CN111258705B - Method and device for detecting cloud hard disk input/output IO (input/output) ortho-position interference - Google Patents
Method and device for detecting cloud hard disk input/output IO (input/output) ortho-position interference Download PDFInfo
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Abstract
The application provides a method and a device for detecting cloud hard disk input/output IO (input/output) ortho-position interference. According to the technical scheme, the parameters of the IO request are obtained through the virtual machine, the parameters comprise the block size of the IO request, the time stamp for sending the IO request and the time stamp for finishing the IO request, and whether IO ortho-position interference occurs in the cloud hard disk of the virtual machine to which the parameters belong is judged according to the parameters. The technical scheme can realize IO (input/output) ortho-position interference detection of the cloud hard disk of the virtual machine through the virtual machine of the service layer, and is independent of a cloud infrastructure layer because the technical scheme is realized on the virtual machine of the service layer.
Description
Technical Field
The application relates to the field of computers, in particular to a method and a device for detecting cloud hard disk input/output (IO) ortho interference.
Background
Cloud computing is a new type of information technology (information technology, IT) infrastructure usage, delivery and operation model. The narrow cloud computing, i.e. infrastructure as a service (infrastructure as a service, IAAS), is based on a virtualization technology, and pools and manages IT infrastructure of a considerable scale, so that users can conveniently access and use the cloud computing through a network, and the cloud computing system has the characteristics and advantages of quick deployment, self-service on-demand use, charging on actual use and the like. Cloud computing can be classified into public clouds, private clouds, and hybrid clouds according to provisioning patterns.
In public cloud environment, the physical storage system is managed in a centralized way and is virtualized into a cloud hard disk, and the cloud hard disk is mounted on a virtual machine for use. The public cloud physical storage system is shared by a plurality of virtual machines, so that the cloud hard disk mounted on the virtual machine can be reduced in performance due to interference of reading and writing of the large-flow cloud hard disk of other virtual machines, and the loaded application and service of the virtual machine are seriously affected.
Therefore, detecting whether the cloud hard disk mounted on the virtual machine is subject to the ortho-position interference is necessary for the management of the virtual machine.
Disclosure of Invention
The application provides a method and a device for detecting IO (input/output) ortho-position interference of a cloud hard disk, which can realize the IO ortho-position interference detection of a virtual machine cloud hard disk in a service layer.
In a first aspect, the present application provides a method for detecting cloud hard disk IO ortho interference, the method comprising: the method comprises the steps that a first virtual machine obtains parameters of an IO request, wherein the parameters comprise the block size of the IO request, a time stamp for sending the IO request and a time stamp for finishing the IO request; and the first virtual machine determines whether IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the parameter.
In the technical scheme, the first virtual machine can directly or indirectly embody the read-write performance of the cloud hard disk by acquiring the block size of the IO request, the time stamp of sending the IO request and the time stamp of completing the IO request, so as to judge whether the cloud hard disk of the second virtual machine generates IO ortho-position interference. In this way, the technical scheme can realize IO (input/output) ortho-position interference detection of the cloud hard disk of the virtual machine through the virtual machine of the service layer.
In addition, the technical scheme is realized on the virtual machine of the service layer, so the technical scheme does not depend on the cloud infrastructure layer.
In some possible implementations, the determining, by the first virtual machine according to the parameter, whether IO ortho-position interference occurs in a cloud hard disk of a second virtual machine to which the parameter belongs includes: the first virtual machine determines a time difference between the time stamp of the sent IO request and the time stamp of the completed IO request; the first virtual machine determines the ratio of the time difference value to the block size of the IO request as unit sector processing time delay; and the first virtual machine determines whether IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value.
The block size of the IO request can influence the actual processing time of the IO request, and the technical scheme determines the unit sector processing time delay of the current IO request through the block size of the IO request, the time stamp for sending the IO request and the time stamp for finishing the IO request, and judges whether the cloud hard disk of the second virtual machine generates IO adjacent bit interference or not according to the unit sector processing time delay of the current IO request, so that the influence of the block size of each IO request on the IO request processing time delay can be eliminated.
In some possible implementations, the unit sector processing delay reference value includes at least one of: the historical minimum unit sector processing time delay of the second virtual machine, the unit sector processing time delay predicted value and the average value of the historical minimum unit sector processing time delay of each virtual machine in a virtual machine set, wherein the unit sector processing time delay predicted value is obtained by the mapping relation of the first virtual machine according to the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request, the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request and the unit sector processing time delay predicted value, and the virtual machine set is composed of the second virtual machine and/or at least one third virtual machine, and the third virtual machine and the second virtual machine have the same application.
In the above technical solution, the accuracy of the detection result can be improved by comparing the unit sector processing delay with the historical minimum unit sector processing delay of the second virtual machine or the unit sector processing delay predicted value or the average value of the historical minimum unit sector processing delay of at least one third virtual machine or any combination thereof.
In some possible implementations, the determining, by the first virtual machine, whether IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value includes: and when the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than a first threshold value, the first virtual machine determines that IO ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, the deviation value of the unit sector processing time delay of the cloud hard disk of the second virtual machine relative to the unit sector processing time delay reference value is compared with the preset threshold, so that the detection precision can be flexibly adjusted by configuring the preset threshold.
In some possible implementations, when the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than a first threshold, the determining that the IO adjacent bit interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs includes: and in a plurality of continuous monitoring periods, the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than the first threshold value, and the IO ortho-position interference of the cloud hard disk of the second virtual machine to which the parameter belongs is determined.
In the technical scheme, when the processing time delay of the unit sector of the cloud hard disk of the second virtual machine continuously deviates from the processing time delay reference value of the unit sector for a plurality of times, the first virtual machine judges that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine, so that the influence of accidental factors on a detection result can be avoided, and the accuracy of the detection result is improved.
In some possible implementations, the determining, by the first virtual machine according to the parameter, whether IO ortho-position interference occurs in a cloud hard disk of a second virtual machine to which the parameter belongs includes: the first virtual machine determines a time difference between the time stamp of the sent IO request and the time stamp of the completed IO request; the first virtual machine determines a time difference estimated value according to the block size of the IO request and the mapping relation between the block size of the IO request and the time difference; and the first virtual machine determines whether IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the time difference relative to the time difference estimated value.
In the technical scheme, only the time difference and the time difference predicted value between the time stamp for sending the IO request and the time stamp for finishing the IO request are needed to be calculated, so that the calculation complexity can be reduced.
In some possible implementations, the determining, by the first virtual machine, whether IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the time difference relative to the time difference estimated value includes: and when the deviation value of the time difference relative to the time difference predicted value is larger than a second threshold value, the first virtual machine determines that IO (input/output) ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, the deviation value of the time difference of the cloud hard disk of the second virtual machine relative to the time difference predicted value is compared with the preset threshold, so that the detection precision can be flexibly adjusted by configuring the preset threshold.
In some possible implementations, when the deviation value of the time difference relative to the time difference estimated value of the first virtual machine is greater than a second threshold, determining that the IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs, including: and in a plurality of continuous monitoring periods, when the deviation value of the time difference relative to the time difference estimated value is larger than the second threshold value, determining that IO ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, when the time difference of the cloud hard disk of the second virtual machine continuously deviates from the time difference predicted value for a plurality of times, the first virtual machine judges that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine, so that the influence of accidental factors on a detection result can be avoided, and the accuracy of the detection result is improved.
In some possible implementations, when the unit sector processing delay is less than the historical unit sector processing delay, the method further includes: the first virtual machine updates a historical minimum unit sector processing delay in a database.
In a second aspect, the present application provides a device for detecting IO ortho-position interference of a cloud hard disk, where the device includes: the acquisition module is used for acquiring parameters of the IO request, wherein the parameters comprise the block size of the IO request, the time stamp for sending the IO request and the time stamp for completing the IO request; and the processing module is used for determining whether IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the parameter.
In the technical scheme, the first virtual machine can directly or indirectly embody the read-write performance of the cloud hard disk by acquiring the block size of the IO request, the time stamp of sending the IO request and the time stamp of completing the IO request, so as to judge whether the cloud hard disk of the second virtual machine generates IO ortho-position interference. In this way, the technical scheme can realize IO (input/output) ortho-position interference detection of the cloud hard disk of the virtual machine through the virtual machine of the service layer.
In addition, the technical scheme is realized on the virtual machine of the service layer, so the technical scheme does not depend on the cloud infrastructure layer.
In some possible implementations, the processing module is specifically configured to: determining a time difference between the time stamp of the send IO request and the time stamp of the complete IO request; determining the ratio of the time difference value to the block size of the IO request as unit sector processing time delay; and determining whether IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value.
The block size of the IO request can influence the actual processing time of the IO request, and the technical scheme determines the unit sector processing time delay of the current IO request through the block size of the IO request, the time stamp for sending the IO request and the time stamp for finishing the IO request, and judges whether the cloud hard disk of the second virtual machine generates IO adjacent bit interference or not according to the unit sector processing time delay of the current IO request, so that the influence of the block size of each IO request on the IO request processing time delay can be eliminated.
In some possible implementations, the unit sector processing delay reference value includes at least one of: the historical minimum unit sector processing time delay of the second virtual machine, the unit sector processing time delay predicted value and the average value of the historical minimum unit sector processing time delay of each virtual machine in a virtual machine set, wherein the unit sector processing time delay predicted value is obtained by the mapping relation of the first virtual machine according to the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request, the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request and the unit sector processing time delay predicted value, and the virtual machine set is composed of the second virtual machine and/or at least one third virtual machine, and the third virtual machine and the second virtual machine have the same application.
In the above technical solution, the accuracy of the detection result can be improved by comparing the unit sector processing delay with the historical minimum unit sector processing delay of the second virtual machine or the unit sector processing delay predicted value or the average value of the historical minimum unit sector processing delay of at least one third virtual machine or any combination thereof.
In some possible implementations, the processing module is specifically configured to: and when the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than a first threshold value, determining that IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, the deviation value of the unit sector processing time delay of the cloud hard disk of the second virtual machine relative to the unit sector processing time delay reference value is compared with the preset threshold, so that the detection precision can be flexibly adjusted by configuring the preset threshold.
In some possible implementations, the processing module is specifically configured to: and in a plurality of continuous monitoring periods, the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than the first threshold value, and the IO ortho-position interference of the cloud hard disk of the second virtual machine to which the parameter belongs is determined.
In the technical scheme, when the processing time delay of the unit sector of the cloud hard disk of the second virtual machine continuously deviates from the processing time delay reference value of the unit sector for a plurality of times, the first virtual machine judges that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine, so that the influence of accidental factors on a detection result can be avoided, and the accuracy of the detection result is improved.
In some possible implementations, the processing module is specifically configured to: determining a time difference between the time stamp of the send IO request and the time stamp of the complete IO request; determining a time difference predicted value according to the block size of the IO request and the mapping relation between the block size of the IO request and the time difference; and determining whether IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the time difference relative to the time difference predicted value.
In the technical scheme, only the time difference and the time difference predicted value between the time stamp for sending the IO request and the time stamp for finishing the IO request are needed to be calculated, so that the calculation complexity can be reduced.
In some possible implementations, the processing module is specifically configured to: and when the deviation value of the time difference relative to the time difference predicted value is larger than two thresholds, determining that IO (input/output) ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, the deviation value of the time difference of the cloud hard disk of the second virtual machine relative to the time difference predicted value is compared with the preset threshold, so that the detection precision can be flexibly adjusted by configuring the preset threshold.
In some possible implementations, the processing module is specifically configured to: and in a plurality of continuous monitoring periods, when the deviation value of the time difference relative to the time difference predicted value is larger than the second threshold value, determining that IO ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
In the technical scheme, when the time difference of the cloud hard disk of the second virtual machine continuously deviates from the time difference predicted value for a plurality of times, the first virtual machine judges that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine, so that the influence of accidental factors on a detection result can be avoided, and the accuracy of the detection result is improved.
In some possible implementations, the apparatus further includes: and the storage module is used for updating the historical minimum unit sector processing time delay in the database when the unit sector processing time delay is smaller than the historical unit sector processing time delay.
In a third aspect, the present application provides a device for detecting IO ortho-position interference of a cloud hard disk, where a virtual machine is deployed on the device, where the virtual machine may implement functions corresponding to each step in the method related to the first aspect, where the functions may be implemented by hardware, or may be implemented by executing corresponding software by hardware. The hardware or software includes one or more units or modules corresponding to the functions described above.
In some possible implementations, the apparatus includes a processor configured to support the apparatus to perform the corresponding function in the method according to the first aspect. The apparatus may also include a memory for coupling with the processor, which holds the program instructions and data necessary for the apparatus. Optionally, the apparatus further comprises a communication interface for supporting communication between the apparatus and other network elements.
In a fourth aspect, the present application provides a computer readable storage medium comprising instructions which, when executed by a processing module or processor, cause an apparatus for detecting cloud hard input output, IO, ortho-interference to perform the method of the first aspect or any implementation manner of the first aspect.
In a fifth aspect, the present application provides a chip, in which instructions are stored, where the instructions, when executed on a device for detecting IO ortho interference of a cloud hard disk input/output, cause the chip to perform the method of the first aspect or any implementation manner of the first aspect.
In a sixth aspect, the present application provides a computer program product, which when run on an apparatus for detecting cloud hard disk input output, IO, orthotopic interference, causes the apparatus for detecting cloud hard disk input output, IO, orthotopic interference to perform the method of the first aspect or any implementation manner of the first aspect.
Drawings
Fig. 1 is a schematic diagram of IO orthotopic interference of a cloud hard disk.
Fig. 2 is a schematic flow chart of a method for detecting cloud hard disk IO ortho interference according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of an apparatus for detecting cloud hard disk IO ortho-position interference according to an embodiment of the present application.
Fig. 4 is a schematic block diagram of an apparatus for detecting Yun Yingpan IO proximity interference according to another embodiment of the present application.
Fig. 5 is a schematic structural diagram of a detection system according to an embodiment of the present application.
Detailed Description
In order to facilitate understanding of the technical solution of the present application, a brief description will be first made of the concept related to the present application.
Virtualization technology: a plurality of independent operating systems are operated on one physical hardware machine at the same time, the operating system resources operated independently are from the same bottom layer platform resource, and a bottom layer virtual machine supervisor is responsible for distributing the system resources, dispatching the virtual machines, and communicating the virtual machines with each other and the outside. The virtualization technology redefines and divides the IT resources by using a software method, so that the dynamic allocation, flexible scheduling, cross-domain sharing and improvement of the utilization rate of the information technology (information technology, IT) resources of the IT resources can be realized.
Cloud computing: is a novel IT infrastructure use, delivery and operation mode. The narrow cloud computing, i.e. infrastructure as a service (infrastructure as a service, IAAS), is based on virtualization technology, and pools and manages IT infrastructure of considerable scale, so that users can conveniently access and use through a network, and the cloud computing system has the characteristics and advantages of quick deployment, self-service on-demand use, charging on actual use and the like. Cloud computing can be classified into public cloud, private cloud, and hybrid cloud according to the provisioning model.
Cloud hard disk: in a cloud computing environment, a physical storage system is managed in a centralized manner and virtualized into a cloud hard disk, and the cloud hard disk is mounted on a virtual machine for use. In terms of evaluation of read-write performance, the cloud hard disk is measured by IO throughput (namely bytes per second) and input/output per second (input output per second, IOPS) like a common physical hard disk.
Virtual Machine (VM): refers to a virtual device that is emulated on a physical device by virtual machine software. For applications running in virtual machines, these virtual machines operate as real physical devices, on which a guest Operating System (OS) and applications can be installed, and which can also access network resources.
Input Output (IO) proximity interference of cloud hard disk: when the virtual machine accesses the data on the cloud hard disk, the virtual machine is actually required to finally access the remote shared physical storage hard disk through the network. On this path, there may be resource conflicts as shown in fig. 1, which eventually results in a decrease in the read-write performance of the cloud hard disk.
1. Network card of physical machine: because multiple virtual machines can be deployed on one physical machine at the same time, each virtual machine can be provided with multiple cloud hard disks, the access of the cloud hard disks is required to pass through a network card of the physical machine, and when the access flow of the cloud hard disks exceeds the throughput performance of the network card of the physical machine, the access performance of the cloud hard disks is affected.
2. Data center network: the data center network device needs to support read-write access of all virtual machines in the data center to the cloud hard disk, and when traffic bursts (for example, virtual machine migration is performed in a large area), congestion of the data center network may be caused, and finally, the access performance of the cloud hard disk is affected.
3. Physical hard disk: a physical hard disk may be virtualized into multiple cloud hard disks and mounted to multiple virtual machines, so that when the virtual machines perform read-write access to the cloud hard disks on the same physical hard disk in a centralized manner, access performance may also be reduced.
For the virtual machine, the performance of the cloud hard disk mounted by the virtual machine is affected by the above three possible performance effects caused by the read-write of the large-flow cloud hard disk of other virtual machines, and the performance is generally called as IO (input/output) ortho-position interference of the cloud hard disk.
The access performance of the cloud hard disk is reduced due to IO (input/output) ortho-position interference, and the application and service carried by the cloud hard disk can be seriously affected. Therefore, detecting whether the cloud hard disk mounted on the virtual machine is subject to the ortho-position interference is necessary for the management of the virtual machine.
The embodiment of the application provides a method for detecting IO (input/output) ortho-position interference of a cloud hard disk, which can realize the IO ortho-position interference detection of the cloud hard disk of a virtual machine in a service layer when a storage intensive application is operated on the virtual machine, so that countermeasures and evading measures are taken to avoid the influence of the application and the service.
Fig. 2 is a schematic flow chart of a method for detecting cloud hard disk IO ortho interference according to an embodiment of the present application. The method in fig. 2 may be applied in public cloud, private cloud or hybrid cloud environments and executed by a business layer virtual machine. As shown in fig. 2, the method for detecting cloud hard IO proximity interference according to an embodiment of the present application may include at least part of the following contents.
In 210, the first virtual machine obtains parameters of the IO request, including a block size of the IO request, a timestamp of sending the IO request, and a timestamp of completing the IO request.
In 220, the first virtual machine determines, according to the parameter, whether IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs.
The first virtual machine may be a control virtual machine which is deployed in a service layer and is specially used for detecting the cloud hard disk IO ortho-position interference, or may be a service virtual machine with a function of detecting the cloud hard disk IO ortho-position interference, which is not limited in the embodiment of the present application.
The second virtual machine can be any virtual machine which is provided with a cloud hard disk in a mounted mode and can collect and report IO request parameters, and the specific type of the second virtual machine is not limited by the embodiment of the application.
It should be appreciated that in some cases, the first virtual machine and the second virtual machine may be the same virtual machine.
In the technical scheme, the first virtual machine can directly or indirectly embody the read-write performance of the cloud hard disk by acquiring the block size of the IO request, the time stamp of sending the IO request and the time stamp of completing the IO request, so as to judge whether the cloud hard disk of the second virtual machine generates IO ortho-position interference. In this way, the technical scheme can realize IO (input/output) ortho-position interference detection of the cloud hard disk of the virtual machine through the virtual machine of the service layer.
In addition, the technical scheme is realized on the virtual machine of the service layer, so the technical scheme does not depend on the cloud infrastructure layer.
Alternatively, the above technical solution may also be performed by a physical device.
Optionally, the IO parameters acquired by the first virtual machine may also be other parameters related to the performance of the cloud hard disk.
In some embodiments, the second virtual machine collects parameters of the IO request and reports the parameters to the first virtual machine, where the first virtual machine and the second virtual machine are different virtual machines. Optionally, the second virtual machine collects parameters for all the IO requests, and reports the collected parameters. Optionally, the second virtual machine samples and collects parameters of the IO request, and reports the collected parameters, wherein a sampling interval can be configured, that is, whether IO ortho-position interference occurs in the second virtual machine is periodically monitored.
In other embodiments, the first virtual machine collects the parameters of the IO request of itself, and determines whether IO adjacent bit interference occurs to itself, where the first virtual machine and the second virtual machine are the same virtual machine. Optionally, the first virtual machine collects parameters for all IO requests. Optionally, the first virtual machine samples parameters of the acquisition IO request, and a sampling interval thereof may be configured.
Specifically, as one example, the second virtual machine or the first virtual machine may use the linux blktrace tool to gather parameters of the IO request. As another example, the second virtual machine or the first virtual machine may gather parameters of the IO request using flexible IO tester tools.
The IO parameters acquired by the first virtual machine may include a block size of the IO request, a timestamp of sending the IO request, and a timestamp of completing the IO request. The block size of the IO request can represent the byte number of the IO request, and the unit can be the sector number; the timestamp of the IO request is the timestamp of the IO request sent to the cloud hard disk drive of the second virtual machine; the timestamp of completion of the IO request is the timestamp of completion of the data IO request.
And the first virtual machine determines whether IO adjacent bit interference occurs to the cloud hard disk of the second virtual machine to which the parameters belong according to the parameters of the IO request.
In some embodiments, the first virtual machine calculates to obtain a current unit sector processing delay of the IO request according to the block size of the IO request, the timestamp of sending the IO request and the timestamp of completing the IO request, and then determines whether IO ortho-interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs according to a deviation value of the calculated unit sector processing delay relative to a unit sector processing delay reference value.
Specifically, the first virtual machine determines a time difference between a time stamp of sending the IO request and a time stamp of completing the IO request, that is, a time delay of completing the IO request by the cloud hard disk. For example, the time difference between the time stamp of sending the IO request and the time stamp of completing the IO request may be calculated using the following formula.
D2C_t＝Complete_ts-Driver_ts
The d2c_t is a time delay from sending to driving until completion of each IO request, that is, a time delay when the cloud hard disk completes the IO request, complete_ts is a time stamp for completing the IO request, and driver_ts is a time stamp for sending the IO request.
Further, the first virtual machine determines a ratio of the time difference value to the block size of the IO request as a unit sector processing delay. For example, the unit sector processing delay of the current IO request may be calculated using the following formula.
Delay_p_Sector＝D2C_t/IO_size
Wherein delay_p_sector is the unit Sector processing Delay, d2c_t is the Delay for the cloud hard disk to complete the IO request, and io_size is the block size of the IO request.
Optionally, the first virtual machine may calculate to obtain the unit sector processing delay according to an average value of block sizes of the plurality of IO requests obtained in a preset time period and an average value of the plurality of d2c_t calculated in the preset time period.
Delay_p_Sector＝D2C_t’/IO_size’
Wherein delay_p_sector is the unit Sector processing Delay, d2c_t 'is the average value of the Delay of the cloud hard disk completing the IO request, and io_size' is the average value of the block size of the IO request.
Optionally, the first virtual machine may calculate the unit sector processing delay according to an average value of block sizes of the plurality of IO requests acquired in a preset time period and an average value of a plurality of d2c_t calculated in the preset time period, and a centralized area (the centralized area is configurable, for example, 90% of the ratio) is taken. In this way the influence of the data introduced by the tool or other extreme cases can be avoided.
In some embodiments, the unit sector processing latency reference may be a minimum unit sector processing latency of a calendar history of the second virtual machine recorded by the first virtual machine. The first virtual machine periodically monitors the unit sector processing time delay of the second virtual machine and records the historical minimum unit sector processing time delay. For example, the minimum unit sector processing delay can be recorded as follows.
Min_Delay_p_Sector＝Min(Delay_p_Sector 1…n )
Wherein Min_delay_p_Sector is the historical unit Sector processing Delay, delay_p_Sector 1…n Representing n single bit sector processing delays.
In other embodiments, the unit sector processing delay reference value may be a unit sector processing delay pre-estimate. The unit sector processing delay predicted value may be obtained by the first virtual machine according to the block size of the IO request, the timestamp of sending the IO request, the timestamp of completing the IO request, and the mapping relationship between the block size of the IO request, the timestamp of sending the IO request, the timestamp of completing the IO request and the unit sector processing delay predicted value.
Alternatively, the mapping may be fitted by a machine learning algorithm. Specifically, the following relation can be trained by comprehensively using the data collected on the distributed application virtual machine, and the algorithm parameter w is determined 0 、w 1 、w 2 、w 3 . After the algorithm is trained, the method is deployed on the first virtual machine, for IO_ size, complete _ts and driver_ts which are obtained each time, a unit sector processing time delay predicted value is obtained by using a fitted relation, and whether IO adjacent bit interference occurs to the cloud hard disk of the second virtual machine is further judged according to the calculated deviation value of the unit sector processing time delay relative to the unit sector processing time delay predicted value.
Delay_p_Sector＝w 3 *IO_size+w 2 *Complete_ts+w 1 *Driver_ts+w 0
Wherein w is 0 、w 1 、w 2 、w 3 Is an algorithm parameter.
The training of the machine learning algorithm may be performed in a development environment (for example, a private cloud or public cloud simulation environment, where no IO ortho-interference occurs), or in a public cloud production environment (assuming that the IO ortho-interference in the public cloud environment is a small probability event), and embodiments of the present application are not specifically limited.
It should be understood that more parameter types, such as the number of cloud hard disks, the traffic of the storage network interface, etc., can also be considered in the embodiment of the present application.
It should also be understood that the machine learning algorithm is merely exemplary, and the mapping relationship may also be obtained by fitting other algorithms having the same function as the machine learning algorithm, for example, a depth algorithm, a neural network algorithm, and the like, which are not particularly limited in the embodiment of the present application.
In other embodiments, the unit sector processing latency reference may be an average of historical minimum unit sector processing latencies for each virtual machine in a set of virtual machines, the set of virtual machines consisting of the second virtual machine and/or at least one third virtual machine having the same application as the second virtual machine. That is, the first virtual machine records the processing delay of the historical minimum unit sector of each virtual machine to be detected, and when performing IO ortho-position interference detection on the second virtual machine in the plurality of virtual machines to be detected, the processing delay of the historical minimum unit sector of the virtual machine to be detected, which has the same application as that of the second virtual machine, may be considered for comparison, and the processing delay of the historical minimum unit sector of the second virtual machine and the processing delay of the historical minimum unit sector of the at least one third virtual machine may also be considered for comparison.
In other embodiments, the unit sector processing latency reference value is any combination (e.g., two-by-two combination, three combination, etc.) of a historical minimum unit sector processing latency of the second virtual machine, a unit sector processing latency predicted value, an average of historical minimum unit sector processing latencies of each virtual machine in a set of virtual machines, the set of virtual machines consisting of the second virtual machine and/or at least one third virtual machine, the third virtual machine having the same application as the second virtual machine.
That is, the first virtual machine may determine whether the IO ortho-position interference occurs in the cloud hard disk of the second virtual machine according to any one of the historical minimum unit sector processing delay of the second virtual machine, the unit sector processing delay predicted value, and the average value of the historical minimum unit sector processing delay of each virtual machine in the virtual machine set, or may determine whether the IO ortho-position interference occurs in the cloud hard disk of the second virtual machine according to any combination of the historical minimum unit sector processing delay of the second virtual machine, the unit sector processing delay predicted value, and the average value of the historical minimum unit sector processing delay of each virtual machine in the virtual machine set.
In some embodiments, when the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than a first threshold (for example, 50%, where the first threshold is configurable), the first virtual machine determines that the IO ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs. Therefore, the detection precision can be flexibly adjusted by configuring a preset threshold value.
For example, when the unit sector processing delay exceeds 40% of the fluctuation of the historical minimum unit sector processing delay, the first virtual machine determines that the cloud hard disk of the second virtual machine has IO ortho-position interference.
In some embodiments, the first virtual machine periodically monitors a unit sector processing delay of the second virtual machine, and determines that the IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs in a continuous plurality of monitoring periods (for example, 3 continuous monitoring periods may be configured), where the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than a first threshold. Therefore, the influence of accidental factors on the detection result can be avoided, and the accuracy of the detection result is improved.
In some embodiments, the first virtual machine determines a time difference predicted value according to a mapping relationship between a block size of the IO request and the time difference according to a time difference between a time stamp of sending the IO request and a time stamp of completing the IO request (i.e., a time delay of completing the IO request by the cloud hard disk), and further determines whether IO ortho-position interference occurs in the cloud hard disk of the second virtual machine according to a deviation value of the calculated time difference relative to the time difference predicted value. The calculation method of the time difference (i.e. the time delay for the cloud hard disk to complete the IO request) may be referred to the above related description, and will not be described herein.
Optionally, the block of IO requestsThe mapping relationship between the size and the time difference can be obtained by fitting a machine learning algorithm. Specifically, the following relationships can be trained by comprehensively using the data collected on the distributed application virtual machine, and the algorithm parameters w are determined 0 、w 1 . After the algorithm is trained, the algorithm is deployed on the first virtual machine, a time difference predicted value is obtained by using a fitted relation for each obtained IO_size, and whether IO ortho-position interference occurs on the cloud hard disk of the second virtual machine is further judged according to the deviation value of the calculated time difference relative to the time difference predicted value.
D2C_t＝w 1 *IO_size+w 0
Wherein w is 0 、w 1 Is an algorithm parameter.
The training of the machine learning algorithm may be performed in a development environment (for example, a private cloud or public cloud simulation environment, where no IO ortho-interference occurs), or in a public cloud production environment (assuming that the IO ortho-interference in the public cloud environment is a small probability event), and embodiments of the present application are not specifically limited.
It should be understood that more parameter types, such as the number of cloud hard disks, the traffic of the storage network interface, etc., can also be considered in the embodiment of the present application.
It should also be understood that the machine learning algorithm is merely exemplary, and the mapping relationship may also be obtained by fitting other algorithms having the same function as the machine learning algorithm, for example, a depth algorithm, a neural network algorithm, and the like, which are not particularly limited in the embodiment of the present application.
In some embodiments, when the deviation value of the time difference relative to the time difference predicted value of the first virtual machine is greater than a second threshold (for example, 50%, where the second threshold is configurable), it is determined that the IO adjacent bit interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs. Therefore, the detection precision can be flexibly adjusted by configuring a preset threshold value.
For example, when the time difference exceeds 30% of the fluctuation of the time difference predicted value, the first virtual machine determines that the cloud hard disk of the second virtual machine generates IO ortho-position interference.
In some embodiments, the first virtual machine periodically monitors a time difference of the second virtual machine, and determines that the cloud hard disk of the second virtual machine to which the parameter belongs has IO ortho-interference in a continuous plurality of monitoring periods (for example, 3 continuous monitoring periods may be configured), where the deviation value of the time difference relative to the time difference predicted value is greater than two thresholds. Therefore, the influence of accidental factors on the detection result can be avoided, and the accuracy of the detection result is improved.
The above describes in detail an example of a method for detecting cloud hard disk IO ortho interference provided by the present application. It can be understood that, in order to achieve the above functions, the device for detecting the IO ortho-position interference of the cloud hard disk includes a hardware structure and/or a software module corresponding to each function. Those of skill in the art will readily appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as hardware or combinations of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
According to the method, the device for detecting the cloud hard disk IO ortho-position interference can be divided into the functional units according to the method example, for example, each function can be divided into each functional unit, and two or more functions can be integrated into one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that the division of the units in the present application is illustrative, and is merely a logic function division, and other division manners may be implemented in practice.
Fig. 3 is a schematic structural diagram of an apparatus for detecting cloud hard disk IO ortho-position interference according to an embodiment of the present application. As shown in fig. 3, the apparatus 300 is deployed with a virtual machine 340, and the virtual machine 340 includes an acquisition module 310 and a processing module 320.
The obtaining module 310 is configured to obtain parameters of the IO request, where the parameters include a block size of the IO request, a timestamp of sending the IO request, and a timestamp of completing the IO request.
And the processing module 320 is configured to determine, according to the parameter, whether IO ortho-interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs.
Optionally, the processing module 320 is specifically configured to determine a time difference between the timestamp of the sending IO request and the timestamp of the completing IO request; determining the ratio of the time difference value to the block size of the IO request as unit sector processing time delay; and determining whether IO (input/output) ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value.
Optionally, the unit sector processing delay reference value includes at least one of: the historical minimum unit sector processing time delay of the second virtual machine, the unit sector processing time delay predicted value and the average value of the historical minimum unit sector processing time delay of each virtual machine in a virtual machine set, wherein the unit sector processing time delay predicted value is obtained by the mapping relation of the first virtual machine according to the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request, the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request and the unit sector processing time delay predicted value, and the virtual machine set consists of the second virtual machine and/or at least one third virtual machine, and the third virtual machine and the second virtual machine have the same application.
Optionally, the processing module 320 is specifically configured to determine that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs when the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than a first threshold.
Optionally, the processing module 320 is specifically configured to determine, in a plurality of consecutive monitoring periods, that the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than the first threshold, and determine that IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs.
Optionally, the processing module 320 is specifically configured to determine a time difference between the timestamp of the sending IO request and the timestamp of the completing IO request; determining a time difference predicted value according to the block size of the IO request and the mapping relation between the block size of the IO request and the time difference; and determining whether IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the time difference relative to the time difference predicted value.
Optionally, the processing module 320 is specifically configured to determine that the IO ortho-interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs when the deviation value of the time difference relative to the time difference estimated value is greater than a second threshold.
Optionally, the processing module 320 is specifically configured to determine that IO adjacent bit interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs when the deviation value of the time difference relative to the time difference predicted value is greater than the second threshold in a plurality of consecutive monitoring periods.
Optionally, the virtual machine 340 further includes a storage module 330.
And the storage module 330 is configured to update the historical minimum unit sector processing delay in the database when the unit sector processing delay is smaller than the historical unit sector processing delay.
The specific functions and advantages of the acquisition module 310, the processing module 320 and the storage module 330 may be referred to as the method shown in fig. 2, and will not be described herein.
The acquisition module 310 may be a transceiver, a communication interface, or a processor. The storage module 330 may be a memory. The processing module 320 may be a processor or controller, such as a central processing unit (central processing unit, CPU), general purpose processor, digital signal processor (digital signal processor, DSP), application-specific integrated circuit (ASIC), field programmable gate array (field programmable gate array, FPGA) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor may also be a combination that performs the function of a computation, e.g., a combination comprising one or more microprocessors, a combination of a DSP and a microprocessor, etc.
When the acquisition module is a transceiver, the processing module is a processor, and the storage module is a memory, the device for detecting Yun Yingpan IO proximity interference according to the present application may be the device shown in fig. 4.
As shown in fig. 4, the apparatus 400 may include a transceiver 410, a processor 420, and a memory 430.
Only one memory and processor is shown in fig. 4. In an actual control device product, there may be one or more processors and one or more memories. The memory may also be referred to as a storage medium or storage device, etc. The memory may be provided separately from the processor or may be integrated with the processor, as the embodiments of the application are not limited in this respect.
The transceiver 410, the processor 420, and the memory 430 communicate with each other via internal communication paths to transfer control and/or data signals.
Specifically, transceiver 410 is configured to obtain parameters of the IO request, where the parameters include a block size of the IO request, a timestamp of sending the IO request, and a timestamp of completing the IO request. The processor 420 is configured to determine, according to the parameter, whether IO ortho-interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs.
The specific operation and benefits of the apparatus 400 may be seen from the description of the embodiment shown in fig. 2.
Fig. 5 is a schematic structural diagram of a detection system according to an embodiment of the present application. Taking the case where the first virtual machine and the second virtual machine are different virtual machines as an example, fig. 5 is merely exemplary. As shown in fig. 5, virtual machine 540 may correspond to the first virtual machine above (the acquisition module is not shown); virtual machine 521, virtual machine 522, and virtual machine 531 may correspond to the second virtual machine above, and when the same application is deployed in virtual machine 521, virtual machine 522, and virtual machine 531, virtual machine 521, virtual machine 522, and virtual machine 531 may also correspond to the third virtual machine above.
Specifically, after the virtual machine 521, the virtual machine 522 and the virtual machine 531 collect the parameters of the IO request through the collection module 523, the collection module 524 and the collection module 532, the parameters are reported to the virtual machine 540, and the reporting interval is configurable. The virtual machine 540 receives the parameters of the IO requests reported by the virtual machine 521, the virtual machine 522 and the virtual machine 531, updates the database, judges and identifies the IO ortho-interference, and performs alarming and subsequent corresponding processing (for example, log and alarming; applying for a new virtual machine, and after deploying a service, replacing the virtual machine with the IO ortho-interference, etc.).
The specific operation and benefits of the detection system 500 may be described with reference to the embodiment shown in fig. 2.
In various embodiments of the present application, the sequence number of each process does not mean the sequence of execution sequence, and the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.
The steps of a method or algorithm described in connection with the present disclosure may be embodied in hardware, or may be embodied in software instructions executed by a processor. The software instructions may be comprised of corresponding software modules that may be stored in random access memory (random access memory, RAM), flash memory, read Only Memory (ROM), erasable programmable read only memory (erasable programmable ROM), electrically Erasable Programmable Read Only Memory (EEPROM), registers, hard disk, a removable disk, a compact disc read only memory (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions in accordance with the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in or transmitted across a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital versatile disk (digital versatile disc, DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The foregoing embodiments have been provided for the purpose of illustrating the general principles of the present application in further detail, and are not to be construed as limiting the scope of the application, but are merely intended to cover any modifications, equivalents, improvements, etc. based on the teachings of the application.
Claims (13)
1. A method for detecting cloud hard disk input/output (IO) ortho interference, comprising:
the method comprises the steps that a first virtual machine obtains parameters of an IO request, wherein the parameters comprise the block size of the IO request, a time stamp for sending the IO request and a time stamp for completing the IO request;
the first virtual machine determines a time difference between the time stamp of the sent IO request and the time stamp of the completed IO request;
the first virtual machine determines that the ratio of the time difference to the block size of the IO request is unit sector processing delay;
and the first virtual machine determines whether IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value.
2. The method of claim 1, wherein the unit sector processing delay reference value comprises at least one of: the historical minimum unit sector processing delay of the second virtual machine, the unit sector processing delay predicted value and the average value of the historical minimum unit sector processing delay of each virtual machine in a virtual machine set, wherein the unit sector processing delay predicted value is obtained by the mapping relation of the first virtual machine according to the block size of the IO request, the timestamp of sending the IO request and the timestamp of finishing the IO request, the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request and the unit sector processing delay predicted value, and the virtual machine set is composed of the second virtual machine and/or at least one third virtual machine, and the third virtual machine and the second virtual machine have the same application.
3. The method of claim 1, wherein the determining, by the first virtual machine, whether IO ortho-position interference occurs in a cloud hard disk of the second virtual machine to which the parameter belongs according to a deviation value of the unit sector processing delay relative to a unit sector processing delay reference value includes:
And when the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than a first threshold value, the first virtual machine determines that IO (input/output) ortho-position interference occurs on the cloud hard disk of the second virtual machine to which the parameter belongs.
4. The method of claim 3, wherein the determining, by the first virtual machine, that the IO ortho-position interference occurs in the cloud hard disk of the second virtual machine to which the parameter belongs when the deviation value of the unit sector processing delay relative to the unit sector processing delay reference value is greater than a first threshold value comprises:
and in a plurality of continuous monitoring periods, the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than the first threshold value, and the IO ortho-position interference of the cloud hard disk of the second virtual machine to which the parameter belongs is determined.
5. The method of claim 2, wherein when the unit sector processing delay is less than the historical minimum unit sector processing delay, the method further comprises:
and the first virtual machine updates the historical minimum unit sector processing delay in the database.
6. An apparatus for detecting cloud hard disk input/output (IO) ortho interference, wherein the apparatus is deployed with a virtual machine, the virtual machine comprising:
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring parameters of an IO request, wherein the parameters comprise the block size of the IO request, a time stamp for sending the IO request and a time stamp for completing the IO request;
a processing module, configured to determine a time difference between the time stamp of the sending IO request and the time stamp of the completing IO request; determining the ratio of the time difference to the block size of the IO request as unit sector processing time delay; and determining whether IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs according to the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value.
7. The apparatus of claim 6, wherein the unit sector processing delay reference value comprises at least one of: the historical minimum unit sector processing time delay of the second virtual machine, the unit sector processing time delay predicted value and the average value of the historical minimum unit sector processing time delay of each virtual machine in a virtual machine set, wherein the unit sector processing time delay predicted value is obtained by the device according to the mapping relation of the block size of the IO request, the timestamp of sending the IO request and the timestamp of finishing the IO request, the block size of the IO request, the timestamp of sending the IO request, the timestamp of finishing the IO request and the unit sector processing time delay predicted value, and the virtual machine set consists of the second virtual machine and/or at least one third virtual machine which has the same application as the second virtual machine.
8. The apparatus of claim 6, wherein the processing module is specifically configured to:
and when the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than a first threshold value, determining that IO ortho-position interference occurs to the cloud hard disk of the second virtual machine to which the parameter belongs.
9. The apparatus of claim 8, wherein the processing module is specifically configured to:
and in a plurality of continuous monitoring periods, the deviation value of the unit sector processing time delay relative to the unit sector processing time delay reference value is larger than the first threshold value, and the IO ortho-position interference of the cloud hard disk of the second virtual machine to which the parameter belongs is determined.
10. The apparatus of claim 7, wherein the virtual machine further comprises:
and the storage module is used for updating the historical minimum unit sector processing time delay in the database when the unit sector processing time delay is smaller than the historical minimum unit sector processing time delay.
11. An apparatus for detecting cloud hard disk input output, IO, ortho-interference, comprising a transceiver, a processor, and a memory for performing the method of any of claims 1-5.
12. A computer-readable storage medium comprising instructions that, when executed by a processing module or processor, cause an apparatus for detecting cloud hard disk input output, IO, ortho-interference to perform the method of any one of claims 1-5.
13. A chip characterized in that instructions are stored which, when run on a device for detecting cloud hard disk input output, IO, orthotopic disturbances, cause the chip to perform the method of any one of claims 1 to 5.
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