Cloud hard disk speed limiting method and device, equipment, medium and program product

Abstract

The invention relates to the technical field of cloud services, and particularly provides a cloud hard disk speed limiting method, device, equipment, medium and program product.The method comprises the steps that performance parameters of a storage cluster are obtained from a storage engine server side, and the performance parameters comprise a first operation parameter of the storage cluster and a second operation parameter of the storage cluster; storing second operation parameters of a plurality of cloud hard disks deployed in the cluster; determining a service performance threshold value of the storage cluster based on the first operation parameter; based on the service performance threshold value, the first operation parameter and second operation parameters of the multiple cloud hard disks, at least one first target cloud hard disk with the speed to be limited and a flow control parameter associated with at least one target tenant are determined in the multiple cloud hard disks, and the target tenant is the tenant using the first target cloud hard disk; sending a flow control parameter associated with the at least one target tenant to a storage engine server; the flow rate limiting reasonability of the cloud hard disk is improved.

Description

Technical Field

[0001] This disclosure relates to the field of cloud service technology, and in particular to a method, apparatus, device, medium, and program product for limiting cloud disk speed. Background Technology

[0002] In cloud service scenarios, limiting the rate of cloud resources subscribed by tenants is a basic function of cloud services. In storage clusters, limiting sudden traffic to cloud disks can prevent hardware overload failures, protect the overall service stability of the resource pool, and ensure the normal use of services by all users.

[0003] In related technologies, traffic control of cloud disks can be performed through a distributed storage engine server. Specifically, the storage engine server can synchronize traffic control parameters to the cloud disk based on its status when the cloud disk starts up and during service, so that the cloud disk can perform traffic control based on the acquired traffic control parameters.

[0004] However, the solutions for traffic control of cloud disks based on the storage engine server in related technologies usually control the traffic based on the operation status of a single cloud disk or a certain data area of ​​a cloud disk. This closed cloud disk control strategy can easily lead to hardware overload of the storage cluster and cause the risk of pool overflow. Summary of the Invention

[0005] In view of the above problems, this disclosure is made to provide a method, apparatus, device, medium and program product for limiting the speed of cloud disks, so as to improve the rationality of traffic limiting of cloud disks.

[0006] According to a first aspect of this disclosure, a method for limiting the speed of a cloud disk is provided, the method being applied to a policy management server, comprising: The performance parameters of the storage cluster are obtained from the storage engine server, wherein the performance parameters include the first operating parameters of the storage cluster and the second operating parameters of the multiple cloud disks deployed in the storage cluster. The service performance threshold of the storage cluster is determined based on the first operating parameters. Based on the service performance threshold, the first operating parameters, and the second operating parameters of the plurality of cloud disks, at least one first target cloud disk to be rate-limited is determined among the plurality of cloud disks, and traffic control parameters are associated with at least one target tenant, wherein the target tenant is a tenant using the first target cloud disk. The traffic control parameters associated with the at least one target tenant are sent to the storage engine server. The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

[0007] According to a second aspect of this disclosure, a method for limiting the speed of a cloud disk is provided, the method being applied to a business server, including: In response to receiving traffic control parameters associated with each target tenant from the storage engine server, wherein the target tenant is the tenant that needs to be traffic controlled, determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server; Based on the traffic control parameters associated with each target tenant and the traffic limit assessment model, the traffic limit value of the second target cloud disk used by each target tenant is determined; Send the traffic limit of the second target cloud disk to the second target cloud disk in the storage cluster.

[0008] According to a third aspect of this disclosure, a cloud disk speed limiting device is provided, the device being a policy management server, comprising: The monitoring module is configured to obtain performance parameters of the storage cluster from the storage engine server, wherein the performance parameters include first operating parameters of the storage cluster and second operating parameters of multiple cloud disks deployed in the storage cluster. The monitoring module is also configured to determine the service performance threshold of the storage cluster based on the first operating parameters; The monitoring module is also configured to determine at least one first target cloud disk to be rate-limited among the plurality of cloud disks based on the service performance threshold, the first operating parameters and the second operating parameters of the plurality of cloud disks, as well as traffic control parameters associated with at least one target tenant, wherein the target tenant is a tenant using the first target cloud disk. The control module is configured to send traffic control parameters associated with the at least one target tenant to the storage engine server. The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

[0009] According to a fourth aspect of this disclosure, a cloud disk speed limiting device is provided, the device being a service server, comprising: The receiving module is configured to respond to receiving traffic control parameters associated with each target tenant sent by the storage engine server. The target tenant is the tenant that needs to be traffic controlled, determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server. The determination module is configured to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model; The sending module is configured to send the traffic limit value of the second target cloud disk to the second target cloud disk in the storage cluster.

[0010] According to a fifth aspect of this disclosure, a network device is provided, the network device including a memory, a processor, and a computer program stored in the memory, the processor executing the computer program to implement the method of the first aspect or the second aspect.

[0011] According to a sixth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described in the first or second aspect.

[0012] According to a seventh aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first or second aspect.

[0013] The cloud disk speed limiting method, apparatus, device, medium, and program products provided in this disclosure, on the one hand, accurately locate the first target cloud disk to be speed limited and its associated target tenant by synchronously collecting the global first operating parameters of the storage cluster and the second operating parameters of each cloud disk, combined with the cluster service performance threshold. This facilitates user-level traffic control and avoids the problem of insufficient control due to traffic control only on a single cloud disk, which could affect the normal operation of other services. On the other hand, by leveraging the policy synchronization capability of the storage engine server, the traffic control parameters of the target user to be speed limited are transmitted to the business server. The business server then determines the specific traffic limit value of the second target cloud disk used by the target user based on the traffic limit assessment model and sends it to the storage cluster. This achieves tenant-level cloud disk traffic control, effectively suppressing the performance occupation of the cluster by high-load tenants and preventing cluster overload.

[0014] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0015] The above and other objects, features, and advantages of this disclosure will become more apparent from the more detailed description of the embodiments thereof in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the embodiments of this disclosure to explain the disclosure and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0016] Figure 1 This is a schematic diagram illustrating an application scenario of a cloud hard drive speed limiting scheme according to an embodiment of this disclosure.

[0017] Figure 2 This is a flowchart of a cloud hard drive speed limiting method according to an embodiment of this disclosure.

[0018] Figure 3 This is a flowchart of another cloud hard drive speed limiting method according to an embodiment of this disclosure.

[0019] Figure 4 This is a schematic block diagram of the functional modules of a cloud hard drive speed limiting device according to an embodiment of this disclosure.

[0020] Figure 5 This is a schematic block diagram of the functional modules of another cloud hard drive speed limiting device according to an embodiment of this disclosure.

[0021] Figure 6 This is a schematic diagram of a computer program product according to an embodiment of the present disclosure.

[0022] Figure 7 This is a hardware block diagram of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this disclosure more apparent, exemplary embodiments according to this disclosure will now be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this disclosure, and not all embodiments of this disclosure. It should be understood that this disclosure is not limited to the exemplary embodiments described herein.

[0024] To address the aforementioned issues, this disclosure provides a cloud disk speed limiting solution, such as... Figure 1 As shown, Figure 1This diagram illustrates the system architecture of the cloud disk rate limiting scheme provided in this embodiment, including a policy control server 101, a storage engine server 102, a business server 103, and a storage cluster 104. The policy control server 101 obtains multi-level operational status data of the storage cluster 104 from the storage engine server 102, determines the cloud disks in the storage cluster 104 that require rate limiting based on traffic analysis results, and sends the traffic control parameters of the cloud disks to the storage engine server 102. The storage engine server 102 collects multi-level operational status data of the storage cluster 104 and sets the policy... The traffic control parameters determined by the management server 101 are synchronized to the business server 103. The business server 103 can not only mount cloud disks in the storage cluster 104 for tenants to use storage resources, but also receive and match the tenant's traffic control parameters from the storage engine server 102 to calculate the traffic limit of the cloud disk used by the tenant. The storage cluster 104 is used to provide distributed block storage services (such as Ceph) and to carry data reading and writing through cloud disks and store tenant information of cloud disks. It also receives the traffic limit of cloud disks that need to be rate-limited sent by the business server 103 and performs traffic control according to the traffic limit.

[0025] Among them, the policy control server 101, the storage engine server 102, the business server 103, and the storage cluster 104 are different nodes in the cloud service scenario. The policy control server 101 is connected to the storage engine server 102, the storage engine server 102 is connected to the business server 103, and the business server 103 is connected to the storage cluster 104 to implement the cloud disk speed limiting scheme provided in this embodiment.

[0026] Figure 2 A flowchart illustrating an exemplary embodiment of this disclosure of a cloud disk rate limiting method is shown. This cloud disk rate limiting method is applied to a policy management server, such as... Figure 2 As shown, the method in this embodiment of the disclosure may include: Step S201: Obtain the performance parameters of the storage cluster from the storage engine server; The performance parameters include the first operating parameters of the storage cluster and the second operating parameters of the multiple cloud disks deployed in the storage cluster. Step S202: Determine the service performance threshold of the storage cluster based on the first operating parameters; Step S203: Based on the service performance threshold, the first operating parameters and the second operating parameters of multiple cloud disks, determine at least one first target cloud disk to be rate-limited among the multiple cloud disks, and traffic control parameters associated with at least one target tenant, wherein the target tenant is the tenant using the first target cloud disk. Step S204: Send the traffic control parameters associated with at least one target tenant to the storage engine server; The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster. In summary, the cloud disk rate limiting method provided in this disclosure, on the one hand, accurately locates the first target cloud disk to be rate-limited and its associated target tenant by synchronously collecting the global first operating parameters of the storage cluster and the second operating parameters of each cloud disk, combined with the cluster service performance threshold. This facilitates user-level traffic control and avoids the problem of insufficient control due to traffic control only on a single cloud disk, which could affect the normal operation of other services. On the other hand, by leveraging the policy synchronization capability of the storage engine server, the traffic control parameters of the target user to be rate-limited are transmitted to the business server. The business server then determines the specific traffic limit value of the second target cloud disk used by the target user based on the traffic limit assessment model and sends it to the storage cluster. This achieves tenant-level cloud disk traffic control, effectively suppressing the performance occupation of the cluster by high-load tenants and preventing cluster overload.

[0027] The following are Figure 2 The specific implementation methods of each step in the illustrated embodiment are described in detail below: In step S201, the performance parameters of the storage cluster are obtained from the storage engine server; In this embodiment of the disclosure, the performance parameters include first operating parameters of the storage cluster and second operating parameters of multiple cloud disks deployed in the storage cluster; wherein, the first operating parameters are used to characterize the operating status of the storage cluster, and the second operating parameters of the multiple cloud disks include second operating parameters of each cloud disk, which are used to characterize the operating status of the cloud disks.

[0028] It should be noted that, in the embodiments of this disclosure, the policy management server can typically periodically obtain the performance parameters of the storage cluster from the storage engine server to evaluate the operation status of the storage cluster, so as to promptly identify tenants that need to be overloaded and implement tenant rate limiting. The period for the policy management server to evaluate the operation status of the storage cluster can be determined based on actual needs, and this embodiment of the disclosure does not limit this. For example, the policy management server can obtain the performance parameters of the storage cluster from the storage engine server every two days or one day to evaluate the operation status of the storage cluster.

[0029] The first operating parameters may include the number of Object Storage Daemon (OSD) services available to the storage cluster during the current monitoring period, the number of Input / Output Operations Per Second (IOPS) and bandwidth during the current monitoring period, the percentage of Placement Groups (pg) in normal condition, the IOPS of the storage cluster per unit sampling period during the current monitoring period, and / or the IOPS of the storage cluster during historical periods; the unit sampling period is the minimum sampling duration within the monitoring period, which can be determined based on actual needs. This embodiment of the disclosure does not limit this, for example, the monitoring period is 24 hours, and the unit sampling period is 2 hours or 1 hour; and the historical period is a preset historical duration prior to the current time, which can be determined based on actual needs. This embodiment of the disclosure does not limit this, for example, the preset duration is 3600 seconds.

[0030] The second operating parameters include the IOPS of each cloud disk at the current time, the IOPS of each cloud disk at a historical time, and / or the IOPS of each cloud disk in each unit sampling period within the current monitoring period. The historical time is earlier than the start time of the aforementioned historical period. Specifically, it can be determined based on actual needs. This disclosure does not limit this. For example, if the historical period is the time period between 3600 seconds before the current time and the current time, then the historical time is 7200 seconds before the current time.

[0031] In one optional implementation, the process of the policy control server obtaining the performance parameters of the storage cluster from the storage engine server includes: obtaining the performance parameters of the storage cluster from the storage engine server at the end of the current monitoring period.

[0032] In step S202, the service performance threshold of the storage cluster is determined based on the first operating parameters.

[0033] In this embodiment of the disclosure, the service performance threshold of the storage cluster is used to characterize the strength of the service capability of the storage cluster, wherein the larger the service performance threshold, the stronger the service capability of the storage cluster.

[0034] In one optional implementation, the first operating parameters obtained by the policy management server include the number of OSDs, IOPS, and bandwidth that the storage cluster can provide services for in the current monitoring period. The process by which the policy management server determines the service performance threshold of the storage cluster based on the first operating parameters includes: determining the ratio between the storage cluster's IOPS and its bandwidth to obtain the average block size of the storage cluster's read / write IO, wherein the average block size of IO is the average amount of data processed by the storage cluster for all IO requests per unit time; and then, determining the service performance threshold of the storage cluster based on the average block size of IO, the number of OSDs, and the performance evaluation model. By calculating the ratio of IOPS to bandwidth of the storage cluster, the average block size for read and write IO is obtained, accurately characterizing the data volume of IO requests currently being processed by the storage cluster. Combined with the core hardware resource parameter of OSD number, the service performance threshold is comprehensively derived through a performance evaluation model. This ensures that the threshold setting is no longer divorced from the actual request load and hardware capacity of the cluster. It avoids cluster overload and decreased stability due to excessively high thresholds, and also prevents resource waste due to excessively low thresholds. Ultimately, it achieves accurate, dynamic, and reasonable setting of storage cluster service performance thresholds, providing a scientific and realistic judgment benchmark for subsequent precise rate limiting of target cloud disks and tenants.

[0035] The performance evaluation model is as follows: Pc=osd×(a+b×io_size) / 3; (Formula 1) In Formula 1, Pc is the service performance threshold, osd is the number of OSDs that can provide services, io_size is the average block size of read and write IO of the storage cluster, and a and b are the first coefficient and the second coefficient, respectively. The first coefficient and the second coefficient can be determined based on actual needs, and this disclosure does not limit them.

[0036] In one optional implementation, the first operating parameter obtained by the policy management server includes the percentage of normal PG states. Before determining the service performance threshold of the storage cluster based on the first operating parameter, if the policy management server determines that the percentage of normal PG states is less than the proportional threshold, then the service performance threshold of the storage cluster is determined based on the first operating parameter. The proportional threshold can be determined based on actual needs, and this embodiment does not limit it. By establishing a precondition for the percentage of PGs in normal condition, a performance threshold triggering mechanism prioritizing the underlying health of the storage cluster is constructed. Since the percentage of PGs in normal condition directly determines the authenticity and validity of the storage cluster's operating parameters such as IOPS and bandwidth, if the percentage of abnormal PGs is too high, it means that the storage cluster may have problems such as OSD node failure, data replica loss, or load imbalance. In this case, directly calculating the service performance of the storage cluster based on the first operating parameter will cause the policy to fail due to data distortion. The newly added judgment logic requires that the calculation process of the service performance threshold is only initiated when the percentage of PGs in normal condition is higher than the proportional threshold. This avoids the risk of service performance threshold setting deviation from the source and avoids the additional impact on business continuity caused by blindly limiting the rate when the storage cluster itself has failures. Ultimately, it improves the reliability of the cloud disk rate limiting solution.

[0037] In step S203, based on the service performance threshold, the first operating parameters, and the second operating parameters of the multiple cloud disks, at least one first target cloud disk to be rate-limited is determined among the multiple cloud disks, as well as the traffic control parameters associated with at least one target tenant.

[0038] In this embodiment of the disclosure, the target tenant is a tenant that uses at least one first target cloud disk.

[0039] In an optional implementation, if the first operating parameter includes the IOPS of the storage cluster during a historical period, and the second operating parameter includes the IOPS of each cloud disk at the current moment and the IOPS of each cloud disk during a historical period; then the process by which the policy control server determines at least one first target cloud disk to be rate-limited among the multiple cloud disks based on the service performance threshold, the first operating parameter, and the second operating parameter of the multiple cloud disks, and the traffic control parameter associated with at least one target tenant, includes: if the IOPS of the storage cluster during a historical period is greater than the service performance threshold, then for each cloud disk, determine the cloud disk... The difference between the current IOPS and the historical IOPS is used to obtain the first IOPS increment of the cloud disk. Next, the first IOPS increments of multiple cloud disks are sorted in descending order to obtain an IOPS increment sequence, and a preset number of first IOPS increments are selected starting from the first IOPS increment with the largest value in the ops increment sequence. Further, the cloud disks associated with the preset number of first IOPS increments are determined as the first target cloud disks, and the tenants using the first target cloud disks are determined as target tenants. Finally, traffic adjustment parameters are determined based on the IOPS of the storage cluster in the historical period and / or the service type associated with the target tenant.

[0040] On the one hand, when the IOPS of the storage cluster exceeds the service performance threshold within a historical period, it confirms that the storage cluster is indeed overloaded, avoiding excessive traffic control due to occasional data. On the other hand, by using the first IOPS increment obtained from the difference between the current and historical IOPS of a single cloud disk, the first target cloud disk is identified. Since the historical time is earlier than the historical period, the cloud disk and associated tenant causing the storage cluster's IOPS overload within the historical period can be accurately located, resolving the issue of cloud disk-specific rate limiting solutions inadvertently affecting other normal services. Furthermore, by combining the storage cluster's IOPS data within the historical period with the target tenant's business type, traffic adjustment parameters are determined, ensuring that the traffic control strategy not only matches the actual load pressure of the storage cluster but also adapts to the business characteristics of different tenants. This effectively alleviates the performance pressure on the storage cluster while also ensuring fairness in resource usage and business continuity in multi-tenant scenarios.

[0041] It should be noted that, in this embodiment, the service performance threshold and preset quantity can be determined based on actual needs, and this embodiment does not limit them; the traffic adjustment parameter is used to characterize the traffic restriction degree of the cloud disk, wherein, the larger the traffic adjustment parameter, the smaller the traffic restriction degree of the cloud disk. The traffic adjustment parameter includes a first traffic adjustment parameter and a second traffic adjustment parameter associated with the first traffic limit evaluation model. The first traffic limit evaluation model is: IOPS lim=c×size+d; (Formula 2) In Formula 2, IOPS lim is the maximum traffic limit, size is the capacity of the cloud disk, c is the first traffic adjustment parameter, and d is the second traffic adjustment parameter.

[0042] The process by which the policy control server determines traffic adjustment parameters based on the IOPS of the storage cluster in a historical period and / or the service type associated with the target tenant may include: querying a relationship information table based on the IOPS of the storage cluster in a historical period and / or the service type associated with the target tenant to obtain a first traffic adjustment parameter and a second traffic adjustment parameter corresponding to the IOPS of the storage cluster in a historical period and / or the service type associated with the target tenant.

[0043] It is understood that, in the embodiments of this disclosure, the preset information table stores different first traffic adjustment parameters corresponding to different IOPS and / or service types, as well as different second traffic adjustment parameters; the preset information table may be pre-built.

[0044] In an optional implementation, if the first operating parameter includes the IOPS of the storage cluster in each unit sampling period within the current monitoring period, and the second operating parameter of the multiple cloud disks includes the IOPS of each cloud disk in each unit sampling period within the current monitoring period; then the process by which the policy control server determines at least one first target cloud disk to be rate-limited among the multiple cloud disks based on the service performance threshold, the first operating parameter, and the second operating parameter of the multiple cloud disks, and the traffic control parameters of all second target cloud disks associated with at least one target tenant, includes: Based on the IOPS of each cloud disk in each unit sampling period within the current monitoring period, the average IOPS per unit sampling period for each cloud disk is obtained. Then, based on the average IOPS per unit sampling period for multiple cloud disks, the IOPS threshold for each cloud disk is determined. Next, based on the IOPS of each cloud disk in the latest unit sampling period and the IOPS in the earliest unit sampling period, a second IOPS increment for each cloud disk is determined. Simultaneously, based on the IOPS of the storage cluster in the latest unit sampling period and the IOPS in the earliest unit sampling period, the IOPS increment for the storage cluster is determined, and... Based on the IOPS of the storage cluster in each sampling period within the current monitoring cycle, the average IOPS of the storage cluster in each sampling period is obtained. Further, for each cloud disk, if the average IOPS of the cloud disk in each sampling period is greater than the IOPS threshold of the cloud disk, and the second IOPS increment of the cloud disk is greater than the IOPS increment of the storage cluster, then the cloud disk is identified as the first target cloud disk, and the tenant using the first target cloud disk is identified as the target tenant. Finally, the rate of change of the average IOPS of the storage cluster in each sampling period relative to the service performance threshold is determined as the traffic control factor for the target tenant.

[0045] On the one hand, by using the dual criteria of the cloud disk's average IOPS per sampling period exceeding the cloud disk's IOPS threshold, and the single cloud disk's IOPS increment within the current monitoring period being higher than the storage cluster's IOPS increment, the system can accurately identify the primary target cloud disk and its associated tenants that have high load and whose IOPS growth rate is faster than the overall growth rate of the storage cluster. This avoids misjudging cloud disks with normal growth or growth rates lower than the cluster as control targets. On the other hand, by directly using the rate of change of the storage cluster's average IOPS per sampling period relative to the service performance threshold as a traffic control factor, the system highly binds traffic control to the actual overload level of the cluster, improving the scientific nature and adaptability of tenant-level rate limiting strategies.

[0046] The average IOPS per sampling period for each cloud disk is: ;(Formula 3) In formula 3, Let be the average IOPS of the i-th cloud disk in a given sampling period out of n cloud disks. Let be the IOPS of the i-th cloud disk in the j-th unit sampling period, and h be the total number of unit sampling periods within the current monitoring period.

[0047] The IOPS threshold for cloud disks is: ;(Formula 4) In formula 4, This is the IOPS threshold for cloud disks.

[0048] The second IOPS increment for each cloud disk is: ;(Formula 5) In Formula 5, For the second IOPS increment of the i-th cloud disk, Let i be the IOPS of the i-th cloud disk in the latest sampling period. Let be the IOPS of the i-th cloud disk in the earliest unit sampling period.

[0049] And, the flow control factor is: ;(Formula 6) In Formula 6, For flow control factors, Let be the IOPS of the storage cluster in the j-th unit sampling period. This represents the average IOPS per sampling period for the storage cluster.

[0050] It should be noted that, in this embodiment, the mathematical expression for the IOPS increment of the storage cluster is similar to the expression for the second IOPS increment of the cloud disk, and will not be elaborated upon here; the traffic control factor is a parameter value associated with the second traffic limit evaluation model. The traffic control factor is used to characterize the strength of the traffic restriction. The smaller the traffic control factor, the weaker the traffic restriction. The second traffic limit evaluation model is as follows: ;(Formula 7) In an optional implementation, if the traffic control parameter determined by the policy management server is a traffic control factor, the policy management server also needs to: obtain the IOPS of each second target cloud disk used by each target tenant in each unit sampling period within the current monitoring period, and obtain the average IOPS of each second target cloud disk in unit sampling period; and send the average IOPS of each second target cloud disk used by each target tenant to the storage engine server, which is used to synchronize the average IOPS of each second target cloud disk used by each target tenant to the business server, so that the business server can determine the traffic limit of the second target cloud disk used by each target tenant based on the traffic control factor associated with each target tenant and the second traffic limit evaluation model.

[0051] Understandably, if the second target cloud disk is also the first target cloud disk, then there is no need to repeat the process of determining the average IOPS per unit sampling period of the cloud disk. Instead, the average IOPS per unit sampling period of the first target cloud disk obtained during the process of determining the traffic control factor can be directly used as the average IOPS per unit sampling period of the second target cloud disk.

[0052] In step S204, the traffic control parameters associated with at least one target tenant are sent to the storage engine server.

[0053] In this embodiment of the disclosure, the storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

[0054] Figure 3 A flowchart illustrating an exemplary embodiment of this disclosure of a cloud disk rate limiting method is shown. This cloud disk rate limiting method is applied to a business server, such as... Figure 3 As shown, the method in this embodiment of the disclosure may include: Step 301, in response to receiving the traffic control parameters associated with each target tenant sent by the storage engine server; Among them, the target tenant is the tenant that needs to be subject to traffic control, which is determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server. Step S302: Based on the traffic control parameters associated with each target tenant and the traffic limit assessment model, determine the traffic limit value of the second target cloud disk used by each target tenant; Step S303: Send the traffic limit value of the second target cloud disk to the second target cloud disk in the storage cluster; In summary, the cloud disk rate limiting method provided in this disclosure allows the business server to calculate a personalized traffic limit value for the second target cloud disk used by the target tenant through a traffic limit evaluation model after receiving the traffic control parameters synchronized by the storage engine server. This achieves traffic control that can adapt to the business load characteristics of different tenants and the differences in cloud disk specifications, thereby improving the accuracy and reliability of tenant-level traffic rate limiting.

[0055] It should be noted that, in this embodiment of the disclosure, the process by which the business server determines the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model is related to the type of traffic control parameters received by the business server.

[0056] If the traffic control parameters received by the service server include a first traffic adjustment parameter and a second traffic adjustment parameter, the service server uses the first traffic limit assessment model to determine the traffic limit value; or, if the traffic control parameters received by the service server are traffic control factors, the service server uses the second traffic limit assessment model to determine the traffic limit value.

[0057] An exemplary embodiment of this disclosure provides a cloud disk speed limiting device, which can be a policy management server. Figure 4 A schematic block diagram of the functional modules of a cloud disk speed limiting device according to an exemplary embodiment of the present disclosure is shown. Figure 4 As shown, the cloud hard drive speed limiting device 400 includes: The monitoring module 401 is configured to obtain the performance parameters of the storage cluster from the storage engine server, wherein the performance parameters include the first operating parameters of the storage cluster and the second operating parameters of the multiple cloud disks deployed in the storage cluster. The monitoring module 401 is also configured to determine the service performance threshold of the storage cluster based on the first operating parameters; The monitoring module 401 is also configured to determine, based on the service performance threshold, the first operating parameters and the second operating parameters of the plurality of cloud disks, at least one first target cloud disk to be rate-limited among the plurality of cloud disks, and traffic control parameters associated with at least one target tenant, wherein the target tenant is a tenant using the first target cloud disk. The control module 402 is configured to send traffic control parameters associated with the at least one target tenant to the storage engine server. The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

[0058] Optionally, the first operating parameters include the number of object storage daemons (OSDs) that the storage cluster can provide services during the current monitoring period, the number of input / output operations per second (IOPS), and the bandwidth. The monitoring module 401 is configured as follows: The ratio between the IOPS of the storage cluster and the bandwidth of the storage cluster is determined to obtain the average block size of read and write IO of the storage cluster, wherein the average block size of IO is the average amount of data of all IO requests processed by the storage cluster per unit time. Based on the average IO block size, the number of OSDs, and the performance evaluation model, the service performance threshold of the storage cluster is determined.

[0059] Optionally, the first operating parameter includes the percentage of normal states in the grading group (pg). The monitoring module 401 is configured as follows: If the percentage of the number of pg in normal state is less than the percentage threshold, then the service performance threshold of the storage cluster is determined based on the first operating parameters.

[0060] Optionally, the first operating parameter includes the IOPS of the storage cluster within a historical time period, and the second operating parameter includes the second operating parameter of the plurality of cloud disks, including the IOPS of each cloud disk at the current moment, and the IOPS of each cloud disk at a historical time period, wherein the historical time period is a preset duration prior to the current moment, and the historical moment is earlier than the start time of the historical time period. The monitoring module 401 is configured as follows: If the IOPS of the storage cluster in a historical period is greater than the service performance threshold, then for each cloud disk, the difference between the IOPS of the cloud disk at the current moment and the IOPS at a historical moment is determined to obtain the first IOPS increment of the cloud disk. The first IOPS increments of the multiple cloud disks are sorted in descending order to obtain an IOPS increment sequence, and a preset number of first IOPS increments are selected starting from the first IOPS increment with the largest value in the IOPS increment sequence. The cloud disks associated with the preset number of first IOPS increments are identified as the first target cloud disks, and the tenants using the first target cloud disks are identified as the target tenants; Traffic adjustment parameters are determined based on the IOPS of the storage cluster during historical periods and / or the service type associated with the target tenant.

[0061] Optionally, the first operating parameter includes the IOPS of the storage cluster in each unit sampling period within the current monitoring period, and the second operating parameter of the plurality of cloud disks includes the IOPS of each cloud disk in each unit sampling period within the current monitoring period. The monitoring module 401 is configured as follows: Based on the IOPS of each cloud disk in each unit sampling period within the current monitoring period, the average IOPS of each cloud disk in each unit sampling period is obtained, and based on the average IOPS of the multiple cloud disks in each unit sampling period, the IOPS threshold of the cloud disk is determined. The second IOPS increment for each cloud disk is determined based on the IOPS of each cloud disk in the latest unit sampling period and the IOPS of each cloud disk in the earliest unit sampling period. Based on the IOPS of the storage cluster in the latest unit sampling period and the IOPS in the earliest unit sampling period, the IOPS increment of the storage cluster is determined, and the average IOPS of the storage cluster in each unit sampling period within the current monitoring period is obtained according to the IOPS of the storage cluster in each unit sampling period. For each cloud disk, if the average IOPS of the cloud disk in a unit sampling period is greater than the IOPS threshold of the cloud disk, and the second IOPS increment of the cloud disk is greater than the IOPS increment of the storage cluster, then the cloud disk is determined as the first target cloud disk, and the tenant using the first target cloud disk is determined as the target tenant. The rate of change of the average IOPS per unit sampling period of the storage cluster relative to the service performance threshold is determined as the traffic control factor for the target tenant.

[0062] An exemplary embodiment of this disclosure provides a cloud disk speed limiting device, which can be a business server. Figure 5 A schematic block diagram of the functional modules of a cloud disk speed limiting device according to an exemplary embodiment of the present disclosure is shown. Figure 5 As shown, the cloud hard drive speed limiting device 500 includes: The receiving module 501 is configured to respond to receiving traffic control parameters associated with each target tenant sent by the storage engine server, wherein the target tenant is the tenant that needs to be traffic controlled, determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server. The determination module 502 is configured to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model; The sending module 503 is configured to send the traffic limit value of the second target cloud disk to the second target cloud disk in the storage cluster.

[0063] Exemplary embodiments of this disclosure also provide an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor. The memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to cause the electronic device to perform a method according to an embodiment of this disclosure.

[0064] Exemplary embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing a computer program, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to embodiments of this disclosure.

[0065] like Figure 6As shown, an exemplary embodiment of this disclosure also provides a computer program product 600, including a computer program 601, wherein the computer program, when executed by a computer's processor, is used to cause the computer to perform a method according to an embodiment of this disclosure.

[0066] refer to Figure 7 The present invention describes a structural block diagram of an electronic device 700 that can serve as a policy control server or business server for this disclosure, which is an example of a hardware device that can be applied to various aspects of this disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the disclosure described and / or claimed herein.

[0067] like Figure 7 As shown, the electronic device 700 includes a computing unit 701, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 702 or a computer program loaded from a storage unit 708 into a random access memory (RAM) 703. The RAM 703 may also store various programs and data required for the operation of the electronic device 700. The computing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0068] Multiple components in electronic device 700 are connected to I / O interface 705, including: input unit 706, output unit 707, storage unit 708, and communication unit 709. Input unit 706 can be any type of device capable of inputting information to electronic device 700. Input unit 706 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device. Output unit 707 can be any type of device capable of presenting information and may include, but is not limited to, a display, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 708 may include, but is not limited to, disk and optical disk. Communication unit 709 allows electronic device 700 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0069] The computing unit 701 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 701 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 701 performs the various methods and processes described above. For example, in some embodiments, the methods of the exemplary embodiments of this disclosure can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 708. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 700 via ROM 702 and / or communication unit 709. In some embodiments, the computing unit 701 can be configured to perform the methods of the exemplary embodiments of this disclosure by any other suitable means (e.g., by means of firmware).

[0070] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0071] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0072] As used in this disclosure, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0073] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0074] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0075] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer programs or instructions. When the computer program or instructions are loaded and executed on a computer, the processes or functions described in the embodiments of this disclosure are performed entirely or partially. The computer can be a general-purpose computer, a special-purpose computer, a computer network, a terminal, a user equipment, or other programmable device. The computer program or instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, the computer program or instructions can be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium, such as a floppy disk, hard disk, or magnetic tape; it can also be an optical medium, such as a digital video disc (DVD); or it can be a semiconductor medium, such as a solid-state drive (SSD).

[0076] Although this disclosure has been described in conjunction with specific features and embodiments, it will be apparent that various modifications and combinations can be made therein without departing from the spirit and scope of this disclosure. Accordingly, this specification and drawings are merely exemplary illustrations of the disclosure as defined by the appended claims and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of this disclosure. It is obvious that those skilled in the art can make various alterations and modifications to this disclosure without departing from its spirit and scope. Thus, this disclosure is also intended to include any such modifications and modifications that fall within the scope of the claims of this disclosure and their equivalents.

Claims

1. A cloud hard disk speed limiting method, characterized in that, The method is applied to the policy management server and includes: The performance parameters of the storage cluster are obtained from the storage engine server, wherein the performance parameters include the first operating parameters of the storage cluster and the second operating parameters of the multiple cloud disks deployed in the storage cluster. The service performance threshold of the storage cluster is determined based on the first operating parameters. Based on the service performance threshold, the first operating parameters, and the second operating parameters of the plurality of cloud disks, at least one first target cloud disk to be rate-limited is determined among the plurality of cloud disks, and traffic control parameters are associated with at least one target tenant, wherein the target tenant is a tenant using the first target cloud disk. The traffic control parameters associated with the at least one target tenant are sent to the storage engine server. The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

2. The cloud hard disk speed limiting method of claim 1, wherein, The first operating parameters include the number of object storage daemons (OSDs) that the storage cluster can provide services during the current monitoring period, the number of input / output operations per second (IOPS), and the bandwidth. Determining the service performance threshold of the storage cluster based on the first operating parameters includes: The ratio between the IOPS of the storage cluster and the bandwidth of the storage cluster is determined to obtain the average block size of read and write IO of the storage cluster, wherein the average block size of IO is the average amount of data of all IO requests processed by the storage cluster per unit time. Based on the average IO block size, the number of OSDs, and the performance evaluation model, determine the service performance threshold of the storage cluster; and / or, The first operating parameter includes the percentage of normal states in the categorization group PG. Before determining the service performance threshold of the storage cluster based on the first operating parameters, the method further includes: If the percentage of the number of pg in normal state is less than the percentage threshold, then the service performance threshold of the storage cluster is determined based on the first operating parameters.

3. The cloud hard disk speed limiting method of claim 1, wherein, The first operating parameter includes the IOPS of the storage cluster within a historical time period, and the second operating parameter includes the IOPS of each cloud disk at the current moment, and the IOPS of each cloud disk at a historical time period, wherein the historical time period is a preset duration prior to the current moment, and the historical moment is earlier than the start time of the historical time period. The step of determining at least one first target cloud disk to be rate-limited from among the multiple cloud disks based on the service performance threshold, the first operating parameters, and the second operating parameters of the multiple cloud disks, and the traffic control parameters associated with at least one target tenant, includes: If the IOPS of the storage cluster in a historical period is greater than the service performance threshold, then for each cloud disk, the difference between the IOPS of the cloud disk at the current moment and the IOPS at a historical moment is determined to obtain the first IOPS increment of the cloud disk. The first IOPS increments of the multiple cloud disks are sorted in descending order to obtain an IOPS increment sequence, and a preset number of first IOPS increments are selected starting from the first IOPS increment with the largest value in the IOPS increment sequence. The cloud disks associated with the preset number of first IOPS increments are identified as the first target cloud disks, and the tenants using the first target cloud disks are identified as the target tenants; Traffic adjustment parameters are determined based on the IOPS of the storage cluster during historical periods and / or the service type associated with the target tenant.

4. The cloud hard disk speed limiting method of claim 1, wherein, The first operating parameter includes the IOPS of the storage cluster in each sampling period within the current monitoring period, and the second operating parameter of the plurality of cloud disks includes the IOPS of each cloud disk in each sampling period within the current monitoring period. The step of determining at least one first target cloud disk to be rate-limited from among the multiple cloud disks based on the service performance threshold, the first operating parameters, and the second operating parameters of the multiple cloud disks, and the traffic control parameters associated with at least one target tenant, includes: Based on the IOPS of each cloud disk in each unit sampling period within the current monitoring period, the average IOPS of each cloud disk in each unit sampling period is obtained, and based on the average IOPS of the multiple cloud disks in each unit sampling period, the IOPS threshold of the cloud disk is determined. The second IOPS increment for each cloud disk is determined based on the IOPS of each cloud disk in the latest unit sampling period and the IOPS of each cloud disk in the earliest unit sampling period. Based on the IOPS of the storage cluster in the latest unit sampling period and the IOPS in the earliest unit sampling period, the IOPS increment of the storage cluster is determined, and the average IOPS of the storage cluster in each unit sampling period within the current monitoring period is obtained according to the IOPS of the storage cluster in each unit sampling period. For each cloud disk, if the average IOPS of the cloud disk in a unit sampling period is greater than the IOPS threshold of the cloud disk, and the second IOPS increment of the cloud disk is greater than the IOPS increment of the storage cluster, then the cloud disk is determined as the first target cloud disk, and the tenant using the first target cloud disk is determined as the target tenant. The rate of change of the average IOPS per unit sampling period of the storage cluster relative to the service performance threshold is determined as the traffic control factor for the target tenant.

5. A cloud hard disk speed limiting method, characterized in that, The method is applied to the business server and includes: In response to receiving traffic control parameters associated with each target tenant sent by the storage engine server, wherein the target tenant is the tenant that needs to be traffic controlled, determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server; Based on the traffic control parameters associated with each target tenant and the traffic limit assessment model, the traffic limit value of the second target cloud disk used by each target tenant is determined; Send the traffic limit value of the second target cloud disk to the second target cloud disk in the storage cluster.

6. A cloud hard disk speed limiting device, characterized in that, The device is a policy management server, comprising: The monitoring module is configured to obtain performance parameters of the storage cluster from the storage engine server, wherein the performance parameters include first operating parameters of the storage cluster and second operating parameters of multiple cloud disks deployed in the storage cluster. The monitoring module is also configured to determine the service performance threshold of the storage cluster based on the first operating parameters; The monitoring module is also configured to determine at least one first target cloud disk to be rate-limited among the plurality of cloud disks based on the service performance threshold, the first operating parameters and the second operating parameters of the plurality of cloud disks, as well as traffic control parameters associated with at least one target tenant, wherein the target tenant is a tenant using the first target cloud disk. The control module is configured to send traffic control parameters associated with the at least one target tenant to the storage engine server. The storage engine server is used to synchronize the traffic control parameters associated with each target tenant to the business server. The business server is used to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model, and send the traffic limit value to the second target cloud disk in the storage cluster.

7. A cloud hard drive speed limiting device, characterized in that, The device is a service server, including: The receiving module is configured to respond to receiving traffic control parameters associated with each target tenant sent by the storage engine server. The target tenant is the tenant that needs to be traffic controlled, determined by the policy management server based on the operating parameters of the storage cluster collected by the storage engine server. The determination module is configured to determine the traffic limit value of the second target cloud disk used by each target tenant based on the traffic control parameters associated with each target tenant and the traffic limit evaluation model; The sending module is configured to send the traffic limit value of the second target cloud disk to the second target cloud disk in the storage cluster.

8. An electronic device, the electronic device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method of any one of claims 1 to 4 or claim 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 4 or any one of claims 5.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the method described in any one of claims 1 to 4 or any one of claims 5.

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