Cloud resource sharing method and device, electronic equipment and storage medium

Abstract

The invention provides a cloud resource sharing method and device, electronic equipment and a storage medium, and relates to the technical field of Internet of Vehicles, the cloud resource sharing method is applied to a server, the server is deployed with at least two cloud instances, and the cloud instances are used for being in communication connection with a vehicle-mounted terminal. The cloud resource sharing method comprises the following steps: performing use index analysis on each cloud instance to obtain a resource use index of each cloud instance; binding the at least two cloud instances according to resource use characteristics indicated by the resource use indexes to form a resource sharing group; and dynamically adjusting resource allocation in the resource sharing group according to the real-time resource demand of each cloud instance, so as to improve the resource utilization rate of the cloud vehicle machine server.

Description

Technical Field

[0001] The present invention relates to the field of vehicle networking technology, and in particular to a cloud resource sharing method, device, electronic device and storage medium. Background Art

[0002] With the rapid advancement of connected vehicle technology, more and more vehicle manufacturers are focusing on the development of in-vehicle computer applications, transforming them from traditional transportation tools into intelligent third spaces. However, limited storage capacity and computing power make it difficult for in-vehicle devices to accommodate a large number of applications. Furthermore, limited computing resources often lead to lags when deploying configuration-sensitive applications on in-vehicle devices. In this context, migrating services to the cloud has become a common industry choice.

[0003] In related technologies, cloud servers allocate independent resource spaces to vehicle terminals, allowing them to access the cloud server's resource services over the network, thus circumventing local computing resource limitations. However, in this solution, the cloud server resources available to each vehicle terminal remain fixed, which can easily lead to local overload of cloud server resources and low overall cloud server resource utilization. Summary of the Invention

[0004] The problem solved by the present invention is how to improve the resource utilization rate of the cloud vehicle machine service end.

[0005] To solve the above problems, the present invention provides a cloud resource sharing method, device, electronic device and storage medium.

[0006] In a first aspect, the present invention provides a cloud resource sharing method, which is applied to a server, wherein the server is deployed with at least two cloud instances, and the cloud instances are used to communicate with the vehicle terminal. The cloud resource sharing method includes: Performing usage indicator analysis on each of the cloud instances to obtain resource usage indicators of each of the cloud instances; bundling at least two of the cloud instances according to the resource usage characteristics indicated by the resource usage indicator to form a resource sharing group; Within the resource sharing group, resource allocation is dynamically adjusted according to the real-time resource requirements of each cloud instance.

[0007] Optionally, before performing usage indicator analysis on each cloud instance to obtain resource usage indicators of each cloud instance, the method further includes: In response to the instance initialization instruction, cloud resources are allocated to the instruction terminal based on usage requirements, and the cloud instance is configured based on the cloud resources, wherein the instruction terminal is the vehicle-mounted terminal that issues the instance initialization instruction.

[0008] Optionally, performing usage indicator analysis on each cloud instance to obtain resource usage indicators of each cloud instance includes: A usage indicator analysis is performed on each of the cloud instances within a preset historical period to obtain the resource usage indicator of each of the cloud instances, wherein the resource usage indicator includes resource utilization and network bandwidth occupancy.

[0009] Optionally, the resource usage characteristics include resource demand characteristics, usage period characteristics and network segment characteristics; Bundling at least two cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator includes: Determine at least two cloud instances that meet preset similarity conditions as similar instances, wherein the preset similarity conditions include at least one of: the difference in the resource demand characteristics is within a preset difference range, the usage period characteristics is within a preset similarity range, and the network segment characteristics are the same; The similar instances are bundled together to form the resource sharing group.

[0010] Optionally, the resource usage characteristics include storage usage characteristics, processor usage characteristics, and bandwidth usage characteristics; Bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further includes: Determining at least two of the cloud instances that meet preset complementary conditions as complementary instances, wherein the preset complementary conditions include: a sum of the storage usage characteristics of the cloud instances being within a first usage characteristic interval, a sum of the processor usage characteristics of the cloud instances being within a second usage characteristic interval, and a sum of the bandwidth usage characteristics of the cloud instances being within a third usage characteristic interval; The complementary instances are bundled to form the resource sharing group.

[0011] Optionally, dynamically adjusting resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance includes: When the resource demand for the target cloud instance exceeds the cloud resources allocated to the target cloud instance, obtaining cloud resources from other cloud instances in the resource sharing group to which the target cloud instance belongs; When the resource demand for the target cloud instance is lower than the cloud resources allocated to the target cloud instance, excess cloud resources of the target cloud instance are shared with the resource sharing group to which the target cloud instance belongs.

[0012] Optionally, bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further comprises: Determine two specific cloud instances that meet preset similarity conditions as similar instance pairs; The similar instance pair and the specific complementary instance are bundled to form the resource sharing group, wherein the specific complementary instance includes the cloud instance that constitutes a complementary instance with the specific cloud instance.

[0013] In a second aspect, the present invention provides a cloud resource sharing device, which is applied to a server, wherein the server is deployed with at least two cloud instances, and the cloud instances are used to communicate with the vehicle terminal. The cloud resource sharing device includes: An analysis module, configured to analyze usage indicators of each cloud instance to obtain resource usage indicators of each cloud instance; a bundling module, configured to bundle at least two of the cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator; The allocation module is used to dynamically adjust resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the cloud resource sharing method as described in the first aspect when executing the computer program.

[0015] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the cloud resource sharing method as described in the first aspect is implemented.

[0016] The beneficial effects of the cloud resource sharing method of the present invention are: By analyzing the resource usage indicators of cloud instances, we can accurately grasp the actual cloud resource requirements and usage patterns of each cloud instance, providing a data foundation for subsequent resource management and optimization. By bundling cloud instances with correlated cloud resource usage indicators and building cloud resource sharing groups, we can improve load balancing potential. When necessary, we can quickly balance the load between instances within the resource sharing group, reduce the situation of insufficient or excessive resources for a single instance, and further improve efficiency. Dynamic adjustment of resource allocation allows cloud resources to flow flexibly according to actual needs, avoiding the waste or shortage of cloud resources caused by fixed resource configuration. Even if there are sudden changes in resource demand, we can respond quickly and meet the needs to maintain the stability and efficiency of cloud car services. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 Schematic diagram of the process of a cloud resource sharing method according to an embodiment of the present invention; Figure 2 This is an example diagram of a server, a cloud instance, and an in-vehicle terminal according to an embodiment of the present invention; Figure 3 2 is an exemplary diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0018] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0020] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0021] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0022] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0023] In response to the problems existing in the above-mentioned related technologies, this embodiment provides a cloud resource sharing method, device, electronic device and storage medium.

[0024] like Figure 1 As shown, an embodiment of the present invention provides a cloud resource sharing method, which is applied to a server, wherein the server is deployed with at least two cloud instances, and the cloud instances are used to communicate with the vehicle terminal. The cloud resource sharing method includes: Step S100 , performing usage indicator analysis on each of the cloud instances to obtain resource usage indicators of each of the cloud instances.

[0025] In an embodiment of the present invention, the cloud resource sharing method can be applied to all distributed computing resource management solutions, such as allocating resources to vehicle terminals through cloud servers; providing computing power to vehicle terminals through edge computing; or other methods of transferring computing power from local to other places.

[0026] In one embodiment, a cloud instance is communicatively connected to at least one vehicle-mounted terminal and provides computing power services or application push services to the vehicle-mounted terminal. For example, when a user clicks an application icon on the vehicle-mounted terminal, the vehicle-mounted terminal sends an interactive signal, runs the application through the communicatively connected cloud instance, and pushes the generated content to the vehicle-mounted terminal via a video or audio stream, thereby reducing the computing power overhead of the vehicle-mounted terminal itself. In this embodiment, each vehicle corresponds to at least one vehicle-mounted terminal, that is, each vehicle-mounted terminal in a vehicle has a corresponding instance when accessing cloud services. When a vehicle-mounted terminal accesses cloud services, the corresponding users have certain usage characteristics. For example, some users have high network requirements, some users have high storage space requirements, and others have high processor computing power requirements. For different users, usage indicator analysis is required. That is, usage indicator analysis is performed on the cloud instance that provides cloud resources to the vehicle-mounted terminal to obtain resource usage indicators, thereby understanding the resource usage habits of the user corresponding to the vehicle-mounted terminal, which serves as a basis for subsequent resource sharing.

[0027] Step S200 : bundling at least two cloud instances according to the resource usage characteristics indicated by the resource usage indicator to form a resource sharing group.

[0028] In one embodiment, the resource usage indicator is used to indicate the user's utilization of the cloud resources of the cloud instance, such as the utilization of the processor, storage space, or network bandwidth. To better understand the user's utilization of the cloud resources of the cloud instance, the resource usage indicator may further include the usage time, duration, and period of use of the processor, memory, storage space, and network bandwidth; the highest load, lowest load, and average load of the processor; the highest load, lowest load, and average load of the memory; the change trend and change cycle of the storage space; and the highest load, highest load, and average load of the network bandwidth. This is used to better bundle related cloud instances to form a resource sharing group for better resource sharing.

[0029] In one embodiment, a resource sharing group includes two or more cloud instances, and when the load on some cloud instances is too high, computing power is preferentially obtained from other cloud instances in the resource sharing group. In another embodiment, when all cloud instances in the resource sharing group are under high load, computing power is obtained from the server.

[0030] Step S300 , dynamically adjusting resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance.

[0031] In one embodiment, a server continuously collects real-time resource demand data for each cloud instance in a resource sharing group, such as current processor utilization, memory usage, storage space requirements, and network bandwidth requirements. The frequency of data collection can be set based on actual needs. This real-time resource demand data is compared with each instance's currently allocated cloud resources to determine the difference between resource demand and quota. For example, if a cloud instance's CPU utilization exceeds a preset threshold, it indicates insufficient CPU resources; if another cloud instance's memory utilization falls below a threshold, it indicates idle memory capacity. When a cloud instance's resource demand exceeds its quota, some resources are allocated from the idle resources of other cloud instances in the resource sharing group to meet its demand. For example, if cloud instance 1's CPU demand surges, unused CPU cores from cloud instance 2 can be temporarily borrowed. When a cloud instance's resource demand decreases, its idle resources are automatically released to the resource sharing pool for use by other cloud instances in the group. For example, if cloud instance 1's network bandwidth usage decreases, the released bandwidth can be used by cloud instance 2 for bursty data transmission.

[0032] In another embodiment, the long-term resource quota of each cloud instance is adjusted based on historical resource usage trends and current demand fluctuations to optimize the long-term cloud resource allocation efficiency. For example, the resource usage characteristics in the future are predicted based on historical resource usage characteristics to optimize the cloud instances in the resource sharing group. If the resource usage characteristics of a cloud instance are relatively fixed, other cloud instances that match the cloud instance and have relatively fixed usage habits will be used as the resource sharing group; if the resource usage characteristics of a cloud instance often drift, cloud instances with low cloud resource utilization or that also often drift will be used as the resource sharing group to cope with possible habit drift.

[0033] In this embodiment, structured resource usage indicators are generated by collecting operational data from each cloud instance. Based on these indicators, the operational patterns and actual demand of each cloud instance are analyzed, laying the data foundation for subsequent optimization. After identifying resource usage characteristics, related cloud instances are grouped together to form resource sharing units. This allows for the cross-time allocation of idle cloud resources and alleviates local cloud resource pressure through peak-shifting and complementary use. During the operation of the resource sharing group, cloud resource reallocation strategies are implemented based on real-time dynamic demand changes. When a cloud instance experiences a sudden high load, surplus resources from other instances in the group can be temporarily utilized. When long-term demand patterns are monitored to change, cloud instances in the resource sharing group are adjusted accordingly. This multi-layered, interconnected technical architecture ensures that cloud resource allocation is aligned with actual demand, allows for cross-instance resource allocation, and provides the flexibility to respond to sudden demand fluctuations. Ultimately, this forms a closed-loop optimization mechanism of "precise demand identification - intelligent resource scheduling - dynamic and flexible adaptation," significantly reducing resource idleness and overload risks, and overall improving resource utilization efficiency and service quality on the cloud server side.

[0034] Alternatively, as Figure 2 As shown, before analyzing the usage indicators of each cloud instance to obtain the resource usage indicators of each cloud instance, the method further includes: In response to the instance initialization instruction, cloud resources are allocated to the instruction terminal based on usage requirements, and the cloud instance is configured based on the cloud resources, wherein the instruction terminal is the vehicle-mounted terminal that issues the instance initialization instruction.

[0035] In one embodiment, before analyzing the usage indicators of the cloud instance, cloud resources are allocated to the vehicle terminal, a communication connection with the vehicle terminal is obtained, and an instance of providing services to the vehicle terminal is provided.

[0036] When the vehicle terminal sends an instance creation request, the cloud platform will parse the operating parameters in its initialization instructions, including specific requirements such as computing power, storage capacity, and network bandwidth. Based on these parameter values, cloud resources of corresponding specifications are matched in the cloud resource pool: for example, a basic instance with 8GB of memory and 32GB of storage is configured for general needs, or an enhanced configuration with a larger capacity is selected for high-performance needs. After the resource matching is completed, a two-way communication link is established between the cloud instance and the vehicle terminal, and an encrypted transmission protocol that conforms to the Internet of Vehicles scenario is adopted, so that the cloud instance can respond to the terminal's navigation, audio and video entertainment and other operating instructions in real time. Before the instance is officially put into service, the resource scheduling module will record the initial configuration parameters to establish a benchmark reference for subsequent analysis of resource usage patterns.

[0037] Optionally, performing usage indicator analysis on each cloud instance to obtain resource usage indicators of each cloud instance includes: A usage indicator analysis is performed on each of the cloud instances within a preset historical period to obtain the resource usage indicator of each of the cloud instances, wherein the resource usage indicator includes resource utilization and network bandwidth occupancy.

[0038] After receiving a command from the vehicle terminal, the server selects an appropriate cloud instance and configures the appropriate resources based on the required usage information contained in the command. For each selected cloud instance, operational data is collected and analyzed over a pre-set historical period (e.g., one to two weeks). This process aims to capture the actual resource usage of the cloud instance.

[0039] Resource usage metrics are generated by analyzing data from a selected historical period. These metrics include resource utilization and network bandwidth utilization. Resource utilization reflects the cloud instance's utilization of CPU, memory, and storage. For example, CPU utilization includes the proportion of time the processor spends executing tasks per unit time; memory utilization includes the ratio of memory allocated to applications to total available memory. Network bandwidth utilization measures the network transmission load on the cloud instance. This is specifically expressed as the ratio of the amount of data transmitted through the network interface per unit time to the maximum transmission capacity.

[0040] Resource usage indicators provide a basis for subsequent optimization of resource allocation strategies and are used to identify potential resource bottlenecks or excesses, thereby improving resource utilization efficiency and service quality.

[0041] In one embodiment, based on the analysis of usage indicators of cloud instances within a preset historical period, peaks and valleys in resource usage during different time periods can be identified. For example, the CPU utilization of a cloud instance is higher during the daytime working hours, but drops significantly at night. This model helps to formulate more flexible resource scheduling plans, so that resources can be effectively replenished during high-demand periods while avoiding waste during low-demand periods. In addition, the analysis of network bandwidth occupancy is equally important, especially when processing large data transmission tasks, so that network resource configuration can be adjusted in a timely manner to ensure smooth data transmission.

[0042] Optionally, the resource usage characteristics include resource demand characteristics, usage period characteristics and network segment characteristics; Bundling at least two cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator includes: At least two of the cloud instances that meet preset similarity conditions are determined to be similar instances, wherein the preset similarity conditions include: the difference in the resource demand characteristics is within a preset difference range, the usage period characteristics are within a preset similarity range, and the network segment characteristics are the same.

[0043] The similar instances are bundled together to form the resource sharing group.

[0044] Resource demand characteristics indicate the demand for core resources such as CPU, memory, and storage by each cloud instance. Usage period characteristics indicate the active time periods of each instance throughout the day or week, specifically the distribution of high-load and low-load periods. Network segment characteristics indicate the location of each instance in the network topology, typically expressed as the subnet or VLAN to which it belongs.

[0045] To achieve effective resource sharing and utilization, similar instances must be identified and bundled based on resource usage characteristics. By analyzing resource usage indicators, at least two cloud instances that meet preset similarity conditions are identified as similar instances. The preset similarity conditions include at least one of the following three situations: The differences in resource demand characteristics are within a preset difference range, the usage period characteristics are within a preset similarity range, and the network segment characteristics are the same. For example, the CPU utilization difference between two instances does not exceed 10%, and the memory utilization difference does not exceed 5%; the peak usage period overlap rate of the two instances reaches more than 80%; and the two instances are located in the same subnet or belong to the same VLAN. For services involving geographical distribution, instances in close geographical locations may have more consistent usage patterns and lower latency, which is conducive to resource sharing. Different types of services (such as video streaming and online gaming) may have different resource requirements and usage patterns. Different preset difference ranges can be set based on the service type.

[0046] Optionally, the resource usage characteristics include storage usage characteristics, processor usage characteristics, and bandwidth usage characteristics; Bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further includes: At least two of the cloud instances that meet preset complementary conditions are determined to be complementary instances, wherein the preset complementary conditions include: the sum of the storage usage characteristics of the cloud instances is within a first usage characteristic interval, the sum of the processor usage characteristics of the cloud instances is within a second usage characteristic interval, and the sum of the bandwidth usage characteristics of the cloud instances is within a third usage characteristic interval.

[0047] The complementary instances are bundled to form the resource sharing group.

[0048] The resource usage characteristics include storage usage characteristics, processor usage characteristics, and bandwidth usage characteristics. Storage usage characteristics indicate the storage usage of each cloud instance, such as the ratio of used storage to total storage capacity. Processor usage characteristics indicate the CPU usage of each cloud instance, such as CPU utilization or average load. Bandwidth usage characteristics indicate the network transmission usage of each cloud instance, such as the ratio of actual network bandwidth usage to the maximum available bandwidth.

[0049] To achieve effective resource sharing and utilization, it is necessary to identify and bundle complementary instances based on resource usage characteristics. By analyzing resource usage indicators, at least two cloud instances that meet the preset complementary conditions are identified as complementary instances, including: The sum of the storage usage characteristics is within the first usage characteristic range. For example, the sum of the storage usage characteristics of multiple cloud instances exceeds a certain threshold but does not exceed the resource limit (such as 50%-100%). This indicates that the storage requirements of the two instances can complement each other and jointly use more storage resources without exceeding the total quota.

[0050] The sum of the processor usage characteristics is in the second usage characteristic range. For example, the sum of the processor usage characteristics of multiple cloud instances exceeds a certain threshold but does not exceed the resource limit (such as 60%-95%). This means that when one cloud instance is experiencing high load, other instances may be in a low load state, thereby enabling temporary borrowing of resources.

[0051] The sum of the bandwidth usage characteristics is in the third usage characteristic range. For example, the sum of the bandwidth usage characteristics of multiple cloud instances exceeds a certain threshold but does not exceed the resource limit (such as 50%-90%). This indicates that the network bandwidth demands of these instances will not reach peak values ​​at the same time, and bandwidth resources can be shared when necessary.

[0052] Complementary instances that meet the above criteria can be bundled together to form a resource-sharing group. Bundled instances can share resources such as storage, processor power, and network bandwidth when needed, optimizing overall resource utilization. For example, if one cloud instance is running low on storage or overloaded with processors, it can borrow idle resources from another cloud instance, and vice versa.

[0053] In one embodiment, the update period of the resource sharing group may be the same as the preset historical period, or may be established based on the preset historical period. For example, when the preset historical period is 7 days, the update period of the resource sharing group is 7 days. Each time the usage indicator analysis is performed, the resource sharing group is re-established to establish a resource sharing group that is more suitable for the current period.

[0054] Optionally, dynamically adjusting resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance includes: When the resource demand for the target cloud instance exceeds the cloud resources allocated to the target cloud instance, obtaining cloud resources from other cloud instances in the resource sharing group to which the target cloud instance belongs; When the resource demand for the target cloud instance is lower than the cloud resources allocated to the target cloud instance, excess cloud resources of the target cloud instance are shared with the resource sharing group to which the target cloud instance belongs.

[0055] To achieve effective resource sharing and flexible scheduling, resource allocation is dynamically adjusted within the resource sharing group based on the real-time resource requirements of each cloud instance. This is done by monitoring the real-time resource requirements of each cloud instance, including CPU utilization, memory usage, storage space consumption, and network bandwidth usage. This is used to evaluate current resource usage. When a change in the resource requirements of a cloud instance is detected, such as when the CPU utilization of the target cloud instance suddenly increases to exceed the allocated resource quota or exceeds the preset threshold, or when the memory usage of another instance is significantly lower than its allocated quota or lower than the sharing threshold, the resource adjustment mechanism is triggered.

[0056] If resource demand exceeds allocated resources, idle resources can be borrowed from other cloud instances in the resource sharing group. For example, if the CPU demand of the target cloud instance exceeds the currently allocated resources, unused CPU cores can be temporarily borrowed from other instances in the resource sharing group. If the network bandwidth of the target cloud instance is insufficient, underutilized network bandwidth from other cloud instances can be temporarily borrowed.

[0057] If resource demand falls below the sharing threshold, excess cloud resources will be released to other instances in the resource sharing group when needed. For example, if the target cloud instance's memory usage is significantly below its allocated quota, this unused memory can be released to the resource sharing pool for on-demand use by other cloud instances in the group. Alternatively, if the target cloud instance's network bandwidth usage is low, its excess bandwidth can be contributed to other instances in the group that need it.

[0058] Optionally, bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further comprises: Determine two specific cloud instances that meet preset similarity conditions as similar instance pairs; The similar instance pair and the specific complementary instance are bundled to form the resource sharing group, wherein the specific complementary instance includes the cloud instance that constitutes a complementary instance with the specific cloud instance.

[0059] In one embodiment, forming a resource sharing group also includes using similar instance pairs as the basis for the resource sharing group and combining cloud instances that form complementary instances with any one instance in the similar instance pair to form the resource sharing group. For example, if cloud instance 1 and cloud instance 2 form a similar instance pair, the complementary instance of cloud instance 1 and / or cloud instance 2 will be combined with cloud instance 1 and cloud instance 2 to form the resource sharing group.

[0060] Cloud instances 1 and 2 are very similar in storage usage, processor usage, and bandwidth usage characteristics, meeting the preset similarity conditions and are therefore determined to be a similar instance pair. At the same time, although instance 3 has different requirements from instances 1 and 2 in some aspects (such as storage usage), it can form a complementary relationship with instance 1 or 2 in other aspects (such as processor usage). Therefore, the similar instance pairs 1 and 2 and the specific complementary instance 3 are bundled together to form a resource sharing group. In this way, when instances 1 or 2 are facing high load, they can borrow the necessary resources from instance 3; conversely, when instance 3 does not need all resources, its excess resources can be contributed to instances 1 and 2 for use. This approach significantly improves the flexibility and efficiency of resource utilization.

[0061] An embodiment of the present invention provides a cloud resource sharing device, which is applied to a server. The server is deployed with at least two cloud instances, and the cloud instances are used to communicate with a vehicle terminal. The cloud resource sharing device includes: An analysis module, configured to analyze usage indicators of each cloud instance to obtain resource usage indicators of each cloud instance; a bundling module, configured to bundle at least two of the cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator; The allocation module is used to dynamically adjust resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance.

[0062] like Figure 3 As shown, an electronic device 300 provided by an embodiment of the present invention includes a memory 310 and a processor 320; the memory 310 is used to store computer programs; the processor 320 is used to implement the cloud resource sharing method as described above when executing the computer program.

[0063] In other words, an electronic device 300 includes a memory 310 and a processor 320 coupled to the memory 310; the memory 310 is configured to store a computer program; and the processor 320 is configured to perform the following operations when executing the computer program: Performing usage indicator analysis on each cloud instance to obtain resource usage indicators of each cloud instance; bundling at least two of the cloud instances according to the resource usage characteristics indicated by the resource usage indicator to form a resource sharing group; Within the resource sharing group, resource allocation is dynamically adjusted according to the real-time resource requirements of each cloud instance.

[0064] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the cloud resource sharing method described above is implemented.

[0065] In other words, a non-volatile computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to perform the following operations: Performing usage indicator analysis on each cloud instance to obtain resource usage indicators of each cloud instance; bundling at least two of the cloud instances according to the resource usage characteristics indicated by the resource usage indicator to form a resource sharing group; Within the resource sharing group, resource allocation is dynamically adjusted according to the real-time resource requirements of each cloud instance.

[0066] An electronic device 300 that can serve as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 300 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 300 can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0067] Electronic device 300 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) or loaded from a storage unit into a random access memory (RAM). The RAM can also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. An input / output (I / O) interface is also connected to the bus.

[0068] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM). In this application, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network elements. Some or all of these units can be selected based on actual needs to achieve the objectives of the embodiments of the present invention. Furthermore, the functional units in the various embodiments of the present invention can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. These integrated units can be implemented in either hardware or software functional units.

[0069] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A cloud resource sharing method, characterized in that: Applied to a server, the server is deployed with at least two cloud instances, the cloud instances are used to communicate with the vehicle terminal, and the cloud resource sharing method includes: Performing usage indicator analysis on each of the cloud instances to obtain resource usage indicators of each of the cloud instances; bundling at least two of the cloud instances according to the resource usage characteristics indicated by the resource usage indicator to form a resource sharing group; Within the resource sharing group, resource allocation is dynamically adjusted according to the real-time resource requirements of each cloud instance.

2. The cloud resource sharing method according to claim 1, characterized in that: Before analyzing the usage indicators of each cloud instance to obtain the resource usage indicators of each cloud instance, the method further includes: In response to the instance initialization instruction, cloud resources are allocated to the instruction terminal based on usage requirements, and the cloud instance is configured based on the cloud resources, wherein the instruction terminal is the vehicle-mounted terminal that issues the instance initialization instruction.

3. The cloud resource sharing method according to claim 2, characterized in that: The performing usage indicator analysis on each of the cloud instances to obtain the resource usage indicator of each of the cloud instances includes: A usage indicator analysis is performed on each of the cloud instances within a preset historical period to obtain the resource usage indicator of each of the cloud instances, wherein the resource usage indicator includes resource utilization and network bandwidth occupancy.

4. The cloud resource sharing method according to any one of claims 1 to 3, characterized in that: The resource usage characteristics include resource demand characteristics, usage period characteristics and network segment characteristics; Bundling at least two cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator includes: Determine at least two cloud instances that meet preset similarity conditions as similar instances, wherein the preset similarity conditions include at least one of: the difference in the resource demand characteristics is within a preset difference range, the usage period characteristics is within a preset similarity range, and the network segment characteristics are the same; The similar instances are bundled together to form the resource sharing group.

5. The cloud resource sharing method according to any one of claims 1 to 3, characterized in that: The resource usage characteristics include storage usage characteristics, processor usage characteristics and bandwidth usage characteristics; Bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further includes: Determining at least two of the cloud instances that meet preset complementary conditions as complementary instances, wherein the preset complementary conditions include: a sum of the storage usage characteristics of the cloud instances being within a first usage characteristic interval, a sum of the processor usage characteristics of the cloud instances being within a second usage characteristic interval, and a sum of the bandwidth usage characteristics of the cloud instances being within a third usage characteristic interval; The complementary instances are bundled to form the resource sharing group.

6. The cloud resource sharing method according to claim 3, characterized in that: Dynamically adjusting resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance includes: When the resource demand for the target cloud instance exceeds the cloud resources allocated to the target cloud instance, obtaining cloud resources from other cloud instances in the resource sharing group to which the target cloud instance belongs; When the resource demand for the target cloud instance is lower than the cloud resources allocated to the target cloud instance, excess cloud resources of the target cloud instance are shared with the resource sharing group to which the target cloud instance belongs.

7. The cloud resource sharing method according to any one of claims 1 to 3, characterized in that: Bundling at least two cloud instances to form a resource sharing group based on the resource usage characteristics indicated by the resource usage indicator further includes: Determine two specific cloud instances that meet preset similarity conditions as similar instance pairs; The similar instance pair and the specific complementary instance are bundled to form the resource sharing group, wherein the specific complementary instance includes the cloud instance that constitutes a complementary instance with the specific cloud instance.

8. A cloud resource sharing device, characterized in that: Applied to a server, the server is deployed with at least two cloud instances, the cloud instances are used to communicate with the vehicle terminal, and the cloud resource sharing device includes: An analysis module, configured to analyze usage indicators of each cloud instance to obtain resource usage indicators of each cloud instance; a bundling module, configured to bundle at least two of the cloud instances to form a resource sharing group according to the resource usage characteristics indicated by the resource usage indicator; The allocation module is used to dynamically adjust resource allocation within the resource sharing group according to the real-time resource requirements of each cloud instance.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is configured to implement the cloud resource sharing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the cloud resource sharing method according to any one of claims 1 to 7 is implemented.

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