Unmanned cluster assisted multi-strategy network service dynamic migration method and device

Abstract

The invention provides an unmanned cluster assisted multi-strategy network service dynamic migration method and device, relates to the technical field of communication, and considers multiple network service migration strategies to optimize the network service migration problem in an emergency communication network. After the long-term network service optimization function of all users in the emergency communication network is constructed, in view of the fact that the long-term network service optimization function cannot be solved directly, the long-term network service optimization function is further decoupled into an independent time slot strategy optimization function through the Lyapunov theory and then solved, and the long-term network service optimization function is obtained. Therefore, a network service migration strategy of all users in the emergency communication network under each time slot is obtained, scientific guidance is provided for network service migration of the users in the emergency communication network, the user service quality is improved, and the stability of the system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of communications, and in particular to an unmanned cluster-assisted multi-strategy network service dynamic migration method and device. Background Art

[0002] Mobile edge computing is a promising technology that can meet the computing needs of resource-limited mobile terminals by offloading computing tasks to edge servers. Edge servers are usually installed on cellular base stations at fixed locations. Since user terminals usually move irregularly, services need to be dynamically migrated between multiple edge servers to maintain service performance, namely service migration.

[0003] Currently, most of the research on service migration focuses on cellular networks. This type of research is based on a scenario where a mobile terminal can access a ground base station at any location. In this scenario, service migration is only performed through ground base stations. The problem can be described as when and where to migrate services. Specifically, it is a binary decision problem of whether to migrate services between two ground base stations in a certain time slot. The decision consists of two variables between ground base stations: "migrate" and "do not migrate".

[0004] However, more than 50% of the world's regions still lack Internet access, such as rural areas, remote mountainous areas, and isolated island areas. In particular, networks with ground communication equipment destroyed by natural disasters such as earthquakes and floods. In order to meet the demand for communication service access in these situations, the industry has turned its attention to emergency communication networks built by satellites and drones in recent years. Due to the diversity of communication methods of emergency communication networks and their differences from cellular networks, the service migration strategy determination method of cellular networks is not applicable to emergency communication networks, and no method has been proposed in the prior art to provide guidance for service migration in emergency communication networks. Summary of the invention

[0005] The purpose of the present invention is to provide an unmanned cluster-assisted multi-strategy network service dynamic migration method and device to provide scientific guidance for network service migration of users in an emergency communication network.

[0006] In a first aspect, the present invention provides an unmanned cluster-assisted multi-strategy network service dynamic migration method, including: obtaining a first migration overhead, a second migration overhead, and the network service data volume of each user in an emergency communication network; wherein the first migration overhead represents the overhead of the user migrating the network service through a drone base station, and the second migration overhead represents the overhead of the user migrating the network service through a satellite; based on the first migration overhead and the second migration overhead, constructing an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot; wherein the target user represents any user in the emergency communication network; the target time slot represents any time slot in the long-term network service duration; based on the network service data of the target user The service delay calculation function of the target user in the target time slot is constructed by taking into account the transmission bandwidth of each data transmission link in the emergency communication network; under the overhead cost constraint and the service delay constraint, the long-term network service optimization function of all users in the emergency communication network is constructed based on the overhead cost calculation function and the service delay calculation function; based on the Lyapunov theory, the long-term network service optimization function is decoupled into an independent time slot strategy optimization function; the independent time slot strategy optimization function is solved to obtain the network service migration strategy of all users in the emergency communication network in each time slot; among which, the network service migration strategy includes one of the following: no network service migration, network service migration via satellite, and network service migration via drone base station.

[0007] Optionally, the long-term network service optimization function is decoupled into an independent time slot strategy optimization function based on Lyapunov theory, including: constructing a Lyapunov function and an energy queue update function of the target user in the target time slot based on the overhead cost constraint of the long-term network service optimization function; calculating the upper bound of the Lyapunov drift function based on the Lyapunov function and the energy queue update function; taking the sum of the network service target optimization functions of all users in the target time slot as the penalty term of the Lyapunov drift function, and constructing a Lyapunov drift plus penalty objective function; wherein the network service target optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function; solving the upper bound of the objective function based on the upper bound of the Lyapunov drift function; removing the constant term in the upper bound of the objective function to obtain the independent time slot strategy optimization function.

[0008] Optionally, the long-term network service optimization function is expressed as: ;in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service; , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station; , , represents the first migration cost, represents the second migration overhead; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

[0009] Optionally, the energy queue update function is expressed as: ;in, represents the energy queue update function of user k in time slot t+1, represents the energy queue update function of user k in time slot t; the Lyapunov function is expressed as: .

[0010] Optionally, the independent time slot strategy optimization function is expressed as: ; represents the penalty coefficient, .

[0011] In a second aspect, the present invention provides an unmanned cluster-assisted multi-strategy network service dynamic migration device, including: an acquisition module, used to obtain a first migration overhead, a second migration overhead and the network service data volume of each user in the emergency communication network; wherein the first migration overhead represents the overhead of the user migrating the network service through the drone base station, and the second migration overhead represents the overhead of the user migrating the network service through the satellite; a first construction module, used to construct an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot based on the first migration overhead and the second migration overhead; wherein the target user represents any user in the emergency communication network; the target time slot represents any time slot in the long-term network service duration; and a second construction module, used to construct an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot based on the first migration overhead and the second migration overhead; The first embodiment comprises a method for constructing a service delay calculation function for the target user in the target time slot based on the data volume and the transmission bandwidth of each data transmission link in the emergency communication network; a third construction module is used to construct a long-term network service optimization function for all users in the emergency communication network based on the overhead cost calculation function and the service delay calculation function under the overhead cost constraint and the service delay constraint; a decoupling module is used to decouple the long-term network service optimization function into an independent time slot strategy optimization function based on Lyapunov theory; a solution module is used to solve the independent time slot strategy optimization function to obtain the network service migration strategy for all users in the emergency communication network in each time slot; wherein the network service migration strategy includes one of the following: no migration of network service, network service migration via satellite, and network service migration via drone base station.

[0012] Optionally, the decoupling module is specifically used to: construct a Lyapunov function and an energy queue update function of the target user in the target time slot based on the overhead cost constraint of the long-term network service optimization function; calculate the upper bound of the Lyapunov drift function based on the Lyapunov function and the energy queue update function; take the sum of the network service target optimization functions of all users in the target time slot as the penalty term of the Lyapunov drift function, and construct a Lyapunov drift plus penalty objective function; wherein the network service target optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function; solve the upper bound of the objective function based on the upper bound of the Lyapunov drift function; remove the constant term in the upper bound of the objective function to obtain an independent time slot strategy optimization function.

[0013] Optionally, the long-term network service optimization function is expressed as: ;in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service; , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station; , , represents the first migration cost, represents the second migration overhead; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

[0014] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the unmanned cluster-assisted multi-strategy network service dynamic migration method of any one of the aforementioned implementation modes is implemented.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the unmanned cluster-assisted multi-strategy network service dynamic migration method of any one of the aforementioned implementations.

[0016] The present invention proposes an unmanned cluster-assisted multi-strategy network service dynamic migration method, which takes into account multiple network service migration strategies to optimize the network service migration problem in the emergency communication network. After constructing the long-term network service optimization function of all users in the emergency communication network, since the long-term network service optimization function cannot be directly solved, the long-term network service optimization function is further decoupled into an independent time slot strategy optimization function through Lyapunov theory and then solved, thereby obtaining the network service migration strategy of all users in the emergency communication network in each time slot, so as to provide scientific guidance for the network service migration of users in the emergency communication network, thereby improving the user service quality and ensuring the stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A system architecture diagram of a multi-strategy network service migration emergency communication network system provided by an embodiment of the present invention; Figure 2 A flowchart of an unmanned cluster-assisted multi-strategy network service dynamic migration method provided by an embodiment of the present invention; Figure 3 To provide a comparison chart of the probability of uninterrupted service for different types of network service migration strategies; Figure 4 A functional module diagram of an unmanned cluster-assisted multi-strategy network service dynamic migration device provided by an embodiment of the present invention; Figure 5 A schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0022] Embodiment 1 Figure 1 A system architecture diagram of a multi-strategy network service migration emergency communication network system provided by an embodiment of the present invention, such as Figure 1As shown, the emergency communication network system is composed of multiple ground base stations, multiple drone base stations and a satellite. Each network node (ground base station, drone base station and satellite) in the emergency communication network is integrated with a server. If it carries a user's exclusive virtual container, then the node can provide computing services for the user. In view of the purpose of the embodiment of the present invention is to determine the network service migration strategy of users in the emergency communication network, therefore, the users in the embodiment of the present invention are all defaulted to users who need to provide network services in the emergency communication network, and users who do not need network services are not considered.

[0023] In the emergency communication network, given the long distance between ground base stations, there is a drone base station between adjacent ground base stations to ferry information. In addition, since satellites have the characteristics of wide-area coverage, they can establish communication links with any other network nodes and users in the system.

[0024] Figure 2 A flowchart of an unmanned cluster-assisted multi-strategy network service dynamic migration method provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method specifically comprises the following steps: Step S102: obtaining a first migration cost, a second migration cost, and a network service data volume of each user in the emergency communication network.

[0025] Among them, the first migration overhead represents the overhead of users migrating network services through drone base stations, and the second migration overhead represents the overhead of users migrating network services through satellites.

[0026] In the embodiment of the present invention, when user k in the emergency communication network transfers from ground base station i to adjacent ground base station j in time slot t, i≠j, the emergency communication network can provide the following three network service migration strategies: no network service migration, network service migration via satellite, and network service migration via drone base station. The network service migration strategy for user k moving from ground base station i to ground base station j in time slot t is recorded as ;in, Indicates that the network service does not migrate, that is, the business calculation of the network service does not migrate and remains at the ground base station i, but the calculation results are transmitted via satellite; Indicates that users migrate network services through drone base stations; Indicates that users migrate network services via satellite.

[0027] Although a better quality of service (Qos) can be obtained by actively migrating network services when users transfer between ground base stations, the additional overhead costs incurred by network service migration will cause huge consumption to the network. If the network service is not migrated, the migration overhead is 0. Therefore, in order to balance performance and cost in a cost-effective manner, the embodiment of the present invention comprehensively considers the system's service delay and the overhead costs caused by migration. Given that the service delay is positively correlated with the user's network service data volume, the embodiment of the present invention first needs to determine the first migration overhead in the emergency communication network. and the second migration overhead , and the amount of network service data per user.

[0028] Step S104: constructing an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot based on the first migration overhead and the second migration overhead.

[0029] The target user represents any user in the emergency communication network; the target time slot represents any time slot in the long-term network service duration.

[0030] Specifically, it is known that different network service migration strategies correspond to different migration costs. Based on this, the embodiment of the present invention introduces the Dirac function , is a generalized function whose integral is always 1, that is, And, only when hour, ;when hour, Utilizing the above characteristics of the Dirac function, the embodiment of the present invention constructs the overhead cost calculation function of user k moving from ground base station i to ground base station j in time slot t as follows: , ; N represents the total number of ground base stations.

[0031] Obviously, In the sum expansion of may be non-zero, and the rest are all 0, that is, only the corresponding values ​​of the two determined ground base stations involved in the transfer of user k in time slot t It may not be 0. For ease of understanding, the following example is given. Assume that there are N ground base stations in the system, and user k moves from ground base station 3 to ground base station 4 in time slot t, then In the summation expansion of May be non-zero value, the rest , of The value of should all be 0.

[0032] Step S106, constructing a service delay calculation function for the target user in the target time slot based on the network service data volume of the target user and the transmission bandwidth of each data transmission link in the emergency communication network.

[0033] Based on the specific migration methods of the three network service migration strategies mentioned above, we can know that according to the network service data volume of user k and the data transmission link bandwidth involved in each network service migration strategy, the service delay corresponding to each network service migration strategy can be calculated. Combined with the characteristics of the Dirac function, the service delay calculation function of user k in time slot t can be constructed: ;in, represents the amount of network service data for user k, Indicates the transmission bandwidth between the ground and the satellite. The ground includes: ground base stations and users. The transmission bandwidth between the satellite and the ground base station is the same as the transmission bandwidth between the satellite and the user. represents the transmission bandwidth between user k and the drone base station, It represents the transmission bandwidth between user k and the ground base station. 0 means that the network service does not migrate and the network service is still executed at the ground base station i. If i=j, it means that the user does not move and data is transmitted directly through the ground base station i.

[0034] Step S108, under the constraints of overhead cost and service delay, based on the overhead cost calculation function and the service delay calculation function, construct a long-term network service optimization function for all users in the emergency communication network.

[0035] In the embodiment of the present invention, the average overhead of a user in a single time slot is Cannot exceed the cost threshold , the service delay of a single time slot for a user The delay threshold cannot be exceeded ,Under the above overhead cost constraints and service delay constraints, based on the service delay calculation function and overhead cost calculation function of the target user in the target time slot, the long-term network service optimization function of K users in the emergency communication network within T time slots can be constructed.

[0036] Step S110, decoupling the long-term network service optimization function into an independent time slot strategy optimization function based on Lyapunov theory.

[0037] Since the long-term network service optimization function is a long-term optimization target, it cannot be solved directly. Moreover, due to the random arrival of services and the time-varying state of the wireless channel, the network service migration strategies of different time slots affect each other, making it difficult to meet the long-term overhead constraints. For this reason, the embodiment of the present invention decouples the above problem into an independent time slot strategy optimization problem through the Lyapunov theory to ensure the stability of the system.

[0038] Step S112, solving the independent time slot strategy optimization function to obtain the network service migration strategy of all users in the emergency communication network in each time slot.

[0039] Among them, the network service migration strategy includes one of the following: no network service migration, network service migration via satellite, and network service migration via drone base station.

[0040] After obtaining the independent time slot strategy optimization function, the embodiment of the present invention does not specifically limit the solution method of the independent time slot strategy optimization function. The user can choose according to actual needs, for example, the reinforcement learning median iteration method, the strategy iteration method, etc. After the function is solved, , , , .

[0041] The embodiment of the present invention proposes an unmanned cluster-assisted multi-strategy network service dynamic migration method. The method considers multiple network service migration strategies to optimize the network service migration problem in the emergency communication network. After constructing the long-term network service optimization function of all users in the emergency communication network, since the long-term network service optimization function cannot be directly solved, the long-term network service optimization function is further decoupled into an independent time slot strategy optimization function through Lyapunov theory and then solved, thereby obtaining the network service migration strategy of all users in the emergency communication network in each time slot, so as to provide scientific guidance for the network service migration of users in the emergency communication network, thereby improving the user service quality and ensuring the stability of the system.

[0042] In the embodiment of the present invention, the long-term network service optimization function is expressed as: ;in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service.

[0043] , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station.

[0044] , , represents the first migration cost, represents the second migration overhead; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

[0045] In an optional implementation, the above step S110 decouples the long-term network service optimization function into an independent time slot strategy optimization function based on Lyapunov theory, and specifically includes the following steps: Step S1101, based on the overhead cost constraint of the long-term network service optimization function, construct a Lyapunov function and an energy queue update function of a target user in a target time slot.

[0046] When decoupling the problem, the embodiment of the present invention first introduces the energy queue ,in, represents the energy queue update function of user k in time slot t, that is, the queue backlog of user k in time slot t. The initial queue backlog is The energy queue update function defined in the embodiment of the present invention is expressed as: ;in, represents the energy queue update function of user k in time slot t+1, and the average overhead threshold of all users in a single time slot It can be regarded as the queue service rate. When the energy queue is stable, the long-term average overhead will not exceed , satisfying the overhead cost constraint. Then, the Lyapunov function is introduced. The embodiment of the present invention defines the Lyapunov function as: .

[0047] Step S1102, based on the Lyapunov function and the energy queue update function, calculate the upper bound of the Lyapunov drift function.

[0048] According to the Lyapunov function, we need to ensure that the queue backlog of each user is very small. To ensure the stability of the energy queue, the Lyapunov drift function is introduced. , To represent the growth of the Lyapunov function from time slot t to time slot t + 1. The following introduces the calculation method of the upper bound of the Lyapunov drift function.

[0049] Update function based on energy queue You can get: , and then we can get .

[0050] Further combined with the Lyapunov drift function above , and the Lyapunov function , can be obtained ,in, , that is, the upper bound of the Lyapunov drift function is .

[0051] Step S1103, taking the sum of the network service objective optimization functions of all users in the target time slot as the penalty term of the Lyapunov drift function, and constructing a Lyapunov drift plus penalty objective function; wherein the network service objective optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function.

[0052] Specifically, based on the Lyapunov drift function, the conditional Lyapunov drift function can be defined as , It represents the expectation of the backlog of all energy queues in time slot t, which is determined by the network service migration strategy of the current time slot t. In order to minimize the long-term average overhead of the system while ensuring the stability of the energy queue, the Lyapunov drift plus penalty term is introduced to obtain the objective function: ,in, represents the penalty coefficient, .

[0053] Step S1104, solving the upper bound of the objective function based on the upper bound of the Lyapunov drift function.

[0054] It is known that the upper bound of the Lyapunov drift function is expressed as: ;and , from which we can get . That is, the upper bound of the objective function is: .

[0055] Step S1105, removing the constant term in the upper bound of the objective function to obtain an independent time slot strategy optimization function.

[0056] While minimizing the queue backlog, by removing the constant term in the upper bound of the objective function, the long-term network service optimization problem can be transformed into an independent time slot strategy optimization problem, and an independent time slot strategy optimization function is obtained. In the embodiment of the present invention, the independent time slot strategy optimization function is expressed as: .

[0057] In summary, the method proposed in the embodiment of the present invention models the network service migration problem in the emergency communication network as a multi-strategy service migration problem for the first time, and considers a variety of service migration methods to optimize the service migration problem. The ability to use the complementary advantages of satellite and drone base station communications improves the probability of successful service migration. The multi-strategy service migration problem is a long-term optimization goal and cannot be solved directly. Moreover, the random arrival of services and the time-varying state of the wireless channel cause the migration strategies of different time slot systems to affect each other, making it difficult to meet long-term overhead constraints. For this reason, the embodiment of the present invention decouples the original problem into an independent time slot strategy optimization problem through the Lyapunov theory to ensure the stability of the system. While minimizing the optimization function value, the energy queue backlog can be reduced to meet the long-term average energy consumption constraint. In other words, the optimal network service migration strategy can be obtained by solving this problem.

[0058] In order to verify the performance of the method provided by the embodiment of the present invention, the effects of different network service migration strategies on the service uninterrupted rate were compared. Figure 3 To provide a comparison chart of the probability of uninterrupted service for different types of network service migration strategies.

[0059] If there are K users receiving network services in T time slots, when the delay is greater than the service delay threshold , then the user service is interrupted, and the number of users whose service is interrupted is , the probability of no interruption is defined as: .

[0060] In emergency communication networks, service migration can be carried out through a variety of means, such as drones and satellites. Here we mainly analyze the benefits of using multiple means to migrate services compared to using a single means to migrate services. The benefits here are mainly described by whether the service is interrupted, and the service delay threshold is not exceeded. Defined as no interruption of service. Figure 3 The service delay threshold Set it to 0.5s, 1s, and 1.5s to analyze the relationship between latency and communication means. {0, 1, 2} indicates the situation where drone migration and satellite migration coexist; {0, 1} means that only the drone migration method can be used; {0, 2} indicates that only satellite migration mode can be used.

[0061] Figure 3 The horizontal axis is the number of users who are currently using the service, and the vertical axis is the probability of uninterrupted service. The purpose of network service migration is to ensure that all network services can continue to operate without interruption. Figure 3 As shown, when the service delay threshold Increases, the probability of uninterrupted service becomes higher when only drones are used for service migration. This is because drones cannot provide service migration for users in every period of time, so waiting for drones will also be regarded as a delay. When satellite migration is available, the probability of uninterrupted service is more closely related to the number of users. This is because satellites can migrate services under any circumstances, but their resources are very limited. Once the number of users increases, it is difficult to ensure that the service will not be interrupted. Compared with the single-means service migration method, the probability of uninterrupted service in the case of coexistence of drone migration and satellite migration increased by 37.82%. In other words, the experiment proves that multi-strategy service migration in emergency communication networks can more effectively ensure service continuity than single service migration.

[0062] Embodiment 2 An embodiment of the present invention also provides an unmanned cluster-assisted multi-strategy network service dynamic migration device, which is mainly used to execute the unmanned cluster-assisted multi-strategy network service dynamic migration method provided in the above-mentioned embodiment one. The following is a specific introduction to the unmanned cluster-assisted multi-strategy network service dynamic migration device provided in the embodiment of the present invention.

[0063] Figure 4 A functional module diagram of an unmanned cluster-assisted multi-strategy network service dynamic migration device provided by an embodiment of the present invention, such as Figure 4 As shown, the device mainly includes: an acquisition module 11, a first construction module 12, a second construction module 13, a third construction module 14, a decoupling module 15, and a solution module 16, wherein: The acquisition module 11 is used to obtain the first migration overhead, the second migration overhead and the network service data volume of each user in the emergency communication network; wherein the first migration overhead represents the overhead of the user migrating the network service through the drone base station, and the second migration overhead represents the overhead of the user migrating the network service through the satellite.

[0064] The first construction module 12 is used to construct an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot based on the first migration overhead and the second migration overhead; wherein the target user represents any user in the emergency communication network; and the target time slot represents any time slot in the long-term network service duration.

[0065] The second construction module 13 is used to construct a service delay calculation function for the target user in the target time slot based on the network service data volume of the target user and the transmission bandwidth of each data transmission link in the emergency communication network.

[0066] The third construction module 14 is used to construct a long-term network service optimization function for all users in the emergency communication network based on the overhead cost calculation function and the service delay calculation function under the overhead cost constraint and the service delay constraint.

[0067] The decoupling module 15 is used to decouple the long-term network service optimization function into an independent time slot strategy optimization function based on Lyapunov theory.

[0068] The solution module 16 is used to solve the independent time slot strategy optimization function to obtain the network service migration strategy of all users in the emergency communication network in each time slot; wherein the network service migration strategy includes one of the following: no migration of network service, migration of network service via satellite, and migration of network service via drone base station.

[0069] The embodiment of the present invention proposes an unmanned cluster-assisted multi-strategy network service dynamic migration device, which takes into account multiple network service migration strategies to optimize the network service migration problem in the emergency communication network. After constructing a long-term network service optimization function for all users in the emergency communication network, since the long-term network service optimization function cannot be directly solved, the long-term network service optimization function is further decoupled into an independent time slot strategy optimization function through Lyapunov theory and then solved, thereby obtaining the network service migration strategy for all users in the emergency communication network in each time slot, so as to provide scientific guidance for the network service migration of users in the emergency communication network, thereby improving the user service quality and ensuring the stability of the system.

[0070] Optionally, the decoupling module 15 is specifically used for: Based on the overhead cost constraint of the long-term network service optimization function, the Lyapunov function and the energy queue update function of the target user in the target time slot are constructed.

[0071] Based on the Lyapunov function and the energy queue update function, the upper bound of the Lyapunov drift function is calculated.

[0072] The sum of the network service objective optimization functions of all users in the target time slot is used as the penalty term of the Lyapunov drift function to construct the Lyapunov drift plus penalty objective function; among which, the network service objective optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function.

[0073] The upper bound of the objective function is solved based on the upper bound of the Lyapunov drift function.

[0074] The constant term in the upper bound of the objective function is removed to obtain the independent time slot strategy optimization function.

[0075] Optionally, the long-term network service optimization function is expressed as: ; in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service.

[0076] , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station.

[0077] , , represents the first migration cost, represents the second migration overhead; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

[0078] Optionally, the energy queue update function is expressed as: ;in, represents the energy queue update function of user k in time slot t+1, represents the energy queue update function of user k in time slot t.

[0079] The Lyapunov function is expressed as: .

[0080] Optionally, the independent time slot strategy optimization function is expressed as: ; represents the penalty coefficient, .

[0081] Embodiment 3 See also Figure 5An embodiment of the present invention provides an electronic device, which includes: a processor 60, a memory 61, a bus 62 and a communication interface 63, the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is used to execute an executable module stored in the memory 61, such as a computer program.

[0082] The memory 61 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0083] The bus 62 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0084] Among them, the memory 61 is used to store programs, and the processor 60 executes the program after receiving the execution instruction. The method executed by the device defined by the process disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 60 or implemented by the processor 60.

[0085] The processor 60 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 60. The above processor 60 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to be executed, or the hardware and software modules in the decoding processor can be executed. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 61, and the processor 60 reads the information in the memory 61 and completes the steps of the above method in combination with its hardware.

[0086] A computer program product of an unmanned cluster-assisted multi-strategy network service dynamic migration method and apparatus provided in an embodiment of the present invention includes a computer-readable storage medium storing a non-volatile program code executable by a processor. The instructions included in the program code can be used to execute the method in the previous method embodiment. The specific implementation can be found in the method embodiment and will not be repeated here.

[0087] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0088] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0089] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0090] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the product of the invention is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific position, be constructed and operated in a specific position, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", "third", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0091] In addition, the terms "horizontal", "vertical", "overhanging" and the like do not mean that the components are required to be absolutely horizontal or overhanging, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", and does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0092] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0093] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-strategy network service dynamic migration method assisted by unmanned cluster, characterized in that: include: Obtain a first migration cost, a second migration cost, and a network service data volume of each user in the emergency communication network; wherein the first migration cost represents the cost of the user performing network service migration through the drone base station, and the second migration cost represents the cost of the user performing network service migration through the satellite; Based on the first migration overhead and the second migration overhead, an overhead cost calculation function for the target user to transfer between adjacent ground base stations in a target time slot is constructed; wherein the target user represents any user in the emergency communication network; and the target time slot represents any time slot in the long-term network service duration; Based on the network service data volume of the target user and the transmission bandwidth of each data transmission link in the emergency communication network, construct a service delay calculation function of the target user in the target time slot; Under the overhead cost constraint and the service delay constraint, based on the overhead cost calculation function and the service delay calculation function, construct a long-term network service optimization function for all users in the emergency communication network; Based on Lyapunov theory, the long-term network service optimization function is decoupled into independent time slot strategy optimization functions; Solve the independent time slot strategy optimization function to obtain the network service migration strategy of all users in the emergency communication network in each time slot; wherein the network service migration strategy includes one of the following: no migration of network service, migration of network service via satellite, and migration of network service via drone base station.

2. The unmanned cluster-assisted multi-strategy network service dynamic migration method according to claim 1, characterized in that: Based on Lyapunov theory, the long-term network service optimization function is decoupled into independent time slot strategy optimization functions, including: Based on the overhead cost constraint of the long-term network service optimization function, constructing a Lyapunov function and an energy queue update function of a target user in a target time slot; Based on the Lyapunov function and the energy queue update function, calculating the upper bound of the Lyapunov drift function; The sum of the network service objective optimization functions of all users in the target time slot is used as the penalty term of the Lyapunov drift function to construct a Lyapunov drift plus penalty objective function; wherein the network service objective optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function; Solving the upper bound of the objective function based on the upper bound of the Lyapunov drift function; The independent time slot strategy optimization function is obtained by removing the constant term in the upper bound of the objective function.

3. The unmanned cluster-assisted multi-strategy network service dynamic migration method according to claim 2, characterized in that: The long-term network service optimization function is expressed as: ; in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service; , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station; , , represents the first migration cost, represents the second migration cost; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

4. The unmanned cluster-assisted multi-strategy network service dynamic migration method according to claim 3 is characterized in that: The energy queue update function is expressed as: ;in, represents the energy queue update function of user k in time slot t+1, represents the energy queue update function of user k in time slot t; The Lyapunov function is expressed as: .

5. The unmanned cluster-assisted multi-strategy network service dynamic migration method according to claim 4, characterized in that: The independent time slot strategy optimization function is expressed as: ; represents the penalty coefficient, .

6. An unmanned cluster-assisted multi-strategy network service dynamic migration device, characterized in that: include: An acquisition module is used to acquire a first migration cost, a second migration cost, and a network service data volume of each user in the emergency communication network; wherein the first migration cost represents the cost of the user performing network service migration through the drone base station, and the second migration cost represents the cost of the user performing network service migration through the satellite; A first construction module is used to construct an overhead cost calculation function for a target user to transfer between adjacent ground base stations in a target time slot based on the first migration overhead and the second migration overhead; wherein the target user represents any user in the emergency communication network; and the target time slot represents any time slot in the long-term network service duration; The second construction module is used to construct a service delay calculation function of the target user in the target time slot based on the network service data volume of the target user and the transmission bandwidth of each data transmission link in the emergency communication network; A third construction module is used to construct a long-term network service optimization function for all users in the emergency communication network based on the overhead cost calculation function and the service delay calculation function under the overhead cost constraint and the service delay constraint; A decoupling module, used for decoupling the long-term network service optimization function into an independent time slot strategy optimization function based on Lyapunov theory; A solution module is used to solve the independent time slot strategy optimization function to obtain the network service migration strategy of all users in the emergency communication network in each time slot; wherein the network service migration strategy includes one of the following: no migration of network service, migration of network service via satellite, and migration of network service via drone base station.

7. The unmanned cluster-assisted multi-strategy network service dynamic migration device according to claim 6, characterized in that: The decoupling module is specifically used for: Based on the overhead cost constraint of the long-term network service optimization function, constructing a Lyapunov function and an energy queue update function of a target user in a target time slot; Based on the Lyapunov function and the energy queue update function, calculating the upper bound of the Lyapunov drift function; The sum of the network service objective optimization functions of all users in the target time slot is used as the penalty term of the Lyapunov drift function to construct a Lyapunov drift plus penalty objective function; wherein the network service objective optimization function of the target user is the weighted sum of the overhead cost calculation function and the service delay calculation function; Solving the upper bound of the objective function based on the upper bound of the Lyapunov drift function; The independent time slot strategy optimization function is obtained by removing the constant term in the upper bound of the objective function.

8. The unmanned cluster-assisted multi-strategy network service dynamic migration device according to claim 7, characterized in that: The long-term network service optimization function is expressed as: ; in, , Indicates the preset delay weight, represents the service delay calculation function of user k in time slot t, represents the cost calculation function of user k moving from ground base station i to ground base station j in time slot t; , , , represents the network service migration strategy for user k moving from ground base station i to ground base station j in time slot t, represents the total number of users in the emergency communication network, represents the total number of ground base stations in the emergency communication network, Indicates the duration of long-term network service; , represents the Dirac function, represents the amount of network service data for user k, It represents the transmission bandwidth between the ground and the satellite. represents the transmission bandwidth between user k and the drone base station, represents the transmission bandwidth between user k and the ground base station; , , represents the first migration cost, represents the second migration cost; represents the overhead cost constraint, represents the service delay constraint, Indicates the average overhead threshold of all users in a single time slot. Indicates the service delay threshold of the user.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, wherein: When the processor executes the computer program, the unmanned cluster-assisted multi-strategy network service dynamic migration method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the unmanned cluster-assisted multi-strategy network service dynamic migration method according to any one of claims 1 to 5 is implemented.

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