internet of things based on the network slice as claimed in claim 6, wherein: in step S7, an interaction between the URLLC slices and the mtc slices is modeled as a Stackelberg game, the URLLC slices are used as followers, the mtc slices are used as leaders, the URLLC slices used as followers determine the amount of released bandwidth obtained from the mtc slices to minimize data upload delay, the mtc slices make a decision according to a data upload delay request sent by the URLLC slices, on the premise of ensuring that the upload rate of each piece of equipment in the mtc slices is greater than the minimum rate limit thereof, the computational power of uploaded data in MEC nodes is determined while optimizing the computational power consumption, an optimal bandwidth allocation solution is obtained by studying the game between the URLLC slices and the mtc slices, and the Qos requirements of the URLLC slices and the mtc slices are balanced under the game equilibrium solution.
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