Scheduling method and device for cloud host of cloud platform

Abstract

The embodiment of the invention provides a scheduling method and device for cloud hosts of a cloud platform, and relates to the field of computers, after at least two host machines are obtained, aggregation grouping is carried out according to host machine attributes of the host machines, and the host machine attributes of the host machines in a host machine set of the same group meet similar conditions, so that when a scheduling task is executed, the host machine attributes of the host machines in the same group meet the similar conditions. According to the method and the device, the attributes of the host machines do not need to be inquired one by one, matching is performed according to the grouped host machine attributes, so that the time required for scheduling the host machines is shortened, the problem of relatively low scheduling efficiency of the cloud host of the cloud platform can be solved, and the technical effect of improving the scheduling efficiency of the cloud host of the cloud platform is achieved.

Description

Technical Field

[0001] The embodiments of the present application relate to the computer field, and more specifically, to a scheduling method and device for a cloud platform cloud host. Background Art

[0002] In the related art, in the scenario of cloud platform virtual machine scheduling, when executing the scheduling task, it is necessary to query the properties of the host machines one by one to select the host machine for scheduling, but this method is time-consuming and labor-intensive, resulting in a long time consumed when performing host machine queries, which in turn leads to the problem of low scheduling efficiency of cloud platform cloud hosts. Therefore, there is a problem of low scheduling efficiency of cloud platform cloud hosts.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a method and device for scheduling cloud hosts on a cloud platform, so as to at least solve the problem of low scheduling efficiency of cloud hosts on a cloud platform in the related art.

[0005] According to an embodiment of the present application, a scheduling method for cloud hosts on a cloud platform is provided, comprising: obtaining at least two host machines; aggregating and grouping the at least two host machines according to the host attributes of the host machines to obtain at least two host machine sets, wherein the host attributes of the host machines in the host machine sets of the same group satisfy similar conditions; obtaining a cloud host task to be scheduled; and determining, from the at least two host machine sets, a scheduling host machine that matches the cloud host task to execute the cloud host task.

[0006] According to another embodiment of the present application, a scheduling device for a cloud platform cloud host is provided, including: a first acquisition unit, used to acquire at least two host machines; a grouping unit, used to aggregate and group the at least two host machines according to the host attributes of the above-mentioned host machines, to obtain at least two groups of host machine sets, wherein the host attributes of the above-mentioned host machines in the above-mentioned host machine sets of the same group meet similar conditions; a second acquisition unit, used to acquire cloud host tasks that need to be scheduled; a determination unit, used to determine, from the at least two groups of host machine sets, a scheduling host machine that matches the above-mentioned cloud host task, to execute the above-mentioned cloud host task.

[0007] As an optional scheme, the above-mentioned grouping unit includes: a grouping module, which is used to aggregate and group the above-mentioned at least two host machines according to the qualitative attributes and quantitative attributes of the above-mentioned host machines to obtain the above-mentioned at least two groups of host machine sets, wherein the qualitative attributes of the above-mentioned hosts in the above-mentioned host machine sets of the same group are the same, and the quantitative attribute deviations of the above-mentioned hosts in the above-mentioned host machine sets of the same group are within a preset range.

[0008] As an optional scheme, the above-mentioned grouping unit includes: a grouping module, which is used to aggregate and group the above-mentioned at least two host machines according to the qualitative attributes and quantitative attributes of the above-mentioned host machines to obtain the above-mentioned at least two groups of host machine sets, wherein the qualitative attributes of the above-mentioned hosts in the above-mentioned host machine sets of the same group are the same, and the quantitative attribute deviations of the above-mentioned hosts in the above-mentioned host machine sets of the same group are within a preset range.

[0009] As an optional solution, the grouping module includes: a first acquisition submodule, configured to acquire the remaining CPU amount of the first host machine, wherein the quantitative attribute of the first host machine includes the remaining CPU amount.

[0010] As an optional solution, the grouping module further includes: a second acquisition submodule, configured to acquire the remaining amount of memory of the second host machine, wherein the quantitative attribute of the second host machine includes the remaining amount of memory.

[0011] As an optional solution, the grouping module further includes: a third acquisition submodule, used to obtain an identifier of the third host machine obtained by string concatenation and hash operation, wherein the qualitative attributes of the third host machine include the identifier.

[0012] As an optional solution, the third acquisition submodule includes: an acquisition subunit, used to acquire a first string of sorted and spliced ​​storage IDs of the third host, a second string of sorted and spliced ​​network IDs of the third host, a CPU architecture attribute of a third target host, and a model attribute of the third host; a sorting subunit, used to sort the first string and the second string to obtain a sorted first string and a sorted second string; a splicing subunit, used to splice the sorted first string and the sorted second string to obtain a spliced ​​string; an adding subunit, used to add the CPU architecture attribute and the model attribute to the end of the spliced ​​string to obtain a target string; and an operation subunit, used to perform a hash operation on the target string to obtain the identifier.

[0013] As an optional scheme, the above-mentioned determination unit includes: a first determination module, used to determine a first host machine that matches the above-mentioned cloud host task from the above-mentioned first host machine set, and determine a second host machine that matches the above-mentioned cloud host task from the above-mentioned second host machine set, wherein the above-mentioned scheduling host includes the above-mentioned first host machine and the above-mentioned second host machine.

[0014] As an optional solution, the first determination module includes: a locking submodule, which is used to lock the first host machine and the second host machine, wherein the locked host machines are set to prohibit scheduling.

[0015] As an optional solution, the locking submodule includes: an unlocking subunit, which is used to unlock the first host machine and the second host machine when the cloud host task is completed, wherein the unlocked host machine is set to allow scheduling.

[0016] As an optional solution, the above-mentioned determination unit includes: an update module, which is used to update the host properties of the above-mentioned scheduling host machine when the above-mentioned cloud host task is completed, and re-aggregate and group the updated scheduling host machines.

[0017] As an optional scheme, the above-mentioned determination unit includes: an acquisition module, used to obtain other cloud host tasks that need to be scheduled; a second determination module, used to determine the scheduling host machine matching the above-mentioned other cloud host tasks from the above-mentioned at least two groups of host machine sets except the scheduling host machine matching the above-mentioned cloud host tasks, so as to execute the above-mentioned other cloud host tasks.

[0018] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0019] According to another embodiment of the present application, a computer program product is provided, including a computer program, wherein when the computer program is configured to be executed by a processor, the steps in any one of the above method embodiments are performed.

[0020] Through the present application, at least two host machines are obtained; at least two host machines are aggregated and grouped according to the host attributes of the host machines to obtain at least two host machine sets, wherein the host attributes of the host machines in the same group of host machine sets meet similar conditions; a cloud host task that needs to be scheduled is obtained; and a scheduling host machine that matches the cloud host task is determined from the at least two host machine sets to execute the cloud host task.

[0021] Specifically, after obtaining at least two host machines, the hosts are aggregated and grouped according to their host attributes, and the host attributes of the hosts in the same group of host machines meet similar conditions. Therefore, when executing scheduling tasks, there is no need to query the attributes of the host machines one by one, but to match them according to the host attributes of the group, thereby reducing the time required for scheduling the hosts, solving the problem of low scheduling efficiency of cloud platform cloud hosts, and achieving the technical effect of improving the scheduling efficiency of cloud platform cloud hosts. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of an application environment of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0023] Figure 2 is a flow chart of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0024] Figure 3 is a schematic diagram of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0025] Figure 4 is a flow chart of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0026] Figure 5 is a schematic diagram of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0027] Figure 6 is a flow chart of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0028] Figure 7 is a schematic diagram of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0029] Figure 8 is a flow chart of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0030] Fig. 9 is a schematic diagram of a scheduling method for a cloud platform cloud host according to an embodiment of the present application;

[0031] Fig.10 It is a structural block diagram of the scheduling device of the cloud platform cloud host in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The embodiments of the present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0034] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 1 is a hardware structure block diagram of a server device of a cloud platform cloud host scheduling method according to an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0035] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the scheduling method of the cloud platform cloud host in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0036] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0037] In this embodiment, a scheduling method for a cloud platform cloud host is provided. Figure 2 is a flow chart of a scheduling method for a cloud platform cloud host according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0038] Step S202, obtaining at least two host machines;

[0039] Step S204 , according to the host attributes of the host machines, at least two host machines are aggregated and grouped to obtain at least two host machine sets, wherein the host attributes of the host machines in the same group of host machine sets meet similar conditions.

[0040] Step S206, obtaining the cloud host tasks that need to be scheduled;

[0041] Step S208, determining a scheduling host machine that matches the cloud host task from at least two sets of host machines to execute the cloud host task.

[0042] In an optional embodiment, the cloud platform may be, but is not limited to, a platform that provides computing resources and services to users through a network.

[0043] In an optional embodiment, the cloud host can be understood as, but not limited to, a virtual machine running on a host machine.

[0044] In an optional embodiment, the host machine may be, but is not limited to, understood as a physical server running virtualization software in a cloud platform, and may be, but is not limited to, responsible for hosting the operation of virtual machines.

[0045] In an optional embodiment, host attributes may be understood as, but not limited to, various parameters or indicators of characteristics and status of the host machine.

[0046] In an optional embodiment, at least two host machines are obtained. This process may be understood, but is not limited to, as identifying and selecting at least two host machines on the cloud platform, and the two host machines have the ability to execute cloud host tasks.

[0047] Furthermore, at least two host machines are aggregated and grouped according to the host attributes of the host machines to obtain at least two host machine sets. This process can be but is not limited to being understood as aggregating and grouping at least two host machines according to the host attributes. After grouping, each group is a host machine set, and the hosts in each set are consistent in host attributes, so that when a host machine is subsequently selected for scheduling cloud host tasks, a quick selection can be made based on the grouping information, thereby improving the scheduling efficiency.

[0048] Next, the cloud host tasks that need to be scheduled are obtained. This process can be understood as, but is not limited to, extracting virtual machine tasks that need to be created or run from the request queue of the cloud platform.

[0049] Finally, a scheduling host machine that matches the cloud host task is determined from at least two groups of host machines to execute the cloud host task. This process can be understood, but is not limited to, as selecting a scheduling host machine that matches the cloud host task from the host machine grouping for resource allocation based on the resource requirements of the cloud host task, to ensure that the allocated host machine has sufficient resources to execute the cloud host task.

[0050] It should be noted that the hosts are first aggregated and grouped based on their host attributes. Then, when a new cloud host task needs to be executed, it is no longer necessary to search and select from all hosts. Instead, based on the task requirements and the host grouping information, the appropriate host set is quickly located and selected for scheduling, which improves the scheduling efficiency of cloud hosts on the cloud platform.

[0051] Through the above steps, at least two host machines are obtained; at least two host machines are aggregated and grouped according to the host attributes of the host machines to obtain at least two host machine sets, wherein the host attributes of the host machines in the same group of host machine sets meet similar conditions; the cloud host task that needs to be scheduled is obtained; and a scheduling host machine that matches the cloud host task is determined from the at least two host machine sets to execute the cloud host task.

[0052] Specifically, after obtaining at least two host machines, the hosts are aggregated and grouped according to their host attributes, and the host attributes of the hosts in the same group of host machines meet similar conditions. Therefore, when executing scheduling tasks, there is no need to query the attributes of the host machines one by one, but to match them according to the host attributes of the group, thereby reducing the time required for scheduling the hosts, solving the problem of low scheduling efficiency of cloud platform cloud hosts, and achieving the technical effect of improving the scheduling efficiency of cloud platform cloud hosts.

[0053] The execution subject of the above steps may be a server, a terminal, etc., but is not limited thereto.

[0054] As an optional solution, at least two hosts are aggregated and grouped according to the host attributes of the hosts to obtain at least two sets of host machines, including:

[0055] According to the qualitative attributes and quantitative attributes of the host machines, at least two host machines are aggregated and grouped to obtain at least two host machine sets, wherein the qualitative attributes of the hosts in the same group of host machine sets are the same, and the quantitative attribute deviations of the hosts in the same group of host machine sets are within a preset range.

[0056] In an optional embodiment, the host attributes may include, but are not limited to, qualitative attributes and quantitative attributes.

[0057] In an optional embodiment, the qualitative attributes may include, but are not limited to, CPU architecture, host model, and operating system type.

[0058] In an optional embodiment, the quantitative attribute may include but is not limited to at least one of a CPU remaining amount and a memory remaining amount.

[0059] In an optional embodiment, the qualitative attributes of the host machines in the host machine set of the same group are the same, and the deviation of the quantitative attributes of the host machines in the host machine set of the same group is within a preset range. This process can be understood, but is not limited to, as the qualitative attributes of the host machines in the set must be completely consistent, while the quantitative attributes do not have to be completely equal, but the deviation must be within a preset range.

[0060] It should be noted that by dividing the attributes of the host into qualitative and quantitative categories, and grouping them according to the similarity of these two categories of attributes, when creating a set of hosts, since the qualitative attributes determine the type and compatibility of the host, the qualitative attributes within the same group will be prioritized when grouping to ensure that they are completely consistent. At the same time, for quantitative attributes, differences are allowed within a preset range, avoiding the situation where hosts are assigned to different groups due to slight differences in quantitative attributes, thereby improving the scheduling efficiency of cloud platforms and resource utilization.

[0061] Through the embodiment of the present application, at least two hosts are aggregated and grouped according to the qualitative and quantitative attributes of the host machines, and at least two sets of host machines are obtained, wherein the qualitative attributes of the hosts in the same set of host machines are the same, and the quantitative attribute deviations of the hosts in the same set of host machines are within a preset range. Aggregating and grouping the hosts by qualitative and quantitative attributes achieves the technical purpose of providing greater flexibility for the scheduling algorithm, thereby achieving the technical effect of improving the scheduling efficiency of the cloud platform cloud host.

[0062] As an optional solution, when the at least two host machines include the first host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative attributes and quantitative attributes of the host machines to obtain at least two sets of host machines, the method further includes:

[0063] The remaining amount of a CPU of the first host machine is obtained, wherein the quantitative attribute of the first host machine includes the remaining amount of the CPU.

[0064] In an optional embodiment, the first host machine may be, but is not limited to, a host machine that will perform cloud host task scheduling in the cloud platform.

[0065] In an optional embodiment, the CPU remaining amount may be, but is not limited to, understood as the amount of unoccupied CPU resources on the first host machine, and may be, but is not limited to, used to reflect the remaining computing capacity of the host machine.

[0066] It should be noted that by obtaining the remaining CPU of the first host machine, the system can more accurately evaluate the resource status and attributes of the first host machine, thereby facilitating the realization of similar quantitative attributes and consistent qualitative attributes in subsequent grouping, thereby improving the accuracy of grouping and enabling faster finding of a set of host machines that meet the scheduling conditions during scheduling, thereby improving the scheduling efficiency of the cloud host.

[0067] Through the embodiment of the present application, the CPU remaining amount of the first host is obtained, wherein the quantitative attribute of the first host includes the CPU remaining amount. By obtaining the CPU remaining amount of the first host, the system can more accurately evaluate the resource status and attributes of the first host, thereby achieving the technical purpose of facilitating the realization of quantitative attribute similarity and qualitative attribute consistency in subsequent grouping, improving the accuracy of grouping, and enabling the host set that meets the scheduling conditions to be found more quickly during scheduling, thereby achieving the technical effect of improving the scheduling efficiency of the cloud host.

[0068] As an optional solution, when the at least two host machines include the second host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative attributes and quantitative attributes of the host machines to obtain at least two sets of host machines, the method further includes:

[0069] The remaining amount of memory of the second host is obtained, wherein the quantitative attribute of the second host includes the remaining amount of memory.

[0070] In an optional embodiment, the second host machine may be, but is not limited to, a host machine that will perform cloud host task scheduling in the cloud platform.

[0071] In an optional embodiment, the remaining amount of memory may be, but is not limited to, understood as the amount of unoccupied memory resources on the second host machine, and may be, but is not limited to, used to reflect the remaining storage capacity of the host machine.

[0072] In an optional embodiment, the process of obtaining the remaining amount of memory of the second host machine may be understood, but is not limited to, as follows: when grouping the host machines, the system needs to obtain the current remaining amount of memory resources of the target host machine.

[0073] It should be noted that by obtaining the remaining memory of the second host machine, the system can more accurately evaluate the resource status and attributes of the second host machine, thereby facilitating the realization of quantitative attribute similarity and qualitative attribute consistency in subsequent grouping, improving the accuracy of grouping, and making it possible to find a set of host machines that meet the scheduling conditions more quickly during scheduling, thereby improving the scheduling efficiency of the cloud host.

[0074] Through the embodiment of the present application, the remaining memory of the second host is obtained, wherein the quantitative attributes of the second host include the remaining CPU. By obtaining the remaining memory of the second host, the system can more accurately evaluate the resource status and attributes of the second host, thereby achieving the technical purpose of achieving similar quantitative attributes and consistent qualitative attributes during grouping, improving the accuracy of grouping, and enabling faster finding of a set of host machines that meet the scheduling conditions during scheduling, thereby achieving the technical effect of improving the scheduling efficiency of the cloud host.

[0075] As an optional scheme, when at least two host machines include a third host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative and quantitative attributes of the host machines to obtain at least two groups of host machine sets, the method also includes: obtaining an identifier of the third host machine obtained by string concatenation and hash operation, wherein the qualitative attributes of the third host machine include the identifier.

[0076] In an optional embodiment, the third host machine may be, but is not limited to, a host machine that will perform cloud host task scheduling in the cloud platform.

[0077] In an optional embodiment, the qualitative attribute may include, but is not limited to, an identifier obtained by string concatenation and hash operation.

[0078] In an optional embodiment, the identifier of the target host is obtained through string concatenation and hash operation. This process can be understood, but is not limited to, as follows: in order to process the qualitative attributes of the target host, the system will perform string concatenation on these attribute information, and then convert them into a numerical identifier through hash operation, so as to quickly compare whether the qualitative attributes between the hosts are the same.

[0079] It should be noted that by obtaining the qualitative attribute identifier of the third host machine, the system can more accurately evaluate the resource status and attributes of the third host machine, so as to achieve similarity of quantitative attributes and consistency of qualitative attributes when grouping, thereby improving the accuracy of grouping, making it possible to find a set of host machines that meet the scheduling conditions more quickly during scheduling, thereby improving the scheduling efficiency of the cloud host.

[0080] Through the embodiment of the present application, the remaining CPU of the third host is obtained, wherein the quantitative attribute of the third host includes the remaining CPU. By obtaining the identifier of the third host obtained by string concatenation and hash operation, the accuracy of grouping is improved, so that the technical purpose of finding a set of hosts that meet the scheduling conditions more quickly during scheduling is achieved, thereby achieving the technical effect of improving the scheduling efficiency of the cloud host.

[0081] As an optional solution, obtain the target host's identifier by string concatenation and hashing, including:

[0082] S1-1, obtaining a first string of target host storage IDs after sorting, a second string of target host IDs after sorting, a CPU architecture attribute of the target host, and a model attribute of the target host;

[0083] S1-2, sorting the first character string and the second character string to obtain a sorted first character string and a sorted second character string;

[0084] S1-3, concatenating the sorted first character string and the sorted second character string to obtain a concatenated character string;

[0085] S1-4, adding the CPU architecture attribute and the model attribute to the end of the concatenated string to obtain the target string;

[0086] S1-5, perform hash operation on the target string to obtain a label.

[0087] In an optional embodiment, the storage id may be, but is not limited to, a unique identifier of an associated storage device in the host, used to distinguish different storage resources. The storage id may be, but is not limited to, part of the qualitative attributes of the host, reflecting the type and location of the storage resource connected to the host.

[0088] In an optional embodiment, the host id may be, but is not limited to, a unique identifier of a network device associated with the host, used to distinguish different network resources. The network id may be, but is not limited to, a part of the qualitative attributes of the host, reflecting the type and topology of the network resources to which the host is connected.

[0089] In an optional embodiment, a first string of target host storage ids that are sorted and concatenated, a second string of target host ids that are sorted and concatenated, a CPU architecture attribute of the target host, and a model attribute of the target host are obtained. This process can be but is not limited to being understood as the system collecting the associated storage id, network id, CPU architecture and host model information of the target host.

[0090] In an optional embodiment, the first string and the second string are sorted to obtain the sorted first string and the sorted second string. This process can be understood, but not limited to, as the first string and the second string need to be sorted in order to ensure that the generated identifier is not affected by the specific storage or network resource order. The sorted strings will be used for subsequent concatenation operations.

[0091] In an optional embodiment, the sorted first string and the sorted second string are concatenated to obtain a concatenated string. This process can be understood, but is not limited to, as concatenating the sorted storage ID string and the network ID string together to generate a string that comprehensively reflects the storage and network resources connected to the host machine.

[0092] In an optional embodiment, the CPU architecture attribute and the model attribute are added to the end of the concatenated string to obtain a target string. This process can be understood, but is not limited to, as continuing to add the CPU architecture and model attributes of the host machine on the basis of the concatenated string to form a target string that comprehensively reflects the qualitative attributes of the host machine.

[0093] In an optional embodiment, a hash operation is performed on the target string to obtain an identifier. This process can be understood, but is not limited to, as taking the target string as input, executing a hash operation algorithm, and generating a unique numerical identifier, which will serve as a qualitative attribute identifier of the host machine.

[0094] It should be noted that the system first generates two sorted strings by collecting and sorting the storage ID, network ID, CPU architecture and model attributes of the host. Then, the two strings are concatenated and the CPU architecture and model attributes are added to form a target string. Finally, a hash operation is performed on the target string to generate a qualitative attribute identifier. The purpose of this process is to convert the qualitative attributes of the host into a simple numerical identifier, which is convenient for quick comparison and grouping, thereby improving the efficiency and accuracy of the scheduling of cloud hosts on the cloud platform.

[0095] Through the embodiment of the present application, the first string of the target host storage id after sorting and splicing, the second string of the target host id after sorting and splicing, the CPU architecture attribute of the target host, and the model attribute of the target host are obtained; the first string and the second string are sorted to obtain the sorted first string and the sorted second string; the sorted first string and the sorted second string are spliced ​​to obtain the spliced ​​string; the CPU architecture attribute and the model attribute are added to the end of the spliced ​​string to obtain the target string; the target string is hashed to obtain an identifier. By collecting and sorting the strings of the qualitative attributes of the host, the technical purpose of converting the qualitative attributes of the host into a simple numerical identifier is achieved, thereby achieving the technical effect of improving the efficiency and accuracy of the scheduling of the cloud platform cloud host.

[0096] As an optional solution, when the at least two host sets include a first host set and a second host set, determining a scheduling host matching the cloud host task from the at least two host sets includes:

[0097] A first host matching the cloud host task is determined from the first host set, and a second host matching the cloud host task is determined from the second host set, wherein the scheduling host includes the first host and the second host.

[0098] In an optional embodiment, a first host machine that matches the cloud host task is determined from the first host machine set. This process can be understood, but is not limited to, as follows: based on the host machine grouping, the system will screen out host machines from the first host machine set that can meet these requirements based on the resource configuration requirements of the cloud host task.

[0099] In an optional embodiment, a second host machine that matches the cloud host task is determined from the second host machine set. This process can be but is not limited to being understood as, based on the host machine grouping, the system will screen out host machines from the second host machine set that can meet these requirements based on the resource configuration requirements of the cloud host task.

[0100] It should be noted that by comprehensively considering the qualitative and quantitative attributes of the host machines, the system can accurately screen out hosts that match the cloud host tasks from each host machine set. Ultimately, the scheduled hosts may include hosts from different host machine sets, thereby improving the scheduling efficiency of the cloud platform cloud hosts and enabling the cloud platform to handle high-concurrency cloud host creation requests.

[0101] In addition, the scheduling host includes the first host and the second host, which means that the final scheduling host may come from multiple host sets, including the first host set and the second host set. When creating cloud host tasks with high concurrency, the system can select hosts from multiple host sets in parallel to speed up the scheduling process and improve scheduling efficiency.

[0102] Through the embodiment of the present application, a first host matching the cloud host task is determined from the first host set, and a second host matching the cloud host task is determined from the second host set, wherein the scheduling host includes the first host and the second host. By comprehensively considering the qualitative and quantitative attributes of the host, the technical purpose of the final scheduling host may include hosts from different host sets is achieved, thereby improving the scheduling efficiency of the cloud platform cloud host.

[0103] In an optional embodiment, after determining a first host matching the cloud host task from the first host set and determining a second host matching the cloud host task from the second host set, the method further includes:

[0104] The first host machine and the second host machine are locked, wherein the locked host machines are set to prohibit scheduling.

[0105] In an optional embodiment, locking can be but is not limited to being understood as locking the host machine, which means setting the host machine to a temporary resource protection state to prevent other cloud host tasks from being concurrently scheduled on the host machine to ensure that the resource requirements of the currently scheduled tasks can be met.

[0106] In an optional embodiment, the first host and the second host are locked, wherein the locked host is set to prohibit scheduling. This process can be understood as, but not limited to, after determining the first host and the second host that match the cloud host task, the system will lock the two hosts and set their status to prohibit scheduling. This means that during the scheduling process, other cloud host tasks will not be able to select these two hosts, ensuring that the resource requirements of the current cloud host task are met first.

[0107] It should be noted that after the host machine is determined as the scheduling target of the cloud host task, the system will lock it to prevent interference from other tasks, ensure that the resource requirements of the current task are met, and improve the stability of the scheduled host machine during scheduling.

[0108] Through the embodiment of the present application, the first host machine and the second host machine are locked, wherein the locked host machines are set to prohibit scheduling. By locking the first host machine and the second host machine, the technical purpose of locking the host machine after it is determined as the scheduling target of the cloud host task is achieved to prevent interference from other tasks and ensure that the resource requirements of the current task are met, thereby achieving the technical effect of improving the stability of the scheduled host machine during scheduling.

[0109] In an optional embodiment, after locking the first host machine and the second host machine, wherein the locked host machines are set to prohibit scheduling, the method further includes:

[0110] When the cloud host task is executed, the first host machine and the second host machine are unlocked, wherein the unlocked host machines are set to allow scheduling.

[0111] In an optional embodiment, unlocking can be understood as, but not limited to, when the cloud host task on the host is completed or no longer needs locking protection for other reasons, the system will unlock the host and restore it to a state that allows scheduling. Unlocking means that the host can be selected and scheduled by other cloud host tasks again and participate in resource allocation.

[0112] In an optional embodiment, when the cloud host task is completed, the first host and the second host are unlocked, wherein the unlocked host is set to allow scheduling. This process can be understood as, but not limited to, when the cloud host task is completed, or the host resources are no longer needed for other reasons, the system will unlock the locked first host and the second host, and restore their status to allow scheduling. This means that the host can participate in the resource allocation on the cloud platform again and provide services for other cloud host tasks.

[0113] It should be noted that when the tasks of the first host and the second host are completed or the resource demand no longer exists, the system will unlock the host in time to provide scheduling opportunities for subsequent cloud host tasks, which not only improves resource utilization but also improves scheduling efficiency.

[0114] Through the embodiment of the present application, when the cloud host task is completed, the first host machine and the second host machine are unlocked, wherein the unlocked host machine is set to allow scheduling. By unlocking the host machine, the technical purpose of unlocking after the cloud host task is completed is achieved, thereby achieving the technical effect of improving scheduling efficiency and resource utilization.

[0115] In an optional embodiment, after determining a scheduling host matching the cloud host task from at least two sets of host machines to execute the cloud host task, the method further includes:

[0116] When the cloud host task is completed, the host properties of the scheduling host are updated, and the updated scheduling host is re-aggregated and grouped.

[0117] It should be noted that after the cloud host task is executed, the system will update the host properties of the scheduled host to ensure the real-time and accuracy of the host resource status information. Subsequently, the system will regroup the scheduled hosts according to the updated host properties and assign them to new host groups, so that changes in host resources can be reflected in a timely manner, improving the accuracy and efficiency of scheduling.

[0118] Through the embodiment of the present application, when the cloud host task is completed, the host properties of the scheduling host are updated, and the updated scheduling host is re-aggregated and grouped. This achieves the technical purpose of being able to reflect the changes in host resources in a timely manner, and further achieves the technical effect of improving the scheduling efficiency of the cloud platform cloud host.

[0119] As an optional solution, in the process of determining a scheduling host matching the cloud host task from at least two sets of host machines to execute the cloud host task, the method further includes:

[0120] S2-1, obtain other cloud host tasks that need to be scheduled;

[0121] S2-2, determining a scheduling host machine matching other cloud host tasks from at least two sets of host machines other than the scheduling host machine matching the cloud host task, to execute the other cloud host tasks.

[0122] It should be noted that after a host is scheduled for a cloud host task, the host being called is set as the target host. At this time, if there are other cloud tasks that need to schedule the host, the tasks of other cloud hosts that need to be scheduled are obtained, and the host matching the cloud tasks of the other hosts that need to be scheduled is determined from the host except the target host. If the corresponding host is successfully matched, the host can be called to complete the cloud host task without waiting for the cloud task of the host being scheduled to be completed. By obtaining other cloud host tasks that need to be scheduled and matching cloud hosts that can be matched from the unscheduled hosts, the host being scheduled cannot be matched, so that the concurrent execution of cloud tasks can be achieved, and the stability problem caused by the scheduling of the ongoing host can be prevented, thereby improving the execution efficiency of cloud host tasks and the stability of the host when it is scheduled.

[0123] In addition, when the cloud host task completes the cloud host task by scheduling the host machine, the scheduled host machine is released. The scheduled host machine is no longer used as the target host machine, but as a host machine that can be scheduled by other cloud tasks, which increases the resource utilization of the host machine.

[0124] Through the embodiment of the present application, other cloud host tasks that need to be scheduled are obtained; and scheduling hosts that match other cloud host tasks are determined from at least two sets of host machines other than the scheduling host machine that matches the cloud host task, so as to execute other cloud host tasks. By obtaining other cloud host tasks that need to be scheduled and matching cloud hosts that can be matched from unscheduled hosts, the host machine that is being scheduled cannot be matched, thereby achieving the technical purpose of being able to realize concurrent execution of cloud tasks and preventing the problem of decreased stability caused by the scheduling of the ongoing host machine, thereby achieving the technical effect of improving the execution efficiency of cloud host tasks and the stability of the host machine when it is scheduled.

[0125] As an optional solution, the above-mentioned cloud platform cloud host scheduling method is applied to the scenario of concurrent scheduling of cloud hosts.

[0126] Optionally, in this embodiment, a scheduling algorithm is designed to more quickly and reasonably schedule the cloud host to the host machine. The specific contents are as follows:

[0127] (1) First, all hosts must be grouped, and grouping requires a grouping standard. This embodiment performs a certain operation based on the host's CPU remaining, memory remaining, associated network, associated storage, architecture information, etc., and finally obtains a result, and groups are grouped according to this result. The specific operation method is: divide the host's attributes into qualitative and quantitative types. There are two quantitative operations for the host, one is the CPU remaining , remaining memory , when the difference in the qualitative attributes between two hosts is less than m, they are considered the same. For the qualitative attributes of the host, the concatenation string method is adopted. First, the storage keys associated with the host are sorted to obtain: datastore_keys = {d1, d2, d3, d4}; then the network keys associated with the host are sorted to obtain: network_keys = {net1, net2, net3, net4}; then it is concatenated with other parameters into the original string: origin_str = datastore_keys + network_keys + cpu architecture + host model; then it is hashed to obtain the final string: = H(origin_str). Each host machine thus gets three scores: , , .

[0128] (2) Next, we need to group the hosts according to these three scores. For quantitative scores, such as CPU and memory, we do not require them to be completely equal to be grouped together. Instead, we give them a deviation range m. That is, if the deviation is within the range m, they can be considered the same. For qualitative scores, they must be exactly the same to be considered the same. Therefore, the method to determine whether two hosts are grouped together is as follows:

[0129] group_flag = | - | < m

[0130] and | - |< m

[0131] and =

[0132] (3) After grouping, several groups are obtained. Each group contains several hosts with the same configuration. The optional Figure 3As shown, there are hosts A, B and C in group one, there are hosts D and F in group two, and there are hosts G, H, I and K in group three.

[0133] If there is a cloud host (available hosts are hosts A, B, C, D, E, F) to perform scheduling, first Figure 3 Each group searches for its own available host, randomly selects one, and locks it. The options are as follows Figure 4 As shown, in Figure 3 In the scenario, hosts A and F become locked, and the available hosts of this cloud host can be changed as follows: Figure 5 As shown, when requesting a host, there are host A, host B, host C, host D, and host F, and at this time, host A and host F are locked and participate in the execution scheduling.

[0134] (4) If there is another cloud host to perform scheduling (the available hosts are D, F, G, H, I, K), loop through each available host, search from the host group, lock one in each group, and the others will not participate in scheduling. The optional changes in host grouping are as follows: Figure 6 As shown, in this case, host A is locked in group 1, hosts D and F are locked in group 2, and host G is locked in group 3. The optional changes of the hosts participating in the scheduling of this cloud host are as follows: Figure 7 As shown in the figure, the available hosts for the requesting host are host G, host H, host I, host K, host D, and host F. At this time, host G and host D are locked. Because the hosts locked this time are different from the hosts locked by the cloud host scheduling described in (3), this scheduling can be executed concurrently with (3) without waiting for the cloud host scheduling of (3) to be completed.

[0135] (5) Optional Figure 8 As shown, continued Figure 5 In the scenario of , if another cloud host is used to perform scheduling, the available hosts are D, F, G, H, I, and K. However, hosts D and F in the host group are currently performing scheduling and are locked. In this case, scheduling needs to wait for the previous host scheduling to be completed and for hosts D and F to be released before scheduling can begin.

[0136] (6) If the host scheduling of a cloud host is completed at this time and the cloud host selects a host, the configuration (CPU, memory) of the newly scheduled cloud host must be added to the configuration, the grouping score must be recalculated, and the grouping must be re-performed.

[0137] In an optional embodiment, a cloud host concurrency method based on group isolation is proposed. The specific implementation process is as follows:

[0138] (1) First, perform grouping parameter calculation on the hosts according to the host grouping calculation method.

[0139] (2) The host machines are grouped according to the results of the grouping operation, and the host machine groups are marked in the database.

[0140] (3) When the cloud host executes scheduling, it first obtains available hosts based on the cloud host creation parameters. The number of available hosts obtained here may be many.

[0141] (4) Traverse these available hosts and search in the host group to see if there are any hosts in the group that are not participating in the scheduling. If not, wait for other scheduling tasks to complete and release the host. If there are any hosts in the group that are not participating in the scheduling, randomly select one from the group, lock it, and participate in the scheduling. In this way, a host is obtained from different groups to participate in the scheduling.

[0142] (5) After the scheduling is completed, the cloud host obtains a target host and recalculates the grouping of the target host. The calculation here needs to take into account the configuration parameters of the new cloud host. After the new grouping of the host is completed, the lock is released and the scheduling is completed.

[0143] (6) Each module has a timed task inside, which finds the messages that need to be processed according to the priority of the messages in the queue. Then, it determines whether the message is to be processed by this module based on the message type. If so, it is removed from the queue and then placed in the business module for processing. If not, the priority of the message is recalculated and the priority of the message in the queue is updated.

[0144] To further illustrate, the optional Fig. 9 As shown, when host A, host B, and host C are in waiting, these three hosts can be selected for scheduling. When host A, host B, and host C are selected for scheduling after a period of time, host A, host B, and host C are in a locked scheduling state and can no longer be selected for scheduling by other tasks.

[0145] Through the embodiments of the present application, an algorithm for host grouping calculation is designed using the idea of ​​grouping, and host machines with consistent configuration and resource attributes are grouped. The idea of ​​isolation is adopted, and when host machines in the same group participate in cloud host scheduling, they have equal chances of being scheduled and will not affect each other. They are isolated and executed concurrently, which improves the execution efficiency of scheduling without affecting the accuracy of scheduling.

[0146] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0147] In this embodiment, a scheduling device for a cloud platform cloud host is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and will not be repeated hereafter. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0148] Fig.10 is a structural block diagram of a scheduling device of a cloud platform cloud host according to an embodiment of the present application, such as Fig.10 As shown, the device comprises:

[0149] A first acquisition unit 1002 is used to acquire at least two host machines;

[0150] The grouping unit 1004 is used to aggregate and group at least two hosts according to the host attributes of the host machines to obtain at least two host sets, wherein the host attributes of the hosts in the same host set meet similar conditions;

[0151] The second acquisition unit 1006 is used to acquire the cloud host tasks that need to be scheduled;

[0152] The determination unit 1008 is used to determine a scheduling host machine that matches the cloud host task from at least two sets of host machines to execute the cloud host task.

[0153] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0154] As an optional scheme, the grouping unit 1004 includes: a grouping module, which is used to aggregate and group at least two host machines according to the qualitative attributes and quantitative attributes of the host machines to obtain at least two groups of host machine sets, wherein the qualitative attributes of the host machines in the same group of host machine sets are the same, and the quantitative attribute deviations of the host machines in the same group of host machine sets are within a preset range.

[0155] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0156] As an optional solution, the grouping module includes: a first acquisition submodule, used to acquire the CPU remaining amount of the first host machine, wherein the quantitative attribute of the first host machine includes the CPU remaining amount.

[0157] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0158] As an optional solution, the grouping module further includes: a second acquisition submodule, configured to acquire the remaining amount of memory of the second host machine, wherein the quantitative attribute of the second host machine includes the remaining amount of memory.

[0159] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0160] As an optional solution, the grouping module further includes: a third acquisition submodule, used to obtain an identifier of the third host machine obtained by string concatenation and hash operation, wherein the qualitative attribute of the third host machine includes the identifier.

[0161] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0162] As an optional solution, the third acquisition submodule includes: an acquisition subunit, which is used to obtain a first string of sorted and spliced ​​storage ids of the third host, a second string of sorted and spliced ​​network ids of the third host, a CPU architecture attribute of the third target host, and a model attribute of the third host; a sorting subunit, which is used to sort the first string and the second string to obtain a sorted first string and a sorted second string; a splicing subunit, which is used to splice the sorted first string and the sorted second string to obtain a spliced ​​string; an adding subunit, which is used to add the CPU architecture attribute and the model attribute to the end of the spliced ​​string to obtain a target string; and an operation subunit, which is used to perform a hash operation on the target string to obtain an identifier.

[0163] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0164] As an optional solution, the determination unit 1008 includes: a first determination module, used to determine a first host machine that matches the cloud host task from a first host machine set, and to determine a second host machine that matches the cloud host task from a second host machine set, wherein the scheduling host machine includes the first host machine and the second host machine.

[0165] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0166] As an optional solution, the first determination module includes: a locking submodule, which is used to lock the first host machine and the second host machine, wherein the locked host machines are set to prohibit scheduling.

[0167] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0168] As an optional solution, the locking submodule includes: an unlocking subunit, which is used to unlock the first host machine and the second host machine when the cloud host task is completed, wherein the unlocked host machine is set to allow scheduling.

[0169] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0170] As an optional solution, the determination unit 1008 includes: an updating module, which is used to update the host properties of the scheduling host machine when the cloud host task is completed, and re-aggregate and group the updated scheduling host machines.

[0171] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0172] As an optional solution, the determination unit 1008 includes: an acquisition module, used to obtain other cloud host tasks that need to be scheduled; and a second determination module, used to determine, from at least two groups of host machine sets other than the scheduling host machine that matches the cloud host task, a scheduling host machine that matches the other cloud host tasks, to execute the other cloud host tasks.

[0173] For specific embodiments, reference may be made to the examples shown in the above-mentioned cloud platform cloud host scheduling method, which will not be described in detail in this example.

[0174] It should be noted that the above-mentioned virtual devices (modules, units, sub-modules, sub-units, components, etc.) can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to this: the above-mentioned virtual devices are all located in the same processor; or, the above-mentioned virtual devices are located in different processors in any combination.

[0175] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0176] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0177] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0178] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0179] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0180] An embodiment of the present application further provides a computer program product, including a computer program, wherein when the computer program is configured to be executed by a processor, the computer program performs the steps in any of the above method embodiments.

[0181] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0182] Obviously, those skilled in the art should understand that the above-mentioned virtual devices or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0183] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for scheduling a cloud host on a cloud platform, characterized in that: include: Get at least two hosts; Aggregating and grouping the at least two hosts according to the host attributes of the host machines to obtain at least two host sets, wherein the host attributes of the hosts in the host sets of the same group meet similar conditions; Get the cloud host tasks that need to be scheduled; A scheduling host machine matching the cloud host task is determined from the at least two sets of host machines to execute the cloud host task.

2. The method according to claim 1, characterized in that The step of aggregating and grouping the at least two host machines according to the host attributes of the host machines to obtain at least two sets of host machines includes: According to the qualitative attributes and quantitative attributes of the host machines, the at least two host machines are aggregated and grouped to obtain the at least two host machine sets, wherein the qualitative attributes of the host machines in the same group of the host machine sets are the same, and the quantitative attribute deviations of the host machines in the same group of the host machine sets are within a preset range.

3. The method according to claim 2, characterized in that In a case where the at least two host machines include the first host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative attributes and the quantitative attributes of the host machines to obtain the at least two sets of host machines, the method further includes: The remaining CPU amount of the first host machine is obtained, wherein the quantitative attribute of the first host machine includes the remaining CPU amount.

4. The method according to claim 2, characterized in that: In the case where the at least two host machines include the second host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative attributes and the quantitative attributes of the host machines to obtain the at least two sets of host machines, the method further includes: Obtaining a remaining amount of memory of the second host machine, wherein the quantitative attribute of the second host machine includes the remaining amount of memory.

5. The method according to claim 2, characterized in that: In the case where the at least two host machines include a third host machine, in the process of aggregating and grouping the at least two host machines according to the qualitative attributes and the quantitative attributes of the host machines to obtain the at least two sets of host machines, the method further includes: An identifier of the third host machine is obtained by string concatenation and hash operation, wherein the qualitative attribute of the third host machine includes the identifier.

6. The method according to claim 5, characterized in that The obtaining of the identifier of the third host obtained by string concatenation and hash operation includes: Obtaining a first string of sorted storage IDs of the third host, a second string of sorted network IDs of the third host, a CPU architecture attribute of the third target host, and a model attribute of the third host; Sorting the first character string and the second character string to obtain a sorted first character string and a sorted second character string; Concatenate the sorted first character string and the sorted second character string to obtain a concatenated character string; Add the CPU architecture attribute and the model attribute to the end of the concatenated string to obtain a target string; A hash operation is performed on the target character string to obtain the identifier.

7. The method according to claim 1, characterized in that In a case where the at least two host sets include a first host set and a second host set, determining a scheduling host matching the cloud host task from the at least two host sets includes: A first host matching the cloud host task is determined from the first host set, and a second host matching the cloud host task is determined from the second host set, wherein the scheduling host includes the first host and the second host.

8. The method according to claim 7, characterized in that After determining the first host machine that matches the cloud host task from the first host machine set, and determining the second host machine that matches the cloud host task from the second host machine set, the method further includes: locking the first host machine and the second host machine, wherein the locked host machines are set to prohibit scheduling.

9. The method according to claim 8, characterized in that After locking the first host machine and the second host machine, wherein the locked host machines are set to prohibit scheduling, the method further includes: When the cloud host task is executed, the first host machine and the second host machine are unlocked, wherein the unlocked host machines are set to allow scheduling.

10. The method according to any one of claims 1 to 9, characterized in that After determining, from the at least two sets of host machines, a scheduling host machine matching the cloud host task to execute the cloud host task, the method further includes: When the cloud host task is executed, the host attributes of the scheduling host are updated, and the updated scheduling host is re-aggregated and grouped.

11. The method according to any one of claims 1 to 9, characterized in that In the process of determining, from the at least two sets of host machines, a scheduling host machine that matches the cloud host task to execute the cloud host task, the method further includes: Get other cloud host tasks that need to be scheduled; A scheduling host machine matching the other cloud host tasks is determined from the at least two sets of host machines except the scheduling host machine matching the cloud host task to execute the other cloud host tasks.

12. A scheduling device for a cloud platform cloud host, characterized in that: include: A first acquisition unit, used to acquire at least two host machines; a grouping unit, configured to aggregate and group the at least two hosts according to the host attributes of the host machines to obtain at least two host sets, wherein the host attributes of the hosts in the host sets of the same group meet similar conditions; The second acquisition unit is used to acquire the cloud host tasks that need to be scheduled; The determination unit is used to determine a scheduling host machine that matches the above-mentioned cloud host task from the above-mentioned at least two groups of host machine sets, so as to execute the above-mentioned cloud host task.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 11 when executed by a processor.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 11 are implemented.

15. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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