Cloud platform resource scheduling method and apparatus, and computer device, storage medium and program product

Abstract

The present application relates to the technical field of computers. Disclosed are a cloud platform resource scheduling method and apparatus, and a device, a medium and a product. The method is applied to a data center comprising several power systems, wherein each power system comprises a host machine, the data center comprises several host groups, and each host group comprises cloud hosts. The method comprises: identifying an access host group, and determining the type of the access host group, so as to create a target cloud host (S101); when the access host group is a power anti-affinity host group, acquiring a target power system in a data center, and storing in a candidate list a host machine in the target power system, wherein the target power system is a power system in which the target cloud host can be placed (S102); and for each host machine in the candidate list, acquiring a first score and a second score, so as to determine a power system bound to the target cloud host, wherein the first score is a score for the target power system, and the second score is a score for the host machine in the target power system (S103).

Description

A cloud platform resource scheduling method, apparatus, computer equipment, storage medium, and program product.

[0001] Related applications

[0002] This application claims priority to Chinese patent application filed on December 3, 2024, with application number 202411765394X, entitled "A cloud platform resource scheduling method, apparatus, device, medium and product", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of computer technology, specifically to a cloud platform resource scheduling method, apparatus, computer equipment, storage medium, and program product. Background Technology

[0004] A cloud server group is a logical division of cloud servers, and the elastic cloud servers in a cloud server group follow a unified policy.

[0005] In related technologies, cloud host groups employ four strategies: forced affinity, forced anti-affinity, affinity, and anti-affinity. Affinity refers to enabling proximity deployment, enhancing network capabilities, achieving nearest routing in communication, and reducing network overhead. Anti-affinity, for high reliability considerations, aims to distribute instances as widely as possible. The binding of cloud hosts to host machines within the host group is random. If a host machine fails, the cloud hosts bound to it will also fail, failing to meet high availability requirements.

[0006] Therefore, a cloud platform resource scheduling method that can improve host resource utilization is needed. Summary of the Invention

[0007] According to various embodiments of this application, this application provides a cloud platform resource scheduling method.

[0008] In a first aspect, this application provides a cloud platform resource scheduling method. The method is applied to a data center containing several power systems, where each power system contains a host machine, and the data center includes several host groups, each containing a cloud host. Executed by a computer device, the method includes: identifying an access host group and determining the type of the access host group to create a target cloud host; if the access host group is a power anti-affinity host group, obtaining a target power system in the data center and storing the host machines within the target power system in a candidate list; the target power system is a power system suitable for hosting the target cloud host; and for each host machine in the candidate list, obtaining a first score and a second score to determine the power system bound to the target cloud host; the first score is a score for the target power system, and the second score is a score for the host machines within the target power system.

[0009] In one optional implementation, identifying the access host group and determining the type of the access host group includes: comparing the power links of each host in the access host group and determining the type of the access host group based on the comparison results; and, if the power links are independent of each other, the access host group is a power anti-affinity host group.

[0010] In one optional implementation, obtaining the target power system in the data center includes: determining whether each power system in the data center has a second cloud host, wherein the second cloud host is a cloud host of the same type as the target cloud host; if the power system does not have a second cloud host, the power system is not the target power system; and if the power system does not have a second cloud host, the power system is the target power system.

[0011] In one optional implementation, obtaining the first score includes: obtaining energy consumption information of each power system within the data center to determine the average energy consumption, maximum energy consumption, and first energy consumption value of the data center; the first energy consumption value is the energy consumption information of the power system corresponding to the host in the candidate list; and obtaining the first score of the host in the candidate list based on the average energy consumption, maximum energy consumption, and first energy consumption value of the data center.

[0012] In one alternative implementation, obtaining the second score includes: obtaining evaluation metrics of the host machine, wherein the evaluation metrics include at least one of resource utilization and failure frequency; and, based on the evaluation metrics, collecting evaluation information of the host machine to determine the second score of the host machine.

[0013] In this embodiment, a second score for the host machine is determined based on the host machine's evaluation metrics; the evaluation metrics include at least one of resource utilization and failure frequency.

[0014] In one alternative implementation, obtaining a first score and a second score to determine the power system bound to the target cloud host includes: obtaining a first score, a second score, and a weight sequence of the host; matching the weight sequence with the first score and the second score; obtaining a third score based on the first score, the second score, and the weight sequence; and determining the power system bound to the target cloud host based on the third score.

[0015] Secondly, this application provides a cloud platform resource scheduling device, comprising: an identification module for identifying an access host group and determining the type of the access host group to create a target cloud host; a filtering module for obtaining a target power system in a data center when the access host group is a power anti-affinity host group, and storing the host machines within the target power system in a candidate list; the target power system is a power system suitable for hosting the target cloud host; and a scoring module for obtaining a first score and a second score for each host machine in the candidate list to determine the power system bound to the target cloud host; the first score is a score for the target power system, and the second score is a score for the host machines within the target power system.

[0016] Thirdly, this application provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer-readable instructions, and the processor executes the computer-readable instructions to perform the cloud platform resource scheduling method of the first aspect or any corresponding embodiment described above.

[0017] Fourthly, this application provides a computer-readable storage medium storing computer-readable instructions, which are used to cause a computer to execute the cloud platform resource scheduling method of the first aspect or any corresponding embodiment described above.

[0018] Fifthly, this application provides a computer program product, including computer-readable instructions, which are used to cause a computer to execute the cloud platform resource scheduling method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the disclosed drawings without creative effort.

[0020] Figure 1 is a flowchart illustrating a cloud platform resource scheduling method according to an embodiment of this application;

[0021] Figure 2 is a flowchart illustrating another cloud platform resource scheduling method according to an embodiment of this application;

[0022] Figure 3 is a schematic diagram of a power anti-affinity scheduling architecture model according to an embodiment of this application;

[0023] Figure 4 is a schematic diagram of the power system host management module according to an embodiment of this application;

[0024] Figure 5 is a diagram showing the cloud host placement result of the cloud platform resource scheduling method according to an embodiment of this application;

[0025] Figure 6 is a structural block diagram of a cloud platform resource scheduling device according to an embodiment of this application;

[0026] Figure 7 is a schematic diagram of the hardware structure of a computer device according to an embodiment of this application. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0029] Cloud host groups have four strategies: forced affinity, forced anti-affinity, affinity, and anti-affinity. In the communications field, affinity refers to enabling local deployment, enhancing network capabilities, achieving local routing in communications, and reducing network overhead. Anti-affinity refers to distributing instances as much as possible for high reliability considerations.

[0030] In related technologies, the binding between cloud servers and host machines within a host group is random. If a host machine fails, the cloud server bound to the host machine will also fail, failing to meet the requirements of high availability.

[0031] This application provides a cloud platform resource scheduling method. By identifying access host groups and determining their types, a target cloud host is created. Then, the power systems in the data center are screened, and target host machines in power systems suitable for hosting the target cloud host are stored in a candidate list. Based on the ratings of each host machine in the candidate list, the power system bound to the target cloud host is determined. This ensures reasonable resource allocation, improves system stability, and optimizes data center management.

[0032] According to an embodiment of this application, a cloud platform resource scheduling method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] This embodiment provides a cloud platform resource scheduling method. The method is applied to a data center containing several power systems. Each power system contains a host machine, and the data center includes several host groups, each containing a cloud host. Figure 1 is a flowchart of the cloud platform resource scheduling method according to an embodiment of this application. As shown in Figure 1, the process includes the following steps:

[0034] Step S101: Identify the access host group and determine the type of the access host group in order to create the target cloud host.

[0035] Data centers include several power systems. To facilitate power system management, each power system can be uniquely numbered. This allows for unified management within the data center. In some alternative implementations, power systems can be numbered based on their operational status or geographical location. Similarly, host groups within the data center can also be numbered.

[0036] The data center can store the IDs and type information of each host group. After identifying the access host group, its ID can be obtained to determine its type. Monitoring software can also be used to view the workload distribution of the access host group. In some optional implementations, a message queue service with multiple replicas is input to the access host group. If the replicas are concentrated on one or a few hosts, the access host group can be identified as a power-affinity host group; if they are evenly distributed across different hosts, the access host group can be identified as a power-unaffinity host group.

[0037] If the access host group is a power-unaffinity host group, a target cloud host can be created. If the access host group is not a power-unaffinity host group, the anti-affinity rule is not met, and the target cloud host cannot be created.

[0038] Step S102: If the access host group is a power anti-affinity host group, obtain the target power system in the data center and store the host in the target power system in the candidate list; the target power system is a power system that can house the target cloud host.

[0039] When acquiring a target power system within a data center, it is first necessary to obtain the business applications of the cloud servers and the functionality of the cloud platform on which the cloud servers reside. Then, based on the business applications and cloud platform functionality, it is determined whether the target cloud servers can be placed within the power system.

[0040] In a practical application, the anti-affinity rule states that cloud hosts of the same type cannot be placed in the same power system. Therefore, if there is a cloud host of the same type as the target cloud host in the power system, the target cloud host cannot be placed in the power system.

[0041] The target power system is defined as the power system that can accommodate the target cloud host, and then the host machines within the target power system are added to the candidate list. This means that the target cloud host can select one host machine from the candidate list to bind to.

[0042] Step S103: For each host in the candidate list, obtain a first score and a second score to determine the power system bound to the target cloud host; the first score is the score for the target power system, and the second score is the score for the host within the target power system.

[0043] The first score is for the target power system. First, the primary indicator system for the power system score must be clearly defined. This indicator system can be determined by staff based on actual conditions and may include power supply reliability, power quality, and power supply capacity. Then, information on the corresponding indicators is collected based on actual power system operating data. This can be done by installing power monitoring equipment on the power system to obtain real-time data. Finally, according to the established scoring criteria, each indicator is assigned a corresponding weight and quantitatively scored to obtain the first score.

[0044] The second score is a score for the host machine within the target power system, which can be determined by establishing a second scoring indicator system for the host machine. This second scoring indicator system may include the host machine's hardware configuration, resource utilization, etc. Then, based on this second scoring indicator system, corresponding indicator information is collected. In some optional implementations, relevant monitoring tools can be used to collect the indicator information. Then, for each indicator, a quantitative score is calculated according to pre-set standards and the collected actual data. Finally, based on the scores of each indicator and their assigned weights, the second score of the host machine is calculated using a weighted summation method.

[0045] After obtaining the first and second scores of each host in the candidate list, the first and second scores can be weighted and merged. Based on the weighted fusion result, a host can be selected from the candidate list for binding. In some optional implementations, to select a low-energy power system to place the cloud host, the weight corresponding to the first score can be increased.

[0046] The cloud platform resource scheduling method provided in this embodiment identifies access host groups and determines their types to create target cloud hosts. When the access host group is a power anti-affinity group, it obtains power systems capable of hosting the target cloud host and stores the hosts within those power systems in a candidate list. Then, it scores each host in the candidate list and determines the power system bound to the target cloud host based on the scoring results. In this scheme, by filtering out power systems that cannot host the target cloud host and then placing the target cloud host based on the scoring results, efficient resource utilization can be achieved, avoiding resource idleness or overuse.

[0047] This embodiment provides a cloud platform resource scheduling method. The method is applied to a data center containing several power systems. Each power system contains a host machine, and the data center includes several host groups, each containing a cloud host. Figure 2 is a flowchart of the cloud platform resource scheduling method according to an embodiment of this application. As shown in Figure 2, the process includes the following steps:

[0048] Step S201: Identify the access host group and determine the type of the access host group in order to create the target cloud host.

[0049] For details, please refer to step S101 of the embodiment shown in Figure 1, which will not be repeated here.

[0050] In some optional implementations, step S201 above includes:

[0051] Step S2011: Compare the power links of each host in the access host group, and determine the type of access host group based on the comparison results.

[0052] The type of access host group can be determined by comparing whether the power links of each host within the group are independent. If the power links are not independent, the access host group is not a power-unaffinity host group.

[0053] By examining the load distribution strategy of the access host group, it can be determined whether to avoid binding hosts in the same access host group to a common power link. If the power supply of the hosts in the access host group is relatively independent and the correlation is low, then the access host group is an anti-affinity host group; otherwise, it is an affinity host group.

[0054] In step S2012, when the power links are independent of each other, the access host group is a power anti-affinity host group.

[0055] When power links are independent of each other, the access host group is a power anti-affinity host group.

[0056] By comparing the power links of each host within the access host group and determining the type of access host group based on the comparison results, power-affinity-resistant host groups can be filtered out. This facilitates the subsequent creation of target cloud hosts.

[0057] Step S202: If the access host group is a power anti-affinity host group, obtain the target power system in the data center and store the host in the target power system in the candidate list; the target power system is a power system that can house the target cloud host.

[0058] For details, please refer to step S102 of the embodiment shown in Figure 1, which will not be repeated here.

[0059] In some optional implementations, step S202 above includes:

[0060] Step S2021: Determine whether there is a second cloud host in each power system within the data center. The second cloud host is a cloud host of the same type as the target cloud host.

[0061] First, basic information about the target cloud host and cloud hosts within various power systems can be obtained. Then, this information can be compared to determine if a second cloud host exists within the power system. The basic information can be determined by staff based on the actual situation and may include the cloud host's network functions, operating system type, etc.

[0062] In step S2022, if there is no second cloud host in the power system, the power system is not the target power system.

[0063] Step S2023: In the absence of a second cloud host in the power system, the power system is the target power system.

[0064] There are no two cloud servers of the same type within the same power system, which can reduce the impact of power outages and optimize the use of power resources.

[0065] By determining whether a cloud server of the same type as the target cloud server exists within the power system, it can be determined whether the power system is suitable for hosting the target cloud server. This ensures the rationality of resource allocation and avoids resource waste.

[0066] Step S203: For each host in the candidate list, obtain a first score and a second score to determine the power system bound to the target cloud host; the first score is the score for the target power system, and the second score is the score for the host within the target power system.

[0067] Specifically, step S203 includes:

[0068] Step S2031: Obtain the first score, second score, and weight sequence of the host machine; the weight sequence is matched with the first score and second score.

[0069] In some optional implementations, step S2031 above includes:

[0070] Step a1: Obtain energy consumption information of each power system in the data center to determine the average energy consumption, maximum energy consumption, and first energy consumption value of the data center; the first energy consumption value is the energy consumption information of the power system corresponding to the host in the candidate list.

[0071] Energy consumption information can be obtained by installing energy monitoring equipment at key nodes of the power system within the data center. Then, the average and maximum energy consumption of the power system within the data center can be calculated.

[0072] Step a2: Based on the average energy consumption, maximum energy consumption, and first energy consumption value of the data center, obtain the first score of the host machine in the candidate list.

[0073] Based on the average energy consumption, maximum energy consumption, and first energy consumption value of the data center, obtain the first score of each host in the candidate list.

[0074] The first score can be obtained by the following formula:

[0075] Among them, W Pi The first score for the i-th host machine; P max P represents the maximum energy consumption. i Let be the energy consumption value of the i-th host, i.e., the first energy consumption value; n is the number of power systems in the data center. This represents the average energy consumption of the power system within the data center.

[0076] Based on the energy consumption information of each power system in the data center, the average energy consumption, maximum energy consumption, and first energy consumption value of the data center are determined to obtain the first score of the host in the candidate list, thereby determining the placement location of the target cloud host. This can avoid the situation of excessive energy consumption pressure on the power system in the data center and improve the stability of the method.

[0077] In some optional implementations, step S2031 above further includes:

[0078] Step b1: Obtain the host machine's evaluation metrics, which include at least one of resource utilization and failure frequency.

[0079] The evaluation metrics for the host machine can be determined by staff based on actual circumstances. Resource utilization can include host machine memory utilization, host machine storage resource utilization, and network bandwidth utilization, among others.

[0080] Step b2: Based on the evaluation metrics, collect the host machine's evaluation information to determine the host machine's second score.

[0081] Based on the evaluation indicators, relevant evaluation information is collected.

[0082] When resource utilization is included as an evaluation metric, performance monitoring tools can be used to collect information about the host machine under these metrics.

[0083] When the evaluation metric includes failure frequency, the frequency of host machine failures can be determined by tracking and recording the host machine's operating status.

[0084] Then, based on the weights assigned to the evaluation information, the host machine's second score is calculated using a weighted summation method.

[0085] The second score can be obtained using the following formula:

[0086] Among them, W hi The second score for the i-th host machine; x is the number of evaluation metrics; α ji W represents the weight of the j-th evaluation metric for the i-th host machine. ji Let be the score of the i-th host machine on the j-th evaluation metric.

[0087] Based on the host machine's evaluation metrics, a second score is determined; these metrics include at least one of resource utilization and failure frequency. This allows for the acquisition of information about the host machine's operational status, improving the rationality of subsequent cloud server placement.

[0088] Step S2032: Obtain the third score based on the first score, the second score, and the weight sequence.

[0089] The weight sequence corresponds to the first and second scores. This can be determined by staff based on the actual situation. The third score is obtained by weighted summation based on the first score, the second score, and the weight sequence.

[0090] In a practical application, the third rating can be obtained using the following formula: W i =β1W pi +β2W hi

[0091] Among them, W i β1 is the third rating of the i-th host machine; β1 is the weight of the first rating of the i-th host machine; W piβ1 represents the first score of the i-th host machine; β2 represents the weight of the second score of the i-th host machine; W hi The second score for the i-th host machine.

[0092] Step S2033: Based on the third score, determine the power system bound to the target cloud host.

[0093] Based on the third-ranking scores of each host in the candidate list, the power system to which the target cloud host is bound is determined. In some optional implementations, the host with the highest third-ranking score in the candidate list is selected as the target host, and the target cloud host is bound to the power system of the target host.

[0094] The cloud platform resource scheduling method provided in this embodiment weights and fuses the first and second scores to obtain a third score. This allows for quantitative evaluation of the host machine, thereby determining the power system bound to the target cloud host. It enables the correct scheduling and reasonable placement of cloud hosts.

[0095] This embodiment also provides a power anti-affinity scheduling architecture model, as shown in Figure 3. The power anti-affinity scheduling architecture includes three parts: a CMDB (Configuration Management Database) power system management module, a power system host management module, and an IaaS (Infrastructure as a Service) cloud platform scheduling module. The CMDB power system management module is mainly responsible for monitoring and managing the data center power system. The power system host management module is mainly responsible for managing the physical machines and cloud hosts within the power system, enabling timely detection and adjustment when changes occur in physical machines and cloud hosts. The IaaS cloud platform scheduling module, based on the original scheduling occupancy unit, introduces a power system filter and a power anti-affinity scorer, thereby realizing functions such as the placement and migration of cloud hosts within the access group.

[0096] The CMDB power system management module enables the management of power systems within a data center based on an intelligent operation and maintenance platform and CMDB resource management. This module includes four functions: power system monitoring, power system partitioning, power system number generation, and power system updates. It can manage the power system lifecycle and generate or update power system models based on the data center's power system operation status. It also transmits relevant power system information to the power system host management module or the IaaS cloud platform scheduling module for use by other modules. Furthermore, through power system partitioning, it enables the division and management of different power systems, creating the foundation for power system infrastructure development.

[0097] The power system monitoring function monitors and records the current operation of the power system within the data center to facilitate power system management and guide power dispatching. The power system partitioning function partitions the power system based on its operational status within the data center. This partitioning can be based on factors such as power supply stability and load conditions. Power system number generation generates corresponding identifiers based on the partitioning results, facilitating subsequent power system management and information retrieval. Within the same data center, each power system identifier can be unique. The power system update function adds, deletes, or modifies power system information as needed, in the event of changes to related information or the addition of new power systems.

[0098] The Power System Host Management Module is used to maintain key information regarding the association between power systems and hosts. This includes host management within the power system, cloud host management within the power system, and management of the association relationship between power systems and hosts. Based on the power system division in the CMDB Power System Management Module, it can establish binding relationships between hosts and power systems, thereby managing the hosts within the power system and the cloud hosts running on those hosts. A schematic diagram of the Power System Host Management Module is shown in Figure 4. The Power System Host Management Module includes n power systems, namely Power-1 to Power-n. Each power system has m host groups, namely Host1 to Hostm, and each host group contains several hosts, which can be represented by vm1, vm2, vm3, etc.

[0099] The IaaS cloud platform scheduling module can rationally allocate computing, storage, and network resources based on user needs and the real-time status of platform resources, achieving efficient resource utilization. It can also arrange task scheduling and execution order, processing tasks submitted by different users according to task priority, ensuring that critical business operations are not affected. The IaaS cloud platform scheduling module includes a basic scheduling unit, a power anti-affinity filter, and a power anti-affinity scorer. The basic scheduling unit is used to arrange task scheduling and execution order. The power anti-affinity filter is used to filter out qualified power systems, and the power anti-affinity scorer is used to select which host's power system is more suitable for hosting cloud servers.

[0100] Power anti-affinity filters can be used to screen various power systems within a data center, eliminating power systems that do not contain cloud hosts of the same type, and adding the hosts within the corresponding power systems to a candidate list.

[0101] The power anti-affinity scorer can be used to obtain host machines from the candidate list, score the host machine and the power system corresponding to the host machine, and select a host machine from the candidate list to bind to the cloud host based on the scoring results.

[0102] In a practical application, the power input to the resource pool is a dual-path UPS (Uninterruptible Power Supply). The CMDB power system management module monitors power system energy consumption using the UPS output as the data acquisition point. Based on the actual situation of the power system, six available power systems are identified, and power system numbers are generated. The naming convention for power system numbers is: Data Center-Availability Zone-Power System Keyword. Here, Data Center can be the data center name or number; Availability Zone refers to different areas within the data center; and Power System Keyword can be the name of the power system.

[0103] In the power system host management module, a binding relationship is established between the host machine and the power system. Simultaneously, the association data between the host machine and the power anti-affinity host group is determined, thereby identifying the placement of the cloud hosts.

[0104] In some alternative implementations, a tagging system can be designed to add power system identification tags to each host machine, thereby marking which power system the host machine belongs to, facilitating subsequent management and scheduler operation. In a practical application, the core pseudocode for updating the power system and host machine identification numbers is as follows:

[0105] 1: H_IP = {,,...}

[0106] 2:ch CMDB Manager<-aRoadEqSimpleCode,bRoadEqSimpleCode from UPS

[0107] 2:ch power_number<-watch Power from CMDB Manager

[0108] 3:cronTab SynchronizeHostPowerNumberFromCMDB(H_IP)

[0109] 4:CheckPowerExists(power_number)

[0110] IF NOT_EXISTS->CreatePower(power_number)

[0111] ELSE:->GetPowerByPowerName(power_number)

[0112] 5: UpdateHostPowerNumber(H_IP)

[0113] The newly added power anti-affinity filter and power anti-affinity scorer are deployed to the corresponding resource pool of the IaaS cloud platform management system, thereby supporting the implementation of power anti-affinity scheduling based on the IaaS cloud platform.

[0114] In a practical application, 10 power anti-affinity host groups, namely Group1 to Group10, are connected. Each host group contains six cloud hosts. Using the cloud platform scheduling method provided in this application embodiment, the cloud host placement is shown in Figure 5, where Power1 to Power6 represent six power systems. The six cloud hosts in each power anti-affinity host group are evenly distributed across different power systems. This ensures that cloud hosts within the same host group do not share power systems, thereby achieving high availability at the power system level.

[0115] This embodiment also provides a cloud platform resource scheduling device, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0116] This embodiment provides a cloud platform resource scheduling device, as shown in Figure 6, including:

[0117] The identification module 301 is used to identify the access host group and determine the type of the access host group in order to create the target cloud host.

[0118] The filtering module 302 is used to obtain the target power system in the data center when the access host group is a power anti-affinity host group, and store the host in the target power system into the candidate list; the target power system is a power system that can house the target cloud host.

[0119] The scoring module 303 is used to obtain a first score and a second score for each host in the candidate list to determine the power system bound to the target cloud host; the first score is the score for the target power system, and the second score is the score for the host in the target power system.

[0120] In some alternative implementations, the identification module 301 includes:

[0121] The comparison unit is used to compare the power links of each host in the access host group and determine the type of access host group based on the comparison results.

[0122] The host group determination unit is used to identify the access host group as a power anti-affinity host group when the power links are independent of each other.

[0123] In some alternative implementations, the filtering module 302 further includes:

[0124] The judgment unit is used to determine whether there is a second cloud host in each power system within the data center. The second cloud host is a cloud host of the same type as the target cloud host.

[0125] The first result unit is used to determine if the power system is not the target power system when a second cloud host does not exist in the power system.

[0126] The second result unit is used when the power system is the target power system in the absence of a second cloud host in the power system.

[0127] In some alternative implementations, the scoring module 303 includes:

[0128] The energy consumption information acquisition unit is used to acquire the energy consumption information of each power system in the data center in order to determine the average energy consumption, maximum energy consumption and first energy consumption value of the data center; the first energy consumption value is the energy consumption information of the power system corresponding to the host in the candidate list.

[0129] The first rating acquisition unit is used to obtain the first rating of the host machine in the candidate list based on the average energy consumption, maximum energy consumption, and first energy consumption value of the data center.

[0130] In some alternative implementations, the scoring module 303 includes:

[0131] The evaluation index acquisition unit is used to acquire the evaluation index of the host machine, wherein the evaluation index includes at least one of resource utilization and failure frequency.

[0132] The second rating acquisition unit is used to collect the host machine's evaluation information based on the evaluation indicators in order to determine the host machine's second rating.

[0133] In some alternative implementations, the scoring module 303 includes:

[0134] The information acquisition unit is used to acquire the host machine's first score, second score, and weight sequence; the weight sequence is matched with the first and second scores.

[0135] The third rating acquisition unit is used to obtain the third rating based on the first rating, the second rating, and the weight sequence;

[0136] Placement unit, used to determine the power system bound to the target cloud host based on a third score.

[0137] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0138] In this embodiment, the cloud platform resource scheduling device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0139] This application also provides a computer device having the cloud platform resource scheduling device shown in FIG6 above.

[0140] Please refer to Figure 7, which is a schematic diagram of the structure of a computer device provided in an optional embodiment of this application. As shown in Figure 7, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate with each other using different buses and can be installed on a common motherboard or otherwise as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 uses one processor 10 as an example.

[0141] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0142] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0143] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0144] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0145] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means; Figure 7 shows an example of a connection via a bus.

[0146] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0147] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded over a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the methods shown in the above embodiments are implemented.

[0148] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0149] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0150] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A cloud platform resource scheduling method, wherein, Performed by computer equipment, the method is applied to a data center comprising several power systems, each power system containing host machines, and the data center including several host groups, each host group including cloud hosts; the method includes: Identify the access host group and determine the type of the access host group in order to create the target cloud host; In the case that the access host group is a power-unaffinity host group, the target power system in the data center is obtained, and the hosts within the target power system are stored in a candidate list; the target power system is a power system suitable for hosting the target cloud host; and, For each host in the candidate list, a first score and a second score are obtained to determine the power system bound to the target cloud host; the first score is the score for the target power system, and the second score is the score for the host within the target power system.

2. The method of claim 1, wherein, The process of identifying the access host group and determining the type of the access host group includes: Compare the power links of each host within the access host group, and based on the comparison results, determine the type of the access host group; and, When the power links are independent of each other, the access host group is a power anti-affinity host group.

3. The method of claim 1, wherein, The acquisition of the target power system in the data center includes: Determine whether a second cloud host exists in each power system within the data center, wherein the second cloud host is a cloud host of the same type as the target cloud host; If the second cloud host is not present in the power system, the power system is not the target power system; and, In the absence of the second cloud host in the power system, the power system is the target power system.

4. The method of claim 1, wherein, The process of obtaining the first score includes: The energy consumption information of each power system within the data center is obtained to determine the average energy consumption, maximum energy consumption, and a first energy consumption value of the data center; the first energy consumption value is the energy consumption information of the power system corresponding to the host in the candidate list; and, Based on the average energy consumption, maximum energy consumption, and first energy consumption value of the data center, the first score of the host machine in the candidate list is obtained.

5. The method of claim 1, wherein, The second score is obtained by: Obtain the evaluation metrics of the host machine, wherein the evaluation metrics include at least one of resource utilization and failure frequency; and, Based on the evaluation metrics, the evaluation information of the host machine is collected to determine the second score of the host machine.

6. The method of claim 1, wherein, The process of obtaining the first score and the second score to determine the power system bound to the target cloud host includes: Obtain the first score, the second score, and the weight sequence of the host machine; the weight sequence is matched with the first score and the second score. Based on the first score, the second score, and the weight sequence, a third score is obtained; and, Based on the third score, the power system bound to the target cloud host is determined.

7. A cloud platform resource scheduling apparatus, wherein, The device includes: The identification module is used to identify the access host group and determine the type of the access host group in order to create the target cloud host; The filtering module is used to, when the access host group is a power-unaffinity host group, obtain the target power system in the data center and store the hosts within the target power system in a candidate list; the target power system is a power system suitable for hosting the target cloud host; and, The scoring module is used to obtain a first score and a second score for each host in the candidate list to determine the power system bound to the target cloud host; the first score is the score for the target power system, and the second score is the score for the host in the target power system.

8. A computer device, wherein, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer-readable instructions, the processor executing the steps of the method according to any one of claims 1 to 6 by executing the computer-readable instructions.

9. A computer readable storage medium, wherein, The computer-readable storage medium stores computer-readable instructions for causing a computer to perform the steps of the method according to any one of claims 1 to 6.

10. A computer program product, wherein, It includes computer-readable instructions for causing a computer to perform the steps of the method according to any one of claims 1 to 6.

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