Distributed container simulation method and device, equipment, storage medium and product

Abstract

The invention discloses a distributed container simulation method and device, equipment, a storage medium and a product, and relates to the technical field of data processing.The distributed container simulation method comprises the steps that available host machines are determined based on the computing resource occupation amount of all the host machines; the available host machines are host machines whose computing resource occupation amount does not reach an upper limit; determining a host machine load index of the available host machine; determining a load balancing index of the hardware platform based on the host machine load index; according to the computing resource consumption of the to-be-simulated container, determining a target host machine which enables the load balancing index to be the lowest in the available host machines; and simulating and running the to-be-simulated container through the target host machine. According to the method, dynamic allocation of host machine resources is realized in the unified deployment stage before simulation is started, and the container deployment condition can be dynamically adjusted according to different requirements and real-time container load conditions in the simulation stage, so that a platform can ensure efficient execution of simulation tasks while maximizing the resource utilization rate of the container host machine.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a distributed container simulation method, device, equipment, storage medium and product. Background Art

[0002] 3GPP NTN protocol simulation mainly conducts communication standard and technology simulation for space communication scenarios such as satellites and high-altitude platforms. These technologies play a key role in evaluating the performance of satellite network communication systems, optimizing network deployment strategies, and integrating space-ground and air-ground communication technologies.

[0003] The following are two major challenges facing the existing 3GPP NTN communication simulation. The first is the requirements for hardware platform performance for high-complexity communication system simulation: Since satellite communication has the characteristics of propagation delay, Doppler effect and channel characteristic changes, and also involves the integration of ground network and satellite network, 3GPP NTN communication system simulation is usually very complex and requires strong simulation hardware performance; the second is the management of distributed computing resources: when it is necessary to simulate thousands of users or multiple satellite constellations, 3GPP NTN communication simulation will require more computing resources, and the switching of user terminals between satellites will also bring about changes in computing resource requirements, so computing resource allocation and elastic expansion and contraction strategies need to be flexible and efficient enough. Summary of the invention

[0004] The main purpose of this application is to provide a distributed container simulation method, device, equipment, storage medium and product, aiming to solve the technical problems of existing 3GPP NTN communication system simulation, which is usually very complex, requires strong simulation hardware performance, and has problems with the allocation and elastic expansion of computing resources.

[0005] To achieve the above objectives, the present application proposes a distributed container simulation method, which includes:

[0006] Determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit;

[0007] Determining a host load index of the available host;

[0008] Determining a load balancing index of a hardware platform based on the host machine load index;

[0009] Determine a target host machine with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated;

[0010] The target host machine simulates and runs the container to be simulated.

[0011] In one embodiment, the step of determining, from among the available hosts, a target host machine that has the lowest load balancing index according to the computing resource consumption of the container to be simulated, comprises:

[0012] Sampling the container to be simulated to determine resource occupancy per second of the container to be simulated;

[0013] Determine the computing resource consumption required by the container to be simulated based on the resource occupancy per second;

[0014] Predicting a predicted load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption;

[0015] A target host machine having the lowest predicted load balancing index is determined from among the available host machines.

[0016] In one embodiment, after the step of simulating and running the container to be simulated by the target host machine, the method further includes:

[0017] If the computing resource usage of the target host machine has reached an upper limit after the container to be simulated is run, the target host machine is marked as an unavailable host machine.

[0018] In one embodiment, after the step of simulating and running the container to be simulated by the target host machine, the method further includes:

[0019] During the simulation operation, if there is a container terminal switching event, the container to be simulated in the target host is destroyed;

[0020] Return to the step of predicting the load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption, so as to find a new target host machine.

[0021] In one embodiment, the method further comprises:

[0022] During the synchronization period, network topology update messages are sent to all containers in the simulation process;

[0023] Receive a completion message generated by each container based on the network topology update message;

[0024] When completion messages fed back by all containers are received, the step of initiating network topology update messages to all containers in the simulation running process is performed in the next synchronization cycle.

[0025] In one embodiment, the method further comprises:

[0026] At the end of the simulation running process, destroying the container to be simulated in the target host machine;

[0027] Update the computing resource usage of the target host.

[0028] In addition, to achieve the above purpose, the present application also proposes a distributed container simulation device, the distributed container simulation device comprising:

[0029] A computing resource determination module, used to determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit;

[0030] A load index determination module, used to determine the host load index of the available host;

[0031] A load balancing determination module, used to determine a load balancing index of a hardware platform based on the host machine load index;

[0032] A target host determination module, configured to determine a target host with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated;

[0033] The container simulation running module is used to simulate and run the container to be simulated through the target host machine.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes a distributed container simulation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the distributed container simulation method described above.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the distributed container simulation method described above are implemented.

[0036] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, wherein the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the distributed container simulation method described above are implemented.

[0037] One or more technical solutions proposed in this application have at least the following technical effects:

[0038] The present application determines available hosts based on the computing resource usage of each host; available hosts are hosts whose computing resource usage has not reached the upper limit; determines the host load index of the available hosts; determines the load balancing index of the hardware platform based on the host load index; determines the target host with the lowest load balancing index among the available hosts according to the computing resource consumption of the container to be simulated; and simulates and runs the container to be simulated through the target host. Since the target host that makes the load balancing index of the hardware platform meet the preset conditions is determined based on the computing resource usage of the available hosts and the computing resource consumption of the container to be simulated, not only the dynamic allocation of host resources is realized in the unified deployment stage before the simulation starts, but also the container deployment location and quantity can be dynamically adjusted according to the different requirements of the simulation stage and the real-time container load status, so that the platform can maximize the resource utilization of the container host while ensuring the efficient execution of the simulation task. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0040] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0041] Figure 1 A schematic diagram of a process flow provided for the first embodiment of the distributed container simulation method of the present application;

[0042] Figure 2 A schematic diagram of a process flow provided for the second embodiment of the distributed container simulation method of the present application;

[0043] Figure 3 A schematic diagram of a process flow provided for Embodiment 3 of the distributed container simulation method of the present application;

[0044] Figure 4 A schematic diagram of simulation timing in one implementation of the distributed container simulation method of the present application;

[0045] Figure 5 This is a schematic diagram of the module structure of the distributed container simulation device according to an embodiment of the present application;

[0046] Figure 6 This is a schematic diagram of the device structure of the hardware operating environment involved in the distributed container simulation method in the embodiment of the present application.

[0047] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0048] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0049] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0050] This application designs a set of key technology simulation platforms for 3GPP NTN protocols. Based on the convenience and flexibility of container technology, in order to meet the simulation requirements of 3GPP NTN complex protocols and technologies in low-orbit satellite network scenarios, through the centralized management of container resources, intelligent container orchestration and time synchronization between containers are realized, and a flexible and customizable distributed simulation system including network elements such as terminals, base stations and core networks is built, providing all-round simulation capabilities of physical layer technology, wireless resource management layer algorithms, network protocols, etc., to support the simulation research of next-generation advanced technologies such as 3GPP NTN network construction technology, key algorithms and innovative applications.

[0051] Specifically, this application uses containers to simulate network nodes in satellite-to-ground communications, and develops a container simulation scheduling center to achieve efficient management of container resources. In large-scale satellite-to-ground communication simulation scenarios, the container simulation scheduling center can manage the running locations and life cycles of a large number of containers on the hardware platform, so that the simulation computing load on each server is balanced; it can also intervene in the simulation process by updating the network topology data of the container to achieve simulation time synchronization of each container.

[0052] Compared with the existing technology, this application realizes effective resource management and isolation, flexible expansion capabilities, environmental consistency guarantee and efficient iteration process through distributed container scheduling management. While expanding the simulation scale, it ensures the flexible allocation of computing resources and provides a research and experimental foundation for complex simulations such as 3GPP NTN communication protocols.

[0053] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a computer, a server, etc., or an electronic device, a virtual device, etc. that can realize the above functions. The following uses a distributed container simulation device (hereinafter referred to as the simulation device) as an example to illustrate this embodiment and the following embodiments.

[0054] Based on this, the present application embodiment provides a distributed container simulation method, referring to Figure 1 , Figure 1This is a flow chart of the first embodiment of the distributed container simulation method of the present application.

[0055] In this embodiment, the distributed container simulation method includes steps S10 to S50:

[0056] Step S10, determining available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit;

[0057] It should be noted that, in the embodiment of the present application, each simulation process may include at least the following three types of containers:

[0058] CN: core network container. There is only one in each simulation process. This core network container is responsible for processing the network entry and exit of BS and UE;

[0059] BS: Satellite container. You can set different or the same number of containers according to the actual scale and scenario. The satellite container is mainly responsible for implementing the protocol stack function on the BS side.

[0060] UE: User terminal container, which can be set to different or the same number according to the actual scale and scenario. The user terminal container is mainly responsible for implementing the UE side protocol stack function.

[0061] In addition, it should be noted that in order to realize distributed container simulation, the embodiment of the present application provides a container simulation scheduling center (Container-based Simulation Scheduler, CSS). The container simulation scheduling center in the present application is not a container, but an application deployed outside the hardware platform running the container. In the simulation device of the present application, the container simulation scheduling center can be deployed, and the distributed container simulation method of the present application can be implemented through the container simulation scheduling center.

[0062] It needs to be explained that the relationship between the container simulation scheduling center and each container in this application is a server-client relationship, in which the container simulation scheduling center is the server and the container is the client. The container simulation scheduling platform in this application implements container lifecycle management and efficient scheduling of host computing resources based on a set of intelligent container orchestration strategies and the Docker API integrated therein. Between the container and the container simulation scheduling center, a set of TCP-based simulation control protocols is followed, which is used for operations such as the container simulation scheduling center sending network topology data and the container reporting status; the time synchronization mechanism implemented based on the simulation control protocol can also control the entire simulation process, so that the simulation results are accurate and reliable.

[0063] It should be noted that in an embodiment of the present application, the hardware platform may include several host machines, and the container may run on several host machines of the hardware platform. Since the container simulation scheduling center supports multiple simulations in parallel, the computing resource occupancy of each host machine is different before each simulation starts. Therefore, the container simulation scheduling center in the embodiment of the present application can decide what kind of container and how many containers to run on each host machine according to a customized and intelligent container orchestration strategy. In an embodiment of the present application, the container orchestration strategy can be designed based on a greedy algorithm, and its basic idea is to allocate containers to a certain host machine in the order of the required main computing resources from most to least, so that the load balancing index of all hosts is optimal after a single container is allocated.

[0064] It is understandable that computing resources can generally include CPU, memory, bandwidth, hard disk, etc., sorted from high to low in importance. The computing resources that a container needs to consume are a value obtained through testing.

[0065] It is understandable that in the embodiment of the present application, the computing resource usage of each host machine in the hardware platform can be calculated, and then the host machine whose computing resource usage does not reach the upper limit in the hardware platform is determined, that is, the available host machine. When determining the available host machine, the computing resource load of each available host machine can be evaluated from multiple dimensions to determine the load index of the available host machine.

[0066] Specifically, in step S20, a host load index of the available host is determined.

[0067] It can be understood that the above host load index is a parameter used to evaluate the computing resource load of the host.

[0068] In the embodiment of the present application, the computing resource load of the host machine can be evaluated from multiple dimensions through the host load index. Specifically, the host load index can be defined as:

[0069]

[0070] Among them, U r Calculate the occupancy rate of resource r for the host, is the average occupancy rate of computing resource r on the current hardware platform. From the above formula, we can see that if the average occupancy rate of a computing resource is higher, its weight will be higher when evaluating the host load index.

[0071] It should be noted that when determining the host load index of the available host machines, the load balancing index of the hardware platform can be calculated based on the host load index of each available host machine. The load balancing index can represent the coefficient of variation of the SLI (host load index) of each host machine. That is, step S30 determines the load balancing index of the hardware platform based on the host load index.

[0072] It can be understood that the above load balancing index is a parameter used to measure the load balancing degree of the hardware platform. The more balanced the load of the hardware platform is, the smaller the value of the load balancing index is; the more unbalanced the load is, the larger the value of the load balancing index is. Specifically, the calculation method of the load balancing index LBI can be as follows:

[0073]

[0074] Among them, μ(SLI) is used to represent the average value of the host load index, and σ(SLI) is used to represent the standard deviation of the host load index.

[0075] Step S40, determining a target host machine with the lowest load balancing index from among the available host machines according to the computing resource consumption of the container to be simulated;

[0076] Step S50: emulate and run the container to be simulated through the target host machine.

[0077] It should be noted that after determining the computing resource consumption of the container to be simulated O r When the load balance index of each available host is determined, the target host with the lowest load balance index can be determined based on the host load index of each available host and the load balance index of the hardware platform. By deploying the container to be simulated to the target host, the simulation operation of the container to be simulated can be realized.

[0078] The embodiment of the present application determines available hosts based on the computing resource usage of each host; available hosts are hosts whose computing resource usage does not reach the upper limit; host load indexes of available hosts are determined; load balancing indexes of hardware platforms are determined based on host load indexes; target hosts with the lowest load balancing index are determined among available hosts based on computing resource consumption of containers to be simulated; and containers to be simulated are simulated and run through the target host. Since the target host that makes the load balancing index of the hardware platform meet the preset conditions is determined based on the computing resource usage of available hosts and the computing resource consumption of containers to be simulated, not only is dynamic allocation of host resources achieved in the unified deployment phase before the simulation begins, but also the container deployment location and quantity can be dynamically adjusted according to different requirements of the simulation phase and the real-time container load status, so that the platform can maximize the resource utilization of the container host while ensuring the efficient execution of simulation tasks.

[0079] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 2 The step of determining the target host machine with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated comprises:

[0080] Step S41, sampling the container to be simulated to determine the resource occupancy of the container to be simulated per second;

[0081] Step S42, determining the computing resource consumption required by the container to be simulated based on the resource occupancy per second;

[0082] In one implementation of the embodiment of the present application, the above-mentioned process of step S41 and step S42 can be performed before the method is executed, or can be determined during the method execution, and the embodiment of the present application does not limit this. Specifically, in order to determine the computing resource consumption of the container to be simulated, a test scenario in the embodiment of the present application can be: manually start a simulation test dedicated to evaluation, so that each host machine contains only one container, ensuring that the computing resources available to the container are sufficient.

[0083] It should be noted that if a simulation lasts T seconds and there are K containers of a certain type, then the calculation method for the computing resource consumption of such containers can be as follows:

[0084] Each container is sampled every second to calculate the current usage of various computing resources O r,k,t ,r∈{cpu,mem,bw,hd…}; After the simulation, the average computing resource usage O of the K container individuals of each type of container during the simulation process is calculated r,k , and then for all individual O r,k Find the average, and you get the computing resource consumption of the container O r :

[0085]

[0086] Step S43, predicting a predicted load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption;

[0087] Step S44, determining a target host machine with the lowest predicted load balancing index among the available host machines.

[0088] In one implementation of the embodiment of the present application, since the computing resource consumption of the container to be simulated and the computing resource occupancy in each available host machine can be obtained, a prediction can be made based on the computing resource consumption of the container to be simulated and the computing resource occupancy in each available host machine to determine the predicted load balancing index of the hardware platform when the container to be simulated is running in each available host machine.

[0089] In one implementation of the embodiment of the present application, the container to be simulated can be deployed with the minimum load balancing index as the target. Specifically, the predicted load balancing index of the corresponding hardware platform can be predicted when the container to be simulated is deployed in each available host. By comparing the predicted load balancing indexes, the available host corresponding to the minimum predicted load balancing index is used as the target host, and the container to be simulated can be deployed to the target host.

[0090] It should be noted that when determining the target host and deploying the container to be simulated to the target host, the resource consumption of the container to be simulated can be added to the computing resource occupancy of the target host, so as to update the remaining available resources of the target host. When the computing resource occupancy of the host exceeds the upper limit, the container simulation scheduling center can remove the target host from the set of available hosts, thereby avoiding the target host from being assigned too many containers, thereby improving the container simulation efficiency of the hardware platform. Specifically, after the step of simulating and running the container to be simulated through the target host, it also includes:

[0091] If the computing resource usage of the target host machine has reached an upper limit after the container to be simulated is run, the target host machine is marked as an unavailable host machine.

[0092] It is understandable that when a host is marked as an unavailable host, it will be removed from the set of available hosts, and will not be considered for allocation to the unavailable host in subsequent container allocation.

[0093] In one implementation of an embodiment of the present application, when a user terminal switching event occurs during the simulation process, the user terminal container in the user corresponding to the terminal will be destroyed first, and at the same time, the computing resource consumption of the user terminal container will be subtracted from the computing resource occupancy of the host machine where it is located.

[0094] Furthermore, the CSS can search the hardware platform to determine the target host machine that can minimize the LBI, and add the computing resource usage of the target host machine to the computing resource consumption of the container, and restart the user terminal container in the target host machine; finally, re-send the network topology data to the new user terminal container, so that the terminal migrates from the network plane of the original host machine to the network plane after the switch. That is, after the step of simulating and running the container to be simulated through the target host machine, it also includes:

[0095] During the simulation operation, if there is a container terminal switching event, the container to be simulated in the target host is destroyed;

[0096] Return to the step of predicting the load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption, so as to find a new target host machine.

[0097] The embodiment of the present application determines the resource occupancy of the container to be simulated per second by sampling the container to be simulated; determines the computing resource consumption required by the container to be simulated based on the resource occupancy per second; predicts the predicted load balancing index of the hardware platform when the container to be simulated is running in each available host machine based on the computing resource consumption; and determines the target host machine with the lowest predicted load balancing index among the available host machines. Since the computing resource consumption of the container to be simulated is determined, and then the target host machine of the container to be simulated is determined, the simulation processing efficiency of the hardware platform is improved.

[0098] Based on the first embodiment and / or the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those of the above-mentioned first embodiment and / or the second embodiment can refer to the above introduction, and will not be repeated later. Figure 3 In an embodiment of the present application, the method further includes:

[0099] Step S100, initiating a network topology update message to all containers in the simulation running process within the synchronization period;

[0100] Step S200, receiving a completion message generated by each container based on the network topology update message;

[0101] Step S300, when completion messages fed back by all containers are received, the step of initiating a network topology update message to all containers in the simulation running process is executed in the next synchronization cycle.

[0102] It should be noted that the method based on the above embodiment of the present application can balance the load of each host machine in the hardware platform as much as possible when deploying the container, but if the host machine has performance loss, or the computing amount of the container fluctuates, the simulation time and simulation progress of each container may be out of sync. This also means that the network topology of the satellite and the terminal may be out of sync, which may eventually cause problems such as errors when the terminal switches, affecting the accuracy of the simulation results.

[0103] In the embodiment of the present application, considering the problem that the above-mentioned time asynchrony will lead to topology asynchrony, a method based on topology control time is designed. Specifically, the CSS initiates a network topology update request to the container running in simulation in each target host machine at a certain simulation synchronization period (such as 3s, 4s, etc.).

[0104] It is understandable that upon receiving a network topology update request, each container in the simulation process can perform its own simulation logic processing and send a generated completion message (done message) to the CSS when the simulation logic processing is completed. Obviously, the container with a fast simulation time and a fast simulation logic processing will enter the waiting state. For details, please refer to Figure 4 Simulation timing.

[0105] like Figure 4 As shown, in one implementation of an embodiment of the present application, there are three containers in the simulation operation process at the same time, namely container 1, container 2 and container 3. For the simulation operation process, it can be divided into several synchronization cycles on the time axis. In each synchronization cycle, the CSS can first send a network topology update request (including topology data) to each container so that each container starts to perform simulation (simulating) logic processing. At the end of the simulation logic processing process of a container, a completion message can be returned to the CSS, and the idle state can be entered to wait for the simulation logic processing of other containers to be completed. When the simulation logic processing of all containers is completed (that is, when the CSS receives the completion message fed back by all containers), the next synchronization cycle is entered, and the step of initiating a network topology update message to the containers in all simulation operations is executed in the next synchronization cycle.

[0106] In one implementation of the embodiment of the present application, when the simulation running process in the target host machine ends, the container to be simulated in the target host machine can be destroyed and its computing resource occupancy can be updated to achieve an update of the load balancing index in the hardware platform.

[0107] The embodiment of the present application initiates a network topology update message to all containers in the simulation running process within the synchronization period; receives a completion message generated by each container based on the network topology update message; and upon receiving the completion message fed back by all containers, executes the step of initiating a network topology update message to all containers in the simulation running process in the next synchronization period. Through the above scheme of the present application, a strict time synchronization mechanism is realized. The present application indirectly realizes simulation time synchronization by controlling the network topology update, which makes the design of the simulation system more concise and reduces the time complexity of time synchronization. At the same time, through the time synchronization mechanism of the present application, problems that may be encountered when synchronizing with the real time, such as time offset accumulation, network delay, etc., are avoided, so that the system can maintain good simulation result accuracy even when the network conditions fluctuate, thereby improving the robustness of the system.

[0108] This application also provides a distributed container simulation device, please refer to Figure 5 , the distributed container simulation device comprises:

[0109] A computing resource determination module 10, configured to determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit;

[0110] A load index determination module 20, configured to determine a host load index of the available host;

[0111] A load balancing determination module 30, configured to determine a load balancing index of a hardware platform based on the host machine load index;

[0112] A target host determination module 40, configured to determine a target host with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated;

[0113] The container simulation running module 50 is used to simulate and run the container to be simulated through the target host machine.

[0114] The distributed container simulation device provided by the present application adopts the distributed container simulation method in the above embodiment, which can solve the technical problems that the existing 3GPP NTN communication system simulation is usually very complex, requires strong simulation hardware performance, and has problems with the allocation and elastic expansion of computing resources. Compared with the prior art, the beneficial effects of the distributed container simulation device provided by the present application are the same as the beneficial effects of the distributed container simulation method provided by the above embodiment, and the other technical features in the distributed container simulation device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0115] The present application provides a distributed container simulation device, which includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the distributed container simulation method in the above-mentioned embodiment 1.

[0116] Reference below Figure 6 , which shows a schematic diagram of the structure of a distributed container simulation device suitable for implementing the embodiment of the present application. The distributed container simulation device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The distributed container simulation device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0117] like Figure 6As shown, the distributed container simulation device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the distributed container simulation device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the distributed container simulation device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a distributed container simulation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have alternatively.

[0118] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0119] The distributed container simulation device provided by the present application adopts the distributed container simulation method in the above embodiment, which can solve the technical problems that the existing 3GPP NTN communication system simulation is usually very complex, requires strong simulation hardware performance, and has problems with the allocation and elastic expansion of computing resources. Compared with the prior art, the beneficial effects of the distributed container simulation device provided by the present application are the same as the beneficial effects of the distributed container simulation method provided by the above embodiment, and the other technical features in the distributed container simulation device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0120] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0121] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0122] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the distributed container simulation method in the above-mentioned embodiment.

[0123] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0124] The computer-readable storage medium may be included in the distributed container simulation device; or may exist independently without being assembled into the distributed container simulation device.

[0125] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the distributed container simulation device, the distributed container simulation device:

[0126] Determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit;

[0127] Determining a host load index of the available host;

[0128] Determining a load balancing index of a hardware platform based on the host machine load index;

[0129] Determine a target host machine with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated;

[0130] The container to be simulated is run by emulating the target host. The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, and the above-mentioned programming language includes an object-oriented programming language-such as Java, Smalltalk, C++, and also includes a conventional procedural programming language-such as "C" language or similar programming language. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network-including a local area network (LAN) or a wide area network (WAN)-connected to the user's computer, or, it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0131] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0132] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0133] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned distributed container simulation method, and can solve the technical problems that the existing 3GPP NTN communication system simulation is usually very complex, requires strong simulation hardware performance, and has problems with the allocation and elastic expansion of computing resources. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the distributed container simulation method provided by the above-mentioned embodiment, and will not be repeated here.

[0134] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned distributed container simulation method when executed by a processor.

[0135] The computer program product provided by the present application can solve the technical problems that the existing 3GPP NTN communication system simulation is usually very complex, requires strong simulation hardware performance, and has problems with the allocation and elastic expansion of computing resources. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the distributed container simulation method provided by the above embodiment, which will not be described in detail here.

[0136] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A distributed container simulation method, characterized in that: The method comprises: Determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit; Determining a host load index of the available host; Determining a load balancing index of a hardware platform based on the host machine load index; Determine a target host machine with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated; The target host machine simulates and runs the container to be simulated.

2. The distributed container simulation method according to claim 1, characterized in that: The step of determining the target host machine with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated includes: Sampling the container to be simulated to determine resource occupancy per second of the container to be simulated; Determine the computing resource consumption required by the container to be simulated based on the resource occupancy per second; Predicting a predicted load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption; A target host machine having the lowest predicted load balancing index is determined from among the available host machines.

3. The distributed container simulation method according to claim 2, characterized in that: After the step of simulating and running the container to be simulated by the target host machine, the method further includes: If the computing resource usage of the target host machine has reached an upper limit after the container to be simulated is run, the target host machine is marked as an unavailable host machine.

4. The distributed container simulation method according to claim 2, characterized in that: After the step of simulating and running the container to be simulated by the target host machine, the method further includes: During the simulation operation, if there is a container terminal switching event, the container to be simulated in the target host is destroyed; Return to the step of predicting the load balancing index of the hardware platform when the container to be simulated runs in each of the available host machines based on the computing resource consumption, so as to find a new target host machine.

5. The distributed container simulation method according to claim 1, characterized in that: The method further comprises: During the synchronization period, network topology update messages are sent to all containers in the simulation process; Receive a completion message generated by each container based on the network topology update message; When completion messages fed back by all containers are received, the step of initiating network topology update messages to all containers in the simulation running process is performed in the next synchronization cycle.

6. The distributed container simulation method according to claim 1, characterized in that: The method further comprises: At the end of the simulation running process, destroying the container to be simulated in the target host machine; Update the computing resource usage of the target host.

7. A distributed container simulation device, characterized in that: The distributed container simulation device comprises: A computing resource determination module, used to determine available host machines based on the computing resource usage of each host machine; the available host machines are host machines whose computing resource usage does not reach an upper limit; A load index determination module, used to determine the host load index of the available host; A load balancing determination module, used to determine a load balancing index of a hardware platform based on the host machine load index; A target host determination module, configured to determine a target host with the lowest load balancing index among the available host machines according to the computing resource consumption of the container to be simulated; The container simulation running module is used to simulate and run the container to be simulated through the target host machine.

8. A distributed container simulation device, characterized in that: The device comprises: a memory, a processor, and a distributed container simulation program stored in the memory and executable on the processor, wherein the distributed container simulation program is configured to implement the steps of the distributed container simulation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a distributed container simulation program, and when the distributed container simulation program is executed by the processor, the steps of the distributed container simulation method according to any one of claims 1 to 7 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the distributed container simulation method according to any one of claims 1 to 6 are implemented.

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