Storage equipment performance query method and device, storage medium and electronic equipment

Abstract

The embodiment of the invention provides a storage equipment performance query method and device, a storage medium and electronic equipment, and the method comprises the steps: detecting a target query request triggered on a target operation interface; in response to the target query request, collecting first performance change information in a reference time period from target storage equipment; performing division operation on the first performance change information according to the monitoring period to obtain multiple pieces of second performance change information; converting the plurality of pieces of second performance change information into a distribution diagram; and displaying the distribution map on the target operation interface. Through the method and the device, the problem of relatively low query efficiency of the performance of the storage device is solved, and the effect of improving the query efficiency of the performance of the storage device is achieved.

Description

Technical Field

[0001] Embodiments of the present application relate to the technical field of storage device performance query, and more specifically, to a storage device performance query method, apparatus, storage medium, and electronic device. Background Art

[0002] Performance indicators are monitoring data generated during the daily operation of storage devices, and are the basis for customers to analyze whether the storage devices are in good operating condition.

[0003] In the related art, when monitoring whether the operating performance of a storage device is normal, it is often monitored by comparing year-on-year or calculating the maximum reference value. By comparing year-on-year, only the object dimension and the indicator dimension are used for comparative analysis, and only the differences between different objects can be compared, while the monitored objects themselves cannot be compared and abnormalities cannot be identified. Although the method of comparing the maximum reference value with the current value is no longer an analysis based on raw data, it still cannot meet the needs of the performance data of the monitored object itself.

[0004] It is understandable that in the related art, it is impossible to accurately identify abnormal conditions in the operation of the equipment. Summary of the invention

[0005] The embodiments of the present application provide a method, apparatus, storage medium and electronic device for querying storage device performance, so as to at least solve the problem of low efficiency in querying storage device performance in the related art.

[0006] According to an embodiment of the present application, a method for querying the performance of a storage device is provided, which is applied to a monitoring device, wherein the monitoring device is used to connect to a storage device, and a target operation interface is deployed on the monitoring device, the method comprising: detecting a target query request triggered on the target operation interface, wherein the target query request is used to request a query on the distribution of the running performance of the connected target storage device under a target monitoring item; in response to the target query request, collecting first performance change information within a reference time period from the target storage device, wherein the first performance change information is used to indicate the distribution of the running performance of the target storage device under the target monitoring item within the reference time period; performing a division operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate the distribution of the running performance of the target storage device under the target monitoring item within the monitoring period; converting the plurality of second performance change information into a distribution graph, wherein the distribution graph is used to show the change of the running performance of the target storage device under the target monitoring item at each monitoring time as the monitoring time progresses, wherein the monitoring period includes a plurality of the monitoring times; and displaying the distribution graph on the target operation interface.

[0007] In an exemplary embodiment, collecting the first performance change information within a reference time period from the target storage device includes: detecting whether the target query request carries the target time period to obtain a detection result; determining the reference time period based on the detection result; and collecting the first performance change information within the reference time period from the target storage device.

[0008] In an exemplary embodiment, determining the reference time period based on the detection result includes: when the detection result is used to indicate that the target time period is not carried in the target query request, determining the reference time period as a first time difference between a first time and a current time, wherein the first time is earlier than the current time, and the current time is the time when the target query request is triggered on the target operation interface; when the detection result is used to indicate that the target query request carries the target time period, obtaining a candidate time period between a second time and the current time, wherein the second time is earlier than the current time, the second time is the recording time of the first performance change information allowed to be collected on the target storage device, the current time is the time when the target query request is triggered on the target operation interface, the candidate time period includes the target time period, the second time is earlier than the first time, the candidate time period is displayed on the target operation interface, and the time period in the candidate time period in which the selection operation is performed is determined as the reference time period.

[0009] In an exemplary embodiment, the dividing operation is performed on the first performance change information according to the monitoring period to obtain multiple second performance change information, including: when the length of the reference time period is N and the monitoring duration of the monitoring period is M, by performing the following steps, the dividing operation is performed on the first performance change information according to the monitoring period to obtain P performance change information, wherein the multiple second performance change information include the P performance change information, N is a positive integer, M is a positive integer, and P is a positive integer: according to the monitoring duration of the monitoring period, the reference time period is divided into P monitoring periods, wherein each monitoring duration in the P monitoring periods is M, and P is equal to a positive integer obtained by rounding up the value obtained by dividing N by M; the first performance change information is divided into P performance change information corresponding to the P monitoring periods, wherein the i-th performance change information among the P performance change information includes the performance change information corresponding to the i-th monitoring period in the first performance change information, the P monitoring periods include the i-th monitoring period, and i is a positive integer less than or equal to P.

[0010] In an exemplary embodiment, the converting of the plurality of the second performance change information into a distribution graph includes: converting the plurality of the second performance change information into a plurality of average performance change information, wherein the average performance change information is used to indicate the distribution of the average operating performance of the target storage device under the target monitoring item at each of the monitoring times within each of the monitoring cycles; converting the plurality of average performance change information into the distribution graph.

[0011] In an exemplary embodiment, the converting of the plurality of the second performance change information into a plurality of average performance change information comprises: when the plurality of the second performance change information comprises P performance change information, converting the jth group of performance information of the i-th performance change information at the j-th monitoring time among the P performance change information into the j-th average performance information by executing the following steps, wherein the i-th average performance change information comprises R average performance information obtained by converting R groups of performance information at R monitoring times among the i-th performance change information, the R average performance information comprises the j-th average performance information, the i-th performance change information comprises R groups of performance information under the target monitoring item of the target storage device at the R monitoring times within the i-th monitoring cycle, the R groups of performance information comprises the j-th group of performance information, the plurality of average performance change information comprises P average performance change information, the P average performance change information Including the i-th average performance change information, the P performance change information corresponds to P monitoring cycles, the P monitoring cycles include the i-th monitoring cycle, P is a positive integer, j is a positive integer less than or equal to R, i is a positive integer less than or equal to P, and R is a positive integer: performing an average value operation on the j-th group of performance information to obtain the j-th average performance information; converting the multiple average performance change information into the distribution graph, including: when the multiple average performance change information includes P average performance change information and the multiple monitoring times include R monitoring times, converting the P average performance change information into the distribution graph by executing the following steps: using the P performance change information to generate the distribution graph in a target coordinate system, wherein the abscissa of the target coordinate system represents the R monitoring times, and the ordinate of the target coordinate system represents the average operating performance of the target storage device under the target monitoring item at the R monitoring times.

[0012] In an exemplary embodiment, after converting the plurality of second performance change information into a distribution graph, the method further includes: calculating, based on the distribution graph, candidate operating parameters of the target storage device at each monitoring time in the monitoring period; searching for the largest target operating parameter from the candidate operating parameters; detecting whether the ring ratio of the target storage device at the target monitoring time corresponding to the target operating parameter is greater than or equal to a ring ratio threshold; and generating an alarm message when it is detected that the ring ratio is greater than or equal to the ring ratio threshold, wherein the alarm message is used to prompt that the operating performance of the target storage device at the target monitoring time is abnormal.

[0013] In an exemplary embodiment, the calculating, according to the distribution graph, the candidate operating parameters of the target storage device at each monitoring time in the monitoring period, comprises: calculating, according to the distribution graph, the sth operating parameter of the target storage device at the sth monitoring time by executing the following steps, wherein the candidate operating parameter comprises the sth operating parameter, the reference time period is divided into P monitoring periods, each monitoring period in the P monitoring periods comprises R monitoring times, the R monitoring times comprise the sth monitoring time, P is a positive integer, R is a positive integer, and s is a positive integer less than or equal to R: extracting the target storage device from the distribution graph the sth group of average operating performance of the storage device at the sth monitoring time, wherein the wth average operating performance in the sth group of average operating performance is the average operating performance of the target storage device at the sth monitoring time in the wth monitoring cycle in the P monitoring cycles, and w is a positive integer less than or equal to s; calculating the sth group of ring ratios according to the sth group of average operating performance, and performing an average value operation on the sth group of ring ratios to obtain the sth average ring ratio; calculating the sth standard deviation corresponding to the sth monitoring time according to the sth average ring ratio and the sth group of ring ratios, wherein the sth operating parameter includes the sth standard deviation.

[0014] In an exemplary embodiment, calculating the sth group of ring ratios according to the sth group of average operating performance, and performing an average value operation on the sth group of ring ratios to obtain an sth average ring ratio includes: calculating the rth ring ratio in the sth group of ring ratios by the following formula, where r is a positive integer less than or equal to s:

[0015]

[0016] Among them, q r is the rth ring ratio, t r is the rth average running performance in the sth group of average running performances, t r+1 is the r+1th average operating performance in the sth group of average operating performances, the rth average operating performance is the average operating performance of the target storage device at the sth monitoring time in the rth monitoring cycle in the P monitoring cycles, and the r+1th average operating performance is the average operating performance of the target storage device at the sth monitoring time in the r+1th monitoring cycle in the P monitoring cycles; according to the sth average ring ratio and the sth group of ring ratios, calculating the sth standard deviation corresponding to the sth monitoring time, wherein the sth operating parameter includes the sth standard deviation, including: when the sth group of ring ratios includes P ring ratios, calculating the sth standard deviation by the following formula:

[0017]

[0018] Among them, o' s is the sth standard deviation, qr t is the tth ring ratio in the sth group of ring ratios, and μ is the sth average ring ratio.

[0019] According to another embodiment of the present application, a storage device performance query device is provided, which is applied to a monitoring device, wherein the monitoring device is used to connect to the storage device, and a target operation interface is deployed on the monitoring device, and the device includes: a first detection module, which is used to detect a target query request triggered on the target operation interface, wherein the target query request is used to request a query on the distribution of the operating performance of the connected target storage device under the target monitoring item; and a collection module, which is used to respond to the target query request and collect first performance change information within a reference time period from the target storage device, wherein the first performance change information is used to indicate whether the target storage device has changed within the reference time period within the target storage device. The invention relates to a method for distributing the operating performance of the target storage device under the target monitoring item in the monitoring period by performing a division operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item during the monitoring period; a conversion module is used to convert the plurality of second performance change information into a distribution graph, wherein the distribution graph is used to show how the operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time, and the monitoring period includes a plurality of the monitoring times; a display module is used to display the distribution graph on the target operation interface.

[0020] According to another embodiment of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when run.

[0021] According to another embodiment of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0022] According to another embodiment of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0023] Through this application, a visual operation interface is provided on the monitoring device, and the user can query the distribution of the operating performance of the specified storage device under the specified monitoring item through the visual operation interface. The monitoring device can, but is not limited to, divide the collected performance change information according to the monitoring period (such as one day, one week or one month), and convert the divided performance change information into a distribution map, so that the changes in the operating performance of the storage device in different monitoring periods are displayed in the distribution map in an overlapping manner. In this way, the user can intuitively query whether the operating performance in different monitoring periods is normal. Therefore, the problem of low query efficiency of storage device performance can be solved, and the effect of improving the query efficiency of storage device performance can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a hardware structure block diagram of a server device of a storage device performance query method according to an embodiment of the present application;

[0025] Figure 2 is a flow chart of a method for querying storage device performance according to an embodiment of the present application;

[0026] Figure 3 is a schematic diagram of a visualization process of an optional method for querying storage device performance according to an embodiment of the present application;

[0027] Figure 4 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 1 ;

[0028] Figure 5 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 2 ;

[0029] Figure 6 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 3 ;

[0030] Figure 7 is a functional schematic diagram of an optional performance indicator data comparison and analysis module based on a month-on-month basis according to an embodiment of the present application;

[0031] Figure 8 It is a structural block diagram of a storage device performance query device according to an embodiment of the present application. DETAILED DESCRIPTION

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

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

[0034] First, the terms involved in the embodiments of the present application are explained as follows:

[0035] Month-on-month value: refers to the data compared with the previous period, reflecting the comparison of the actual data of the two periods. For example, if the value of this period is 5 and the value of the previous period is 4, then the month-on-month value will be: 5 for this period, 4 for the previous period.

[0036] Ring ratio: refers to the rate of change compared with the previous period (positive / negative), reflecting the changes between the two periods. For example, if the current period value is 5 and the previous period value is 4, the ring ratio is: (current period - previous period) / previous period = 0.25.

[0037] IOPS: Input / Output Operations Per Second, is a measurement method used to test the performance of computer storage devices (such as hard disk drives (HDD), solid state drives (SSD) or storage area networks (SAN)), which can be regarded as the number of reads and writes per second.

[0038] Standard deviation: The square root of the square of the deviation from the mean (i.e., variance), denoted by σ. Standard deviation is also called standard deviation or experimental standard deviation. It is most commonly used in probability statistics as a measure of the degree of statistical distribution. Standard deviation can reflect the degree of dispersion of a data set.

[0039] The method embodiments provided in the embodiments of the present application can be executed in a server device or a similar computing device. Taking running on a server device as an example, Figure 1 1 is a hardware structure block diagram of a server device of a storage device performance query method according to an embodiment of the present application. Figure 1 As shown, the server device may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the server device may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above server device. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0040] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for querying the performance of the storage device in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, to implement the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the server device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0041] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the server device. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0042] In this embodiment, a method for querying the performance of a storage device is provided, which is applied to a monitoring device, wherein the monitoring device is used to connect to the storage device, and a target operation interface is deployed on the monitoring device. Figure 2 is a flow chart of a method for querying storage device performance according to an embodiment of the present application, such as Figure 2 As shown, the process includes the following steps:

[0043] Step S202, detecting a target query request triggered on the target operation interface, wherein the target query request is used to request a query on the distribution of the operating performance of the connected target storage device under the target monitoring item;

[0044] Step S204, in response to the target query request, collecting first performance change information within a reference time period from the target storage device, wherein the first performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item within the reference time period;

[0045] Step S206, performing a division operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item during the monitoring period;

[0046] Step S208, converting the plurality of the second performance change information into a distribution graph, wherein the distribution graph is used to show how the operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time, and the monitoring cycle includes a plurality of the monitoring times;

[0047] Step S210: displaying the distribution graph on the target operation interface.

[0048] Through the above steps, a visual operation interface is provided on the monitoring device, and the user can query the distribution of the operating performance of the specified storage device under the specified monitoring item through the visual operation interface. The monitoring device can, but is not limited to, divide the collected performance change information according to the monitoring period (such as one day, one week or one month), and convert the divided performance change information into a distribution map, so that the changes in the operating performance of the storage device in different monitoring periods are displayed in the distribution map in an overlapping manner. In this way, the user can intuitively query whether the operating performance in different monitoring periods is normal. Therefore, the problem of low query efficiency of storage device performance can be solved, and the effect of improving the query efficiency of storage device performance can be achieved.

[0049] In the technical solution provided in the above step S202, the operating performance of the storage device can be monitored by one or more monitoring items, but is not limited to, and the monitoring items can include but are not limited to performance indicators used to reflect the operating performance of the storage device. For example, the monitoring items can include but are not limited to the IOPS, bandwidth, latency, etc. of the storage device.

[0050] Optionally, in this embodiment, the distribution status may be, but is not limited to, used to indicate a change trend of the operating performance of the target storage device at each monitoring time point in the reference time period.

[0051] Optionally, in this embodiment, the data reading and writing device can be but is not limited to being configured to allow reading and writing of data stored in the storage device. For example, the data reading and writing device can be but is not limited to including a server, and the storage device can be but is not limited to including a hard disk or a RAID array, etc.

[0052] Figure 3 is a schematic diagram of a visualization process of an optional method for querying storage device performance according to an embodiment of the present application, such as Figure 3As shown, the monitoring device 301 is connected to the storage device 302, and the target operation interface 301-1 is deployed on the monitoring device 301. The user can, but is not limited to, trigger a target query request through the target operation interface 301-1. For example, the user can, but is not limited to, select the monitoring items (e.g., performance indicators, IOPS, bandwidth, latency) of the storage device 302 to be queried on the target operation interface 301-1; the user can, but is not limited to, select the monitoring period to be monitored on the target operation interface 301-1, such as day, week, month, custom period, etc.; the user can, but is not limited to, select the time period to be monitored (equivalent to the reference time period) on the target operation interface 301-1, such as , 0:00 on October 7, 2024 to 0:00 on October 20, 2024, etc.; when the user chooses to generate a distribution graph on the target operation interface 301-1, the monitoring device 301 can, but is not limited to, collect performance change data (equivalent to the first performance change information) from the storage device 302 from 0:00 on October 7, 2024 to 0:00 on October 20, 2024, and can, but is not limited to, be segmented according to the monitoring cycle (for example, 7 days) to form the second performance change information, and further convert the second performance change information into a distribution graph. Finally, the generated distribution graph is intuitively displayed on the target operation interface 301-1.

[0053] For example, curve A and curve B can be displayed overlappingly on the target operation interface 301-1, but are not limited to, wherein curve A represents the changing trend of the bandwidth performance of the storage device from 0:00 on October 7, 2024 to 0:00 on October 14, 2024, and curve B represents the changing trend of the bandwidth performance of the storage device from 0:00 on October 14, 2024 to 0:00 on October 21, 2024.

[0054] In the technical solution provided in the above step S204, the reference time period may include but is not limited to the time period specified by the user for query, or the reference time period may include but is not limited to the time period determined by the monitoring device itself when the user does not specify the time period for query, such as the last week, the last month, etc.

[0055] In an exemplary embodiment, the first performance change information within a reference time period can be collected from the target storage device in the following manner, but is not limited to: detecting whether the target query request carries the target time period to obtain a detection result; determining the reference time period based on the detection result; and collecting the first performance change information within the reference time period from the target storage device.

[0056] Optionally, in this embodiment, the granularity of the reference time period and the target time period (for example, one day, half a day, one hour, etc.) can be but is not limited to being the same. For example, the monitoring period set by the user is one week, and the operating performance of the storage device under the monitoring item every day of the week can be but is not limited to being monitored. In this case, the granularity of the reference time period can be but is not limited to also being one day.

[0057] Optionally, in this embodiment, the target time period may include but is not limited to the time period set by the user on the target operation interface. For example, the user specifies the start time and the end time, and the duration of the target time period may include but is not limited to the duration from the start time to the end time.

[0058] In this way, the target time period initially specified by the user on the target operation interface may not be the time period that the user actually wishes to query. For example, when the time period that the user wishes to query is longer, the user may only indicate part of the time period that the user wishes to query on the target operation interface, or the user may incorrectly input the time period that the user wishes to query. In such a case, the reference time period that the user actually wishes to query can be determined based on, but not limited to, whether the user has specified the target time period, thereby improving the accuracy of the determined reference time period.

[0059] In an exemplary embodiment, the reference time period may be determined according to the detection result in the following manner, but not limited to: when the detection result indicates that the target time period is not carried in the target query request, the reference time period is determined as a first time difference between a first time and a current time, wherein the first time is earlier than the current time, and the current time is the time when the target query request is triggered on the target operation interface; when the detection result indicates that the target time period is carried in the target query request, a candidate time period between a second time and the current time is obtained, wherein the second time is earlier than the current time, the second time is the recording time of the first performance change information allowed to be collected on the target storage device, the current time is the time when the target query request is triggered on the target operation interface, the candidate time period includes the target time period, the second time is earlier than the first time, the candidate time period is displayed on the target operation interface, and the time period in the candidate time period where the selection operation is performed is determined as the reference time period.

[0060] Optionally, in this embodiment, when the user does not specify a desired query time period on the target query interface, the first time difference may include but is not limited to a preset fixed period, such as the last day, the last week, or the last month, etc.

[0061] Optionally, in this embodiment, the reference time period may be, but is not limited to, the same as the target time period, or partially the same as, or completely different from, etc. The overlapping portion of the reference time period and the target time period may be, but is not limited to, continuous or discontinuous. When the reference time period is exactly the same as the target time period, it can be used, but not limited to, to indicate that the two time periods completely overlap, for example, the target time period is this week and the reference time period is also this week; when the reference time period is partially the same as the target time period, it can be used, but not limited to, to indicate that some time points of the two time periods overlap, but the overall match is not completely matched, for example, the target time period is this week, and the reference time period may include the first half of this week and the second half of last week; when the reference time period is completely different from the target time period, it can be used, but not limited to, to indicate that there is no overlap in time points between the two, for example, the target time period is this week, and the reference time period is a specific period of last month; when the overlapping part of the reference time period and the target time period is continuous, it can be used, but not limited to, to indicate that the overlapping part of the reference time period and the target time period is continuous in time, for example, the target time period is the third week of this month, and the reference time period includes the second and third weeks; when the overlapping part of the reference time period and the target time period is discontinuous, it can be used, but not limited to, including that the overlapping part of the reference time period and the target time period is discontinuous in time, such as the target time period is this week, and the reference time period is Monday of this week and Tuesday to Thursday of last week.

[0062] In this way, on the one hand, the user can directly set the time period they want to query through the target operation interface. On the other hand, the user can also only set the storage devices and monitoring items they want to query without setting the time period they want to query. This provides users with a variety of ways to set the time period they want to query, thereby improving the user experience.

[0063] In the technical solution provided in the above step S206, the collected performance change information may be divided according to, but not limited to, a monitoring period, and the monitoring period may include, but not limited to, a fixed period, such as one day, one week, one month, etc. The specifications supported by the fixed period month-on-month value view are shown in Table 1:

[0064] Table 1

[0065]

[0066] Optionally, in this embodiment, the monitoring cycle may include but is not limited to a user-defined cycle. The user may select any custom time period t, and then the monitoring device generates time periods a1, a2, a3... of the same time span from the end time backward according to the start time, end time, and time span of the custom time period, until the current day. If the time span is less than one, it is completed according to the future time, and the corresponding indicator data is empty; the time periods b1, b2, b3... of the same time span are generated from the start time forward until the earliest time of the indicator data. Then the user selects the comparison time period of interest from...a3, a2, a1, t, b1, b2, b3... as needed as the comparison time period of the month-on-month view (equivalent to the distribution chart). The time span of t is determined. If it is within 1 day, it can be but not limited to presenting the curve at a granularity of 1 point per minute; if it is more than 1 day and less than 7 days, it can be but not limited to presenting the curve at a granularity of 1 point per hour; if it is more than 7 days, it can be but not limited to presenting the curve at a granularity of 1 point per day; finally, the performance indicator data of the selected time period are displayed overlappingly in the same distribution chart.

[0067] In an exemplary embodiment, the first performance change information may be divided according to the monitoring period to obtain multiple second performance change information in the following manner, but is not limited to: when the length of the reference time period is N and the monitoring period of the monitoring period is M, the first performance change information is divided according to the monitoring period to obtain P performance change information by performing the following steps, wherein the multiple second performance change information include the P performance change information, N is a positive integer, M is a positive integer, and P is a positive integer: according to the monitoring period of the monitoring period, the reference time period is divided into P monitoring periods, wherein each monitoring period in the P monitoring periods is M, and P is equal to a positive integer obtained by rounding up the value obtained by dividing N by M; the first performance change information is divided into P performance change information corresponding to the P monitoring periods, wherein the i-th performance change information among the P performance change information includes the performance change information corresponding to the i-th monitoring period in the first performance change information, the P monitoring periods include the i-th monitoring period, and i is a positive integer less than or equal to P.

[0068] Optionally, in this embodiment, when the value obtained by dividing N by M is not an integer, P can be set to, but is not limited to, a value obtained by rounding up the value obtained by dividing N by M. For example, the value obtained by dividing N by M is 2.5. In this case, P is equal to 3. In this case, for the time when no data is generated, the corresponding data can be set to empty, but is not limited to.

[0069] For example, if a time period (target time period) selected by the user is moved forward or backward by P monitoring cycles, the time series fails to completely cover the set number of cycles, that is, there is a situation where the time period is "shortage" or "overflow" the current data range, the monitoring device can, but is not limited to, adopt the following strategy for completion processing: If the target time period is moved forward or backward by P monitoring cycles, the time series is insufficient to cover all cycles, the monitoring device will automatically extend the target time period to the future time to make up for the missing number of cycles. For these future time points, since the actual performance indicator data has not yet been generated, the corresponding indicator data field will be set to a null value.

[0070] Optionally, in this embodiment, when the value obtained by dividing N by M is an integer, P can be set to be equal to the value obtained by dividing N by M, but is not limited to it.

[0071] In this way, the extracted performance change information is divided into an integer number of monitoring periods, thereby avoiding the possibility that the number of performance change information corresponding to different monitoring periods may be different, and improving the comparability between the performance change information corresponding to different monitoring periods.

[0072] In the technical solution provided in the above step S208, the distribution graph may include, but is not limited to, a fixed-cycle month-on-month value distribution graph, or a custom-cycle month-on-month value distribution graph, etc. For example, a fixed-cycle month-on-month value distribution graph may display, but is not limited to, the month-on-month value of the performance indicator data of the same monitoring item in different fixed periods (such as one day, one week, or one month) to observe the periodic changes in performance over time; a custom-cycle month-on-month value distribution graph may allow, but is not limited to, the user to select any time period t as a comparison benchmark, and the monitoring device will generate a series of comparison time periods with the same time span based on this time period, which may be a historical time period before t or a future time period after t. The user can freely select these time periods for comparison, and the distribution graph will display the month-on-month value of the performance indicator data of the same object in the selected time period.

[0073] By supporting the use of common time division standards (for example, days, weeks, or months, etc.) to create fixed-period year-on-year value views, it is possible to simply, conveniently, and quickly display the performance indicator value comparison of the same object in different time periods. By supporting the selection of custom time periods to create year-on-year views, it is more flexible and convenient for users to display the year-on-year value comparison chart of the same object in any historical time period as needed, providing users with a comprehensive and customized data analysis platform.

[0074] In an exemplary embodiment, the multiple second performance change information can be converted into a distribution graph in the following manner but is not limited to: converting the multiple second performance change information into multiple average performance change information, wherein the average performance change information is used to indicate the distribution of the average operating performance of the target storage device under the target monitoring item at each of the monitoring times within each of the monitoring cycles; converting the multiple average performance change information into the distribution graph.

[0075] Optionally, in this embodiment, multiple second performance change information can be converted into multiple reference performance change information in the following manner, but not limited to, wherein the reference performance change information is used to indicate how the maximum operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time: converting the multiple reference performance change information into the distribution graph, wherein the distribution graph is used to indicate how the maximum operating performance of the target storage device under the target monitoring item at each monitoring time within each monitoring cycle changes with the monitoring time.

[0076] In this way, the maximum operating performance corresponding to the storage device under the monitoring item can be used but not limited to represent the current storage business of the storage device. By displaying the distribution of the maximum operating performance of the storage device under the same monitoring item in different monitoring periods on a month-on-month basis, the accuracy of users in determining whether the operating performance of the storage device is abnormal is improved.

[0077] In an exemplary embodiment, multiple second performance change information can be converted into multiple average performance change information in the following manner but is not limited to: when the multiple second performance change information include P performance change information, the j-th group of performance information of the i-th performance change information at the j-th monitoring time among the P performance change information is converted into the j-th average performance information by executing the following steps, wherein the i-th average performance change information includes R average performance information obtained by converting R groups of performance information at R monitoring times in the i-th performance change information, the R average performance information includes the j-th average performance information, the i-th performance change information includes R groups of performance information under the target monitoring item of the target storage device at the R monitoring times within the i-th monitoring period, the R groups of performance information include the j-th group of performance information, the multiple average performance change information include P average performance change information, and the P average The performance change information includes the i-th average performance change information, the P performance change information corresponds to P monitoring cycles, the P monitoring cycles include the i-th monitoring cycle, P is a positive integer, j is a positive integer less than or equal to R, i is a positive integer less than or equal to P, and R is a positive integer: an average value operation is performed on the j-th group of performance information to obtain the j-th average performance information; the multiple average performance change information is converted into the distribution graph: when the multiple average performance change information includes P average performance change information and the multiple monitoring times include R monitoring times, the P average performance change information is converted into the distribution graph by executing the following steps: the distribution graph is generated in a target coordinate system using the P performance change information, wherein the abscissa of the target coordinate system represents the R monitoring times, and the ordinate of the target coordinate system represents the average operating performance of the target storage device under the target monitoring item at the R monitoring times.

[0078] Optionally, in this embodiment, the change trend of the performance indicator data of each monitoring period in the distribution diagram may be represented by, but not limited to, different curves. Each curve may include, but not limited to, the change trend of the performance indicator data of the same monitoring object in different monitoring periods. For example, one curve may represent the change trend of the IOPS average performance of the storage device performance indicator last week, while another curve represents the change trend of the IOPS average performance of the storage device performance indicator this week.

[0079] Optionally, in this embodiment, the changes in the operating performance of the storage device under the same monitoring item in different monitoring periods can be displayed in an overlapping manner in the same distribution graph, but is not limited to. For example, at the same time point (such as Wednesday), the user can see the performance indicator values ​​of different monitoring periods (this Wednesday, last Wednesday, the Wednesday before last, etc.).

[0080] Through the embodiments of the present application, the distribution graph obtains multiple groups of performance indicator data of the same cycle and different time periods by analyzing the data, and uses multiple curves to overlap and display them in the same distribution graph, clearly and intuitively comparing the changes in the operating performance of the same monitored object in different monitoring cycles, and uses views to manage and display the month-on-month comparison results of the performance indicator data, thereby increasing the diversity of data analysis. This visual display method greatly simplifies the data analysis process, allowing operation and maintenance personnel to more efficiently grasp the equipment operation status, respond in a timely manner, and make optimized decisions.

[0081] Figure 4 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 1 ,like Figure 4 As shown in the figure, taking the monitoring period as one week and the performance indicators of the storage device including IOPS as an example, the change trend of the average value of the performance indicator IOPS of the storage device for five consecutive weeks from January 1 to February 5 is shown. Each curve from curve a to curve e represents the average performance change trend of the performance indicator IOPS of the storage device within one week. The horizontal axis represents the monitoring period, and the vertical axis represents the average value of the performance indicator IOPS of the storage device.

[0082] Figure 4 In the figure, curve a represents the change trend of the average value of the performance indicator IOPS of the storage device every day from January 1 to January 7; curve b represents the change trend of the average value of the performance indicator IOPS of the storage device every day from January 8 to January 14; curve c represents the change trend of the average value of the performance indicator IOPS of the storage device every day from January 15 to January 21; curve d represents the change trend of the average value of the performance indicator IOPS of the storage device every day from January 22 to January 28; curve e represents the change trend of the average value of the performance indicator IOPS of the storage device every day from January 29 to February 4. Curves a to e can be overlapped and displayed in the same distribution diagram, but are not limited to, to show the change trend of the average value of the performance indicator IOPS of the storage device in different monitoring periods.

[0083] Figure 5 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 2 ,like Figure 5 As shown in the figure, taking the monitoring period as one week and the performance indicators of the storage device including bandwidth as an example, the change trend of the average value of the performance indicator bandwidth of the storage device for five consecutive weeks from January 1 to February 4 is shown. Each curve from curve a to curve e represents the average performance change trend of the performance indicator bandwidth of the storage device within one week. The horizontal axis represents the monitoring period, and the vertical axis represents the average value of the performance indicator bandwidth of the storage device.

[0084] Figure 5 In the figure, curve a represents the change trend of the average value of the performance index bandwidth of the storage device every day from January 1 to January 7; curve b represents the change trend of the average value of the performance index bandwidth of the storage device every day from January 8 to January 14; curve c represents the change trend of the average value of the performance index bandwidth of the storage device every day from January 15 to January 21; curve d represents the change trend of the average value of the performance index bandwidth of the storage device every day from January 22 to January 28; curve e represents the change trend of the average value of the performance index bandwidth of the storage device every day from January 29 to February 4. Curves a to e can be overlapped and displayed in the same distribution diagram, but are not limited to, to show the change trend of the average operating performance of the performance index bandwidth of the storage device in different monitoring periods.

[0085] Figure 6 is an optional weekly fixed-period distribution according to an embodiment of the present application Figure 3 ,like Figure 6 As shown in the figure, taking the monitoring period of one week and the performance indicators of the storage device including latency as an example, the change trend of the average value of the performance indicator latency of the storage device for five consecutive weeks from January 1 to February 5 is shown. Each curve from curve a to curve e represents the average performance change trend of the performance indicator latency of the storage device within one week. The horizontal axis represents the monitoring period, and the vertical axis represents the average value of the performance indicator latency of the storage device.

[0086] Figure 6 In the figure, curve a represents the changing trend of the average value of the performance index latency of the storage device every day from January 1 to January 7; curve b represents the changing trend of the average value of the performance index latency of the storage device every day from January 8 to January 14; curve c represents the changing trend of the average value of the performance index latency of the storage device every day from January 15 to January 21; curve d represents the changing trend of the average value of the performance index latency of the storage device every day from January 22 to January 28; curve e represents the changing trend of the average value of the performance index latency of the storage device every day from January 29 to February 4. Curves a to e can be overlapped and displayed in the same distribution diagram, but are not limited to, to show the changing trend of the average value of the performance index latency of the storage device in different monitoring periods.

[0087] Under normal operating conditions, the performance indicators of storage devices, such as IOPS, bandwidth, latency, etc., usually remain in a relatively stable state. This stability is reflected in the small fluctuation range of performance data. The data at various time points within the month-on-month cycle (i.e., compared with the previous same cycle) does not change much, and the data points may show a high degree of overlap in the distribution graph. However, under abnormal circumstances, such as hardware failures, software errors, excessive loads, network problems, etc., the performance indicators of storage devices may fluctuate significantly. For example, the performance data at various time points within the month-on-month cycle may jump or decline significantly, resulting in obvious peaks or troughs in the distribution graph. Figure 4 Data point f1 in Figure 5 Data point f2 in Figure 6 As shown in data point f3 in the figure, an obvious peak is formed in the distribution diagram, which means that on Tuesday corresponding to the monitoring period from January 1 to January 7, the operation of the storage device may be abnormal and needs further confirmation.

[0088] It should be noted that in Figure 4 , Figure 5 and Figure 6 In this paper, we only take the obvious peak in the distribution graph as an example. Figure 4 Data point f1 in Figure 5 Data point f2 in Figure 6 The data point f3 in may also include, but is not limited to, being significantly lower than other data points.

[0089] In an exemplary embodiment, after converting multiple pieces of the second performance change information into a distribution graph, it is possible but not limited to the following method: calculating the candidate operating parameters of the target storage device at each monitoring time in the monitoring period according to the distribution graph; searching for the largest target operating parameter from the candidate operating parameters; detecting whether the ring ratio of the target storage device at the target monitoring time corresponding to the target operating parameter is greater than or equal to a ring ratio threshold; and generating an alarm message when it is detected that the ring ratio is greater than or equal to the ring ratio threshold, wherein the alarm message is used to prompt that the operating performance of the target storage device at the target monitoring time is abnormal.

[0090] Optionally, in this embodiment, the ring ratio threshold corresponding to each monitoring time within the monitoring period can be determined in the following manner but is not limited to: the ring ratio threshold is determined as a value obtained by performing a sum operation on an average value of a group of ring ratios corresponding to the monitoring time within the monitoring period and a target product value, wherein the target product value is equal to a value obtained by performing a product operation on a standard deviation of a group of ring ratios and a proportional coefficient.

[0091] For example, the ring ratio threshold may include, but is not limited to, μ+3o', where μ is an average value of a group of ring ratios corresponding to the monitoring time in the monitoring period, and o' is a standard deviation of the group of ring ratios.

[0092] Optionally, in this embodiment, the candidate operating parameters may include, but are not limited to, a standard deviation of each group of ring ratios.

[0093] Optionally, in this embodiment, the target monitoring time can be obtained in the following manner but is not limited to: filtering out the maximum standard deviation (for example, Monday, etc.) from the standard deviations calculated at all time points in the same cycle; from the monitoring time corresponding to the maximum standard deviation (for example, this Monday, last Monday, the Monday before last, etc.), filtering out the time point (for example, this Monday) whose ring ratio is greater than or equal to the set ring ratio threshold as the target monitoring time.

[0094] In an exemplary embodiment, the candidate operating parameters of the target storage device at each monitoring time in the monitoring period can be calculated according to the distribution graph in the following manner, but is not limited to: by executing the following steps, according to the distribution graph, calculating the s-th operating parameter of the target storage device at the s-th monitoring time, wherein the candidate operating parameter includes the s-th operating parameter, the reference time period is divided into P monitoring periods, each monitoring period in the P monitoring periods includes R monitoring times, the R monitoring times include the s-th monitoring time, P is a positive integer, R is a positive integer, and s is a positive integer less than or equal to R: extracting the target storage device at the s-th monitoring time from the distribution graph the sth group of average operating performance of the target storage device at the sth monitoring time, wherein the wth average operating performance in the sth group of average operating performance is the average operating performance of the target storage device at the sth monitoring time in the wth monitoring cycle in the P monitoring cycles, and w is a positive integer less than or equal to s; calculating the sth group of ring ratios according to the sth group of average operating performance, and performing an average value operation on the sth group of ring ratios to obtain the sth average ring ratio; calculating the sth standard deviation corresponding to the sth monitoring time according to the sth average ring ratio and the sth group of ring ratios, wherein the sth operating parameter includes the sth standard deviation.

[0095] Optionally, in this embodiment, the number of candidate operating parameters may include but is not limited to P*R.

[0096] In an exemplary embodiment, the sth group of ring ratios may be calculated according to the sth group of average operating performance, and an average value operation is performed on the sth group of ring ratios to obtain an sth average ring ratio: the rth ring ratio in the sth group of ring ratios is calculated by the following formula, where r is a positive integer less than or equal to s:

[0097]

[0098] Among them, q r is the rth ring ratio, t r is the rth average running performance in the sth group of average running performances, t r+1 is the r+1th average operating performance in the sth group of average operating performances, the rth average operating performance is the average operating performance of the target storage device at the sth monitoring time in the rth monitoring cycle in the P monitoring cycles, and the r+1th average operating performance is the average operating performance of the target storage device at the sth monitoring time in the r+1th monitoring cycle in the P monitoring cycles; according to the sth average ring ratio and the sth group of ring ratios, calculate the sth standard deviation corresponding to the sth monitoring time, wherein the sth operating parameter includes the sth standard deviation: when the sth group of ring ratios includes P ring ratios, the sth standard deviation is calculated by the following formula:

[0099]

[0100] Among them, o' s is the sth standard deviation, qr t is the tth ring ratio in the sth group of ring ratios, and μ is the sth average ring ratio.

[0101] Optionally, in this embodiment, for the fixed-cycle ring ratio analysis and calculation, the monitoring device has built-in three types of ring ratio views of the cycle, and through a specific algorithm process, the standard deviation of the ring ratio of each time point in the cycle in the three ring ratio views is calculated and analyzed respectively, and then the time point with the highest dispersion among all time points is compared, and then it is determined whether the ring ratio of the point has a ring ratio exceeding (μ+3o'). If so, it is considered that the time point corresponding to the ring ratio may be abnormal. The calculation process of the standard deviation of the ring ratio at a time point can firstly, but is not limited to, calculate the ring ratio qr corresponding to each time period at each time point (equivalent to the rth ring ratio in the sth group of ring ratios) through the following formula (1):

[0102]

[0103] Among them, q r is the ratio of the rth ring, t r is the rth average performance of the sth group of average performance, t r+1 is the r+1th average performance of the sth group of average performance. Calculate the ring ratio of each time period in the cycle at a time point from recent to far according to the date [q r0 ,q r1,q r2 ...], thereby obtaining a group of ring ratio values ​​at a time point, and calculating the average value of this group of ring ratios, the sth standard deviation o' corresponding to the sth monitoring time can be calculated by, but not limited to, the following formula (2): s :

[0104]

[0105] Among them, o' s is the sth standard deviation corresponding to the sth monitoring time, qr t is the tth ring ratio in the sth group of ring ratios, μ is the sth average ring ratio, and a group of ring ratios includes P ring ratios. After calculating the standard deviation of the ring ratio at each time point in the cycle, find the maximum value of these standard deviations, and check whether the ring ratio in the most recent time period in the group of ring ratios at the time point of the maximum value exceeds the ring ratio threshold. If it exceeds, it may be an abnormal risk point, and prompt the user.

[0106] By analyzing the fixed-period year-on-year value view and using a specific algorithm process, the year-on-year standard deviation of the indicator data at the same time point is calculated, and the time point with the largest standard deviation within each time point is counted. Based on the year-on-year threshold (for example, μ+3o'), the time point in the most recent period where the year-on-year ratio exceeds the threshold is analyzed as a possible abnormal risk time point, thereby achieving a scientific and reasonable judgment on whether there is a possible abnormal time point for performance indicator changes and reducing the cost of manual analysis.

[0107] In the technical solution provided in the above step S210, after the distribution graph is displayed on the target operation interface, the performance changes of the target storage device within the monitoring period can be analyzed based on the distribution graph, but is not limited to it. Specifically, by calculating the candidate operating parameters at each monitoring time (which can include but is not limited to the average ring ratio and standard deviation), possible abnormal operating performance indicators are identified. When it is detected that the ring ratio at a certain monitoring time within a certain monitoring period exceeds the preset ring ratio threshold, the monitoring device will generate an alarm message.

[0108] Figure 7 is a functional schematic diagram of an optional performance indicator data comparison and analysis module based on a month-on-month basis according to an embodiment of the present application, such as Figure 7As shown, an optional month-on-month performance indicator data comparison and analysis module is divided into three functional parts as a whole, which can include but is not limited to: realizing month-on-month value presentation of performance indicator data with a fixed period; realizing month-on-month value presentation of performance indicator data with a custom period; realizing month-on-month ratio calculation of performance indicator data with a fixed period and analyzing operation and maintenance suggestions. The implementation of the query of the performance of the entire storage device requires that the monitoring device has collected performance indicator data for a period of time to ensure the data basis for comparative analysis. The month-on-month value presentation is realized by creating a distribution chart. Taking the month-on-month view as an example, the user needs to create the month-on-month view he needs in the month-on-month analysis page of the monitoring device to display the month-on-month value view of the performance indicator data.

[0109] In order to better understand the query process of the storage device performance query method in the embodiment of the present application, the query process of the storage device performance query method in the embodiment of the present application is explained and illustrated in combination with optional embodiments below, which can be applicable to but not limited to the embodiment of the present application.

[0110] The query process of the query method for querying storage device performance may include but is not limited to the following: creating a fixed-cycle year-on-year value view (equivalent to a distribution chart), automatically constructing the same object according to built-in specifications by specifying different cycle types, and generating a performance indicator data comparison chart for different time periods within the same cycle; or creating a custom cycle year-on-year value view, automatically generating time periods with the same time span forward and backward through the selected custom time period for user selection; after the selection is completed, the monitoring device displays the performance indicator data comparison chart through the custom time period set by the view (equivalent to the target operation interface) and other selected time periods with the same time span; in the case of choosing to create a fixed-cycle year-on-year value view, the monitoring device regularly checks the three built-in fixed-cycle year-on-year value views every day, generates year-on-year value comparison data according to the fixed-cycle year-on-year value view, and then analyzes the performance indicator data according to a specific algorithm process. The algorithm process is to first calculate the ring ratio of each time point in the cycle in different time periods, then calculate the standard deviation of the ring ratio of each time point in the cycle, and then count the time point with the largest standard deviation among all time points in the cycle, and then determine whether there is an abnormal analysis time in the most recent time period of this time point based on whether the ring ratio exceeds the threshold (for example, μ+3o').

[0111] Optionally, the specific implementation process of the method for querying storage device performance in the embodiment of the present application may include but is not limited to the following steps:

[0112] (1) Performance indicator data of storage devices has been collected for a period of time in the system (which may be but is not limited to being deployed in monitoring devices);

[0113] (2) On the front page of the system (equivalent to the target operation interface), choose to create a fixed-cycle year-on-year value view (equivalent to a fixed-cycle distribution chart), select the monitoring cycle type (day, week, month), monitoring object, and performance indicator, and then submit and save it to the monitoring device.

[0114] (3) In the month-on-month value view list, select the corresponding newly created fixed-period month-on-month value view and select View. The monitoring device backend obtains the time range timeRange, data granularity interval, and period start and end time [start, end] of the corresponding period type according to the month-on-month value view configuration; reads the performance indicator data within the time range timeRange from the database, and then calculates the average value of the performance indicator data within each granularity according to the data granularity interval; groups the data according to the period start and end time [start, end] to obtain multiple groups of performance indicator data of the same period but different time periods, for example: data from January 1 to January 7 [(Monday, 110), (Tuesday, 100), (Wednesday, 110), (Thursday, 100), (Friday, 120), (Saturday, 130), (Sunday, 120)], etc. The monitoring device frontend presents the obtained multiple groups of performance indicator data of the same period in the same distribution graph, so that the performance indicator values ​​of different time periods at different time points (Monday, Tuesday, etc.) can be clearly identified.

[0115] (4) On the system front page, choose to create a custom period-to-period value view, and then customize and select any time range (equivalent to the target time period); the back end calculates the start time, end time, and time span based on the selected time range, and calculates all time periods with the same time span (equivalent to candidate time periods) from the start of performance indicator data collection to the current time based on the start time forward and the end time backward, and returns them to the front end page for user selection; the user selects the comparison time period (equivalent to the reference time period), monitoring object, and performance indicator that need to be paid attention to from these time periods as needed; then submits and saves them to the monitoring device.

[0116] (5) In the month-on-month value view list, select the corresponding newly created custom period month-on-month value view, and select View. The monitoring device backend will query the performance indicator data in the selected custom comparison time period in turn according to the month-on-month value view configuration, and calculate the average value of the performance indicator data in each granularity (equivalent to the average performance change information) according to the built-in data granularity of the monitoring device, and then return multiple groups of performance indicator data in different time periods of the same cycle according to the time periods selected in the view. For example: If the current time is 10:00 on January 2, and the user-defined time period is from 0:00 to 6:00 on January 2, the time periods that can be selected are: ... 0:00 to 6:00 on January 1, 6:00 to 12:00 on January 1, 12:00 to 18:00 on January 1, 18:00 to 24:00 on January 1, 6:00 to 12:00 on January 2, etc. The month-on-month time periods selected by the user are: 18:00 to 24:00 on January 1, 6:00 to 12:00 on January 2; the performance indicator data finally returned to the front-end page is The energy index data are as follows: 18:00 to 24:00 on January 1: [(18:01, 110), (18:02, 100), (18:03, 110)...], 0:00 to 6:00 on January 2: [(0:01, 110), (0:02, 100), (0:03, 110)...], 6:00 to 12:00 on January 2: [(6:01, 110), (6:02, 100), (6:03, 110)...].

[0117] (6) The system can be triggered to perform fixed monitoring period ratio calculation on a daily basis (or every half day or every hour, etc., this application does not impose any restrictions on this), analyze the ratio value views of different period types (day, week, month) in turn, analyze the abnormal risk time points, and provide feedback to users for suggestions.

[0118] (7) The system calculates and queries the YoY value view data based on the built-in cycle type YoY value view, and multiple groups of performance indicator data in the same cycle but different time periods, such as: data from January 1 to January 7 [(Monday, 110), (Tuesday, 100), (Wednesday, 110), (Thursday, 100), (Friday, 120), (Saturday, 130), (Sunday, 120)], ... January 29 to February 4, etc. Then, according to a specific algorithm, the ring ratios of different time periods at each time point are calculated (such as Monday: [0.25, 0.16, 0.19...], etc.), and then according to the standard deviation algorithm, the standard deviation of each time point is calculated (such as Monday: 3.13, Tuesday: 0.62... etc.); the maximum value of the standard deviation of the ring ratios at these time points is counted (such as Monday: 3.13), and according to the abnormal risk point threshold (μ+3o'), the ring ratios that exceed the threshold in the most recent time period at the time point are checked (such as the ring ratio from January 29 to Monday, February 4), and the time in the time period corresponding to the ring ratio is used as the abnormal risk time point (January 29 to Monday, February 4), and fed back to the user.

[0119] Through the embodiments of the present application, it supports the creation of a month-on-month value view in a fixed period, which is simple and convenient, and can quickly realize the comparison of performance indicator values ​​of the same object in different time periods; it also supports the creation of a month-on-month view in a custom time period, which is more flexible and convenient for users to display the month-on-month value comparison chart of the same object in any historical time period as needed, and the application creation is more extensive.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0121] In the present embodiment, a device for querying the performance of a storage device is also provided, and the device is used to implement the above-mentioned embodiment and preferred implementation mode, and the descriptions that have been made are not repeated here. As used below, the term "module" can implement a combination of software and / or hardware of a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware is also possible and conceivable.

[0122] Figure 8is a structural block diagram of a storage device performance query device according to an embodiment of the present application, which is applied to a monitoring device, the monitoring device is used to connect to the storage device, and a target operation interface is deployed on the monitoring device, such as Figure 8 As shown, the device comprises:

[0123] A first detection module 802 is used to detect a target query request triggered on the target operation interface, wherein the target query request is used to request to query the distribution of the operating performance of the connected target storage device under the target monitoring item;

[0124] A collection module 804 is used to respond to the target query request and collect first performance change information within a reference time period from the target storage device, wherein the first performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item within the reference time period;

[0125] A partitioning module 806 is used to perform a partitioning operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item during the monitoring period;

[0126] A conversion module 808, configured to convert the plurality of the second performance change information into a distribution graph, wherein the distribution graph is used to show how the operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time, and the monitoring cycle includes a plurality of the monitoring times;

[0127] The display module 810 is used to display the distribution graph on the target operation interface.

[0128] Through the above device, a visual operation interface is provided on the monitoring device, and the user can query the distribution of the operating performance of the specified storage device under the specified monitoring item through the visual operation interface. The monitoring device can, but is not limited to, divide the collected performance change information according to the monitoring period (such as one day, one week or one month), and convert the divided performance change information into a distribution map, so that the changes in the operating performance of the storage device in different monitoring periods are displayed in the distribution map in an overlapping manner. In this way, the user can intuitively query whether the operating performance in different monitoring periods is normal. Therefore, the problem of low query efficiency of storage device performance can be solved, and the effect of improving the query efficiency of storage device performance can be achieved.

[0129] In an exemplary embodiment, the acquisition module includes:

[0130] A detection unit, configured to detect whether the target query request carries a target time period, and obtain a detection result; a determination unit, configured to determine the reference time period according to the detection result;

[0131] A collection unit is used to collect the first performance change information within the reference time period from the target storage device.

[0132] In an exemplary embodiment, the determining unit is configured to:

[0133] In a case where the detection result indicates that the target query request does not carry the target time period, determining the reference time period as a first time difference between a first time and a current time, wherein the first time is earlier than the current time, and the current time is the time when the target query request is triggered on the target operation interface;

[0134] In a case where the detection result is used to indicate that the target query request carries the target time period, a candidate time period between a second time and the current time is obtained, wherein the second time is earlier than the current time, the second time is the recording time of the first performance change information allowed to be collected on the target storage device, the current time is the time when the target query request is triggered on the target operation interface, the candidate time period includes the target time period, the second time is earlier than the first time, the candidate time period is displayed on the target operation interface, and the time period in which the selection operation is performed in the candidate time period is determined as the reference time period.

[0135] In an exemplary embodiment, the partitioning module includes:

[0136] When the length of the reference time period is N and the monitoring length of the monitoring period is M, the first performance change information is divided according to the monitoring period to obtain P performance change information by performing the following steps, wherein the plurality of second performance change information includes the P performance change information, N is a positive integer, M is a positive integer, and P is a positive integer:

[0137] A first division unit, configured to divide the reference time period into P monitoring periods according to the monitoring duration of the monitoring period, wherein each monitoring duration in the P monitoring periods is M, and P is equal to a positive integer obtained by rounding up a value obtained by dividing N by M;

[0138] A second division unit is used to divide the first performance change information into P performance change information corresponding to the P monitoring periods, wherein the i-th performance change information among the P performance change information includes the performance change information corresponding to the i-th monitoring period in the first performance change information, the P monitoring periods include the i-th monitoring period, and i is a positive integer less than or equal to P.

[0139] In an exemplary embodiment, the conversion module includes:

[0140] A first conversion unit, configured to convert the plurality of the second performance change information into a plurality of average performance change information, wherein the average performance change information is used to indicate the distribution of the average operating performance of the target storage device under the target monitoring item at each of the monitoring times within each of the monitoring cycles;

[0141] The second conversion unit is used to convert the multiple pieces of average performance change information into the distribution graph.

[0142] In an exemplary embodiment, the first conversion unit is further configured to:

[0143] In the case where the plurality of the second performance change information include P performance change information, the following steps are performed to convert the j-th group of performance information of the i-th performance change information at the j-th monitoring time into the j-th average performance information, wherein the i-th average performance change information includes R average performance information obtained by converting R groups of performance information at R monitoring times in the i-th performance change information, the R average performance information includes the j-th average performance information, and the i-th performance change information includes the R average performance information of the target storage device within the i-th monitoring period. R groups of performance information under the target monitoring item at a monitoring time, the R groups of performance information include the j-th group of performance information, the multiple average performance change information include P average performance change information, the P average performance change information include the i-th average performance change information, the P performance change information corresponds to P monitoring periods, the P monitoring periods include the i-th monitoring period, P is a positive integer, j is a positive integer less than or equal to R, i is a positive integer less than or equal to P, and R is a positive integer: perform an average value operation on the j-th group of performance information to obtain the j-th average performance information;

[0144] The second conversion unit is also used to: when the multiple average performance change information include P average performance change information and the multiple monitoring times include R monitoring times, convert the P average performance change information into the distribution graph by executing the following steps: using the P performance change information to generate the distribution graph in a target coordinate system, wherein the horizontal axis of the target coordinate system represents the R monitoring times, and the vertical axis of the target coordinate system represents the average operating performance of the target storage device under the target monitoring item at the R monitoring times.

[0145] In an exemplary embodiment, the apparatus further comprises:

[0146] a calculation module, configured to calculate, after converting the plurality of the second performance change information into a distribution graph, candidate operating parameters of the target storage device at each monitoring time in the monitoring cycle according to the distribution graph;

[0147] A search module, used for searching the maximum target operating parameter from the candidate operating parameters;

[0148] A second detection module, used to detect whether the ring ratio of the target storage device at the target monitoring time corresponding to the target operating parameter is greater than or equal to a ring ratio threshold;

[0149] A generating module is used to generate an alarm message when it is detected that the ring ratio is greater than or equal to the ring ratio threshold, wherein the alarm message is used to prompt that the operating performance of the target storage device during the target monitoring time is abnormal.

[0150] In an exemplary embodiment, the computing module includes:

[0151] By executing the following steps, according to the distribution graph, the sth operating parameter of the target storage device at the sth monitoring time is calculated, wherein the candidate operating parameter includes the sth operating parameter, the reference time period is divided into P monitoring cycles, each monitoring cycle in the P monitoring cycles includes R monitoring times, the R monitoring times include the sth monitoring time, P is a positive integer, R is a positive integer, and s is a positive integer less than or equal to R:

[0152] an extracting unit, configured to extract, from the distribution graph, an s-th group of average operating performance of the target storage device at the s-th monitoring time, wherein the w-th average operating performance in the s-th group of average operating performance is the average operating performance of the target storage device at the s-th monitoring time in the w-th monitoring cycle in the P monitoring cycles, and w is a positive integer less than or equal to s;

[0153] a processing unit, configured to calculate the ring ratio of the sth group according to the average running performance of the sth group, and perform an average operation on the ring ratio of the sth group to obtain an sth average ring ratio;

[0154] A calculation unit is used to calculate the sth standard deviation corresponding to the sth monitoring time according to the sth average ring ratio and the sth group ring ratio, wherein the sth operating parameter includes the sth standard deviation.

[0155] In an exemplary embodiment, the processing unit is further configured to:

[0156] The rth ring ratio in the sth group of ring ratios is calculated by the following formula, where r is a positive integer less than or equal to s:

[0157]

[0158] Among them, q r is the rth ring ratio, t r is the rth average running performance in the sth group of average running performances, t r+1 is the r+1th average operating performance in the sth group of average operating performances, the rth average operating performance is the average operating performance of the target storage device at the sth monitoring time in the rth monitoring cycle in the P monitoring cycles, and the r+1th average operating performance is the average operating performance of the target storage device at the sth monitoring time in the r+1th monitoring cycle in the P monitoring cycles;

[0159] The calculation unit is further configured to: when the sth group of ring ratios includes P ring ratios, calculate the sth standard deviation by the following formula:

[0160]

[0161] Among them, o' s is the sth standard deviation, qr t is the tth ring ratio in the sth group of ring ratios, and μ is the sth average ring ratio.

[0162] It should be noted that the above modules can be implemented by software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0163] An embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0164] In an exemplary embodiment, the computer-readable storage medium may include, but is not limited to, various media that can store computer programs, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0165] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0166] In an exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0167] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0168] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0169] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0170] For specific examples in this embodiment, reference may be made to the examples described in the above embodiments and exemplary implementation modes, and this embodiment will not be described in detail herein.

[0171] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0172] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for querying storage device performance, characterized in that: Applied to a monitoring device, the monitoring device is used to connect to a storage device, and a target operation interface is deployed on the monitoring device. The method includes: Detecting a target query request triggered on the target operation interface, wherein the target query request is used to request a query on the distribution of the operating performance of the connected target storage device under the target monitoring item; In response to the target query request, first performance change information within a reference time period is collected from the target storage device, wherein the first performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item within the reference time period; Performing a division operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item during the monitoring period; Converting the plurality of the second performance change information into a distribution graph, wherein the distribution graph is used to show how the operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time, and the monitoring cycle includes the plurality of the monitoring times; The distribution graph is displayed on the target operation interface.

2. The method according to claim 1, characterized in that The collecting first performance change information within a reference time period from the target storage device includes: Detecting whether the target query request carries a target time period, and obtaining a detection result; Determining the reference time period according to the detection result; The first performance change information within the reference time period is collected from the target storage device.

3. The method according to claim 2, characterized in that The determining the reference time period according to the detection result includes: In a case where the detection result indicates that the target query request does not carry the target time period, determining the reference time period as a first time difference between a first time and a current time, wherein the first time is earlier than the current time, and the current time is the time when the target query request is triggered on the target operation interface; In a case where the detection result is used to indicate that the target query request carries the target time period, a candidate time period between a second time and the current time is obtained, wherein the second time is earlier than the current time, the second time is the recording time of the first performance change information allowed to be collected on the target storage device, the current time is the time when the target query request is triggered on the target operation interface, the candidate time period includes the target time period, the second time is earlier than the first time, the candidate time period is displayed on the target operation interface, and the time period in which the selection operation is performed in the candidate time period is determined as the reference time period.

4. The method according to claim 1, characterized in that: The performing a division operation on the first performance change information according to the monitoring period to obtain a plurality of second performance change information includes: when the length of the reference time period is N and the monitoring duration of the monitoring period is M, performing the following steps to perform a division operation on the first performance change information according to the monitoring period to obtain P pieces of performance change information, wherein the plurality of second performance change information includes the P pieces of performance change information, N is a positive integer, M is a positive integer, and P is a positive integer: According to the monitoring duration of the monitoring cycle, the reference time period is divided into P monitoring cycles, wherein each monitoring duration in the P monitoring cycles is M, and P is equal to a positive integer obtained by rounding up the value obtained by dividing N by M; The first performance change information is divided into P performance change information corresponding to the P monitoring periods, wherein the i-th performance change information among the P performance change information includes the performance change information corresponding to the i-th monitoring period in the first performance change information, the P monitoring periods include the i-th monitoring period, and i is a positive integer less than or equal to P.

5. The method according to claim 1, characterized in that: The converting the plurality of the second performance change information into a distribution graph comprises: Converting the plurality of the second performance change information into a plurality of average performance change information, wherein the average performance change information is used to indicate the distribution of the average operating performance of the target storage device under the target monitoring item at each of the monitoring times within each of the monitoring cycles; The plurality of average performance variation information are converted into the distribution graph.

6. The method according to claim 5, characterized in that The converting the plurality of the second performance change information into a plurality of average performance change information comprises: In the case where the plurality of the second performance change information include P performance change information, the following steps are performed to convert the j-th group of performance information of the i-th performance change information at the j-th monitoring time into the j-th average performance information, wherein the i-th average performance change information includes R average performance information obtained by converting R groups of performance information at R monitoring times in the i-th performance change information, the R average performance information includes the j-th average performance information, and the i-th performance change information includes the R average performance information of the target storage device within the i-th monitoring period. R groups of performance information under the target monitoring item at a monitoring time, the R groups of performance information include the j-th group of performance information, the multiple average performance change information include P average performance change information, the P average performance change information include the i-th average performance change information, the P performance change information corresponds to P monitoring periods, the P monitoring periods include the i-th monitoring period, P is a positive integer, j is a positive integer less than or equal to R, i is a positive integer less than or equal to P, and R is a positive integer: perform an average value operation on the j-th group of performance information to obtain the j-th average performance information; The converting of the multiple average performance change information into the distribution graph includes: when the multiple average performance change information includes P average performance change information and the multiple monitoring times include R monitoring times, converting the P average performance change information into the distribution graph by executing the following steps: using the P performance change information to generate the distribution graph in a target coordinate system, wherein the abscissa of the target coordinate system represents the R monitoring times, and the ordinate of the target coordinate system represents the average operating performance of the target storage device under the target monitoring item at the R monitoring times.

7. The method according to claim 1, characterized in that After converting the plurality of second performance change information into a distribution graph, the method further includes: Calculating candidate operating parameters of the target storage device at each monitoring time in the monitoring cycle according to the distribution graph; Finding the maximum target operating parameter from the candidate operating parameters; Detecting whether a ring ratio of the target storage device at a target monitoring time corresponding to the target operating parameter is greater than or equal to a ring ratio threshold; When it is detected that the ring ratio is greater than or equal to the ring ratio threshold, an alarm message is generated, wherein the alarm message is used to prompt that the operating performance of the target storage device during the target monitoring time is abnormal.

8. The method according to claim 7, characterized in that The step of calculating the candidate operating parameters of the target storage device at each monitoring time in the monitoring period according to the distribution graph includes: By executing the following steps, according to the distribution graph, the sth operating parameter of the target storage device at the sth monitoring time is calculated, wherein the candidate operating parameter includes the sth operating parameter, the reference time period is divided into P monitoring cycles, each monitoring cycle in the P monitoring cycles includes R monitoring times, the R monitoring times include the sth monitoring time, P is a positive integer, R is a positive integer, and s is a positive integer less than or equal to R: Extracting the s-th group of average operating performance of the target storage device at the s-th monitoring time from the distribution graph, wherein the w-th average operating performance in the s-th group of average operating performance is the average operating performance of the target storage device at the s-th monitoring time in the w-th monitoring cycle in the P monitoring cycles, and w is a positive integer less than or equal to s; Calculating the ring ratio of the sth group according to the average running performance of the sth group, and performing an average operation on the ring ratio of the sth group to obtain an sth average ring ratio; The sth standard deviation corresponding to the sth monitoring time is calculated according to the sth average ring ratio and the sth group ring ratio, wherein the sth operating parameter includes the sth standard deviation.

9. The method according to claim 8, characterized in that The step of calculating the ring ratio of the sth group according to the average operating performance of the sth group, and performing an average value operation on the ring ratio of the sth group to obtain an sth average ring ratio includes: calculating the rth ring ratio of the ring ratio of the sth group by the following formula, wherein r is a positive integer less than or equal to s: Among them, q r is the rth ring ratio, t r is the rth average running performance in the sth group of average running performances, t r+1 is the r+1th average operating performance in the sth group of average operating performances, the rth average operating performance is the average operating performance of the target storage device at the sth monitoring time in the rth monitoring cycle in the P monitoring cycles, and the r+1th average operating performance is the average operating performance of the target storage device at the sth monitoring time in the r+1th monitoring cycle in the P monitoring cycles; The calculating, according to the sth average ring ratio and the sth group of ring ratios, the sth standard deviation corresponding to the sth monitoring time, wherein the sth operating parameter includes the sth standard deviation, comprises: when the sth group of ring ratios includes P ring ratios, calculating the sth standard deviation by the following formula: Among them, o' s is the sth standard deviation, qr t is the tth ring ratio in the sth group of ring ratios, and μ is the sth average ring ratio.

10. A storage device performance query device, characterized in that: Applied to a monitoring device, the monitoring device is used to connect to a storage device, a target operation interface is deployed on the monitoring device, and the device includes: A first detection module is used to detect a target query request triggered on the target operation interface, wherein the target query request is used to request to query the distribution of the operating performance of the connected target storage device under the target monitoring item; A collection module, configured to respond to the target query request and collect first performance change information within a reference time period from the target storage device, wherein the first performance change information is used to indicate the distribution of the operating performance of the target storage device under the target monitoring item within the reference time period; a partitioning module, configured to perform a partitioning operation on the first performance change information according to a monitoring period to obtain a plurality of second performance change information, wherein the second performance change information is used to indicate a distribution of the operating performance of the target storage device under the target monitoring item during the monitoring period; a conversion module, configured to convert the plurality of the second performance change information into a distribution graph, wherein the distribution graph is used to show how the operating performance of the target storage device under the target monitoring item at each monitoring time changes with the monitoring time, and the monitoring cycle includes the plurality of the monitoring times; A display module is used to display the distribution graph on the target operation interface.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program implements the steps of the method described in any one of claims 1 to 9 when executed by a processor.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method described in any one of claims 1 to 9 are implemented.

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