Multi-host link hotspot analysis method and system, computer device and storage medium

Abstract

The application discloses a multi-host link hotspot analysis method and system, computer equipment and a storage medium, and relates to the technical field of information processing. The method comprises the following steps: according to an input IP set, querying link information from each host in a SAN storage network to a storage, so as to form a target data set of the SAN storage network; using an Eclat algorithm to mine the target data set, and finding a maximum frequent item set of the target data set; and based on the maximum frequent item set, analyzing to obtain a hotspot of the SAN storage network. The multi-host link hotspot analysis method can realize fast and accurate positioning of the hotspot in the SAN storage network.

Description

Technical Field

[0001] This invention relates to the field of information processing technology, specifically to a method, system, computer device, and non-volatile computer-readable storage medium for multi-host link hotspot analysis. Background Technology

[0002] Currently, with the development of computer technology, more and more technologies are being applied in the financial field. The traditional financial industry is gradually transforming into financial technology (Fintech), and link hotspot analysis technology is no exception. However, due to the security and real-time requirements of the financial industry, higher requirements are also placed on link hotspot analysis technology.

[0003] SAN storage is a centralized storage system that connects to various hosts via SAN switches. A typical SAN storage network consists of multiple storage devices, multiple SAN switches, and hundreds of hosts. There are several hotspots in the entire storage network, such as switches and storage devices. Components can be further broken down into switch cards, switch ports, storage controllers, storage front-end ports, and so on.

[0004] Currently, in the banking system, when a single application fails, the anomaly can be identified by querying the link information from the host to the switch to the storage and the health information of each component along the link. However, when a large number of applications fail simultaneously, querying the link information for each host is time-consuming, and given the complex link information, pinpointing the fault location relies entirely on the engineer's experience, making rapid fault localization quite difficult. Furthermore, among a large number of host anomaly alarms, some alarms may not be caused by link anomalies, which can interfere with troubleshooting, making it more difficult and time-consuming.

[0005] Currently, the main troubleshooting method for such multi-application anomalies in banking structures is to collect the link information from the host to the storage of the abnormal application, and then manually compare and visually inspect each node on the link to see if there is any anomaly. Usually, without obvious device alarms, it takes at least ten minutes to check the anomalies of 10 hosts. As the number of hosts increases, the difficulty and time consumption of troubleshooting will also increase. If the number of hosts is very large, in most cases only the link information of some hosts can be randomly checked for troubleshooting. On the one hand, it relies heavily on the experience of engineers, and on the other hand, the accuracy of the judgment is relatively low.

[0006] In summary, how to provide a method, system, computer device, and non-volatile computer-readable storage medium for multi-host link hotspot analysis to achieve rapid and accurate hotspot location in SAN storage networks is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0007] In view of the shortcomings of the prior art, the purpose of this invention is to provide a method, system, computer device and non-volatile computer-readable storage medium for multi-host link hotspot analysis that can be used in financial technology or other related fields, aiming to achieve rapid and accurate location of hotspots in SAN storage networks.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] A method for analyzing multi-host link hotspots, comprising:

[0010] Based on the input IP set, query the link information from each host to the storage in the SAN storage network to form the target dataset of the SAN storage network.

[0011] The Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset;

[0012] Based on the largest frequent itemset, the hotspots of the SAN storage network are analyzed.

[0013] In a further technical solution, the multi-host link hotspot analysis method, wherein the step of querying the link information from each host to the storage within the SAN storage network based on the input IP set to form the target dataset of the SAN storage network includes:

[0014] Based on the input set of IPs, query the link information from each host to the storage in the SAN storage network;

[0015] Based on the link information, a record number is set to obtain the corresponding link information dataset;

[0016] Based on the link information dataset, a target dataset is formed to constitute the SAN storage network.

[0017] In a further technical solution, the multi-host link hotspot analysis method, wherein the step of using the Eclat algorithm to mine the target dataset and find the largest frequent itemset of the target dataset includes:

[0018] The minimum support level of the link information dataset is preset;

[0019] Based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset.

[0020] In a further technical solution, the multi-host link hotspot analysis method includes querying the link information from each host to the storage in the SAN storage network based on the input IP set. The link information includes the host's cluster information, physical machine information, and component information.

[0021] In a further technical solution, the multi-host link hotspot analysis method includes, in this case, the component information including the switch, switch port, and switch board connected to the host, as well as the storage, storage port, storage controller, and switch port connected to the storage port configured on the host.

[0022] In a further technical solution, the multi-host link hotspot analysis method includes a pre-set minimum support for the link information dataset, wherein the minimum support is half the number of IPs in the IP set.

[0023] In a further technical solution, the multi-host link hotspot analysis method, wherein the step of analyzing the hotspots of the SAN storage network based on the maximum frequent itemset includes:

[0024] Examine the relevant information of the hotspot in detail and determine whether the hotspot is an anomaly.

[0025] A multi-host link hotspot analysis system, comprising:

[0026] The query module is used to query the link information from each host to the storage in the SAN storage network based on the input IP set, so as to form the target dataset of the SAN storage network.

[0027] The mining module is used to mine the target dataset using the Eclat algorithm to find the largest frequent itemset in the target dataset;

[0028] The analysis module is used to analyze and obtain the hotspots of the SAN storage network based on the largest frequent itemset.

[0029] A computer device, wherein the computer device includes at least one processor; and,

[0030] A memory communicatively connected to the at least one processor; wherein,

[0031] The memory stores a computer program that can be executed by the at least one processor. When the computer program is executed by the at least one processor, it can implement the multi-host link hotspot analysis method as described above.

[0032] A non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, which, when executed by at least one processor, can implement the multi-host link hotspot analysis method as described in any of the preceding claims.

[0033] Compared to existing technologies, this invention provides a method, system, computer device, and non-volatile computer-readable storage medium for multi-host link hotspot analysis. The method includes: querying link information from each host to storage within a SAN storage network based on an input IP set to form a target dataset for the SAN storage network; mining the target dataset using the Eclat algorithm to find the largest frequent itemset; and analyzing the hotspots of the SAN storage network based on the largest frequent itemset. This multi-host link hotspot analysis method enables rapid and accurate location of hotspots in a SAN storage network. Attached Figure Description

[0034] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating a multi-host link hotspot analysis method provided in an embodiment of the present invention.

[0036] Figure 2 for Figure 1 A flowchart illustrating step S100 as described above.

[0037] Figure 3 for Figure 1 A flowchart illustrating step S200 described above.

[0038] Figure 4 This is a schematic diagram of the functional modules of a multi-host link hotspot analysis system provided in an embodiment of the present invention.

[0039] Figure 5 This is a schematic diagram of the hardware structure of the computer device provided in an embodiment of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0041] In the description of this invention, the terms "comprising," "including," "having," and "containing" are all open-ended terms, meaning that they include but are not limited to. The terms "one embodiment," "one specific embodiment," "some embodiments," and "for example," etc., refer to specific features, structures, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. The order of steps involved in the various embodiments is used to illustrate the implementation of this application, and the order of steps is not limited and can be adjusted appropriately as needed.

[0042] Various non-limiting embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0043] Currently, with the development of computer technology, more and more technologies are being applied in the financial field. The traditional financial industry is gradually transforming into financial technology (Fintech), and link hotspot analysis technology is no exception. However, due to the security and real-time requirements of the financial industry, higher requirements are also placed on link hotspot analysis technology.

[0044] SAN storage is a centralized storage system that connects to various hosts via SAN switches. A typical SAN storage network consists of multiple storage devices, multiple SAN switches, and hundreds of hosts. There are several hotspots in the entire storage network, such as switches and storage devices. Components can be further broken down into switch cards, switch ports, storage controllers, storage front-end ports, and so on.

[0045] Currently, in the banking system, when a single application fails, the anomaly can be identified by querying the link information from the host to the switch to the storage and the health information of each component along the link. However, when a large number of applications fail simultaneously, querying the link information for each host is time-consuming, and given the complex link information, pinpointing the fault location relies entirely on the engineer's experience, making rapid fault localization quite difficult. Furthermore, among a large number of host anomaly alarms, some alarms may not be caused by link anomalies, which can interfere with troubleshooting, making it more difficult and time-consuming.

[0046] Currently, the main troubleshooting method for such multi-application anomalies in banking structures is to collect the link information from the host to the storage of the abnormal application, and then manually compare and visually inspect each node on the link to see if there is any anomaly. Usually, without obvious device alarms, it takes at least ten minutes to check the anomalies of 10 hosts. As the number of hosts increases, the difficulty and time consumption of troubleshooting will also increase. If the number of hosts is very large, in most cases only the link information of some hosts can be randomly checked for troubleshooting. On the one hand, it relies heavily on the experience of engineers, and on the other hand, the accuracy of the judgment is relatively low.

[0047] In summary, how to provide a method, system, computer device, and non-volatile computer-readable storage medium for multi-host link hotspot analysis to achieve rapid and accurate hotspot location in SAN storage networks is a problem that urgently needs to be solved by those skilled in the art.

[0048] Therefore, to resolve the above issues, please refer to [link / reference]. Figure 1 This invention provides a method for multi-host link hotspot analysis, wherein the method includes the following steps:

[0049] S100. Based on the input IP set, query the link information from each host to the storage in the SAN storage network to form the target dataset of the SAN storage network.

[0050] S200. Use the Eclat algorithm to mine the target dataset and find the largest frequent itemset in the target dataset;

[0051] S300. Based on the largest frequent itemset, the hotspots of the SAN storage network are analyzed and obtained.

[0052] Further, please refer to Figure 2 The multi-host link hotspot analysis method, wherein step S100, querying the link information from each host to the storage within the SAN storage network based on the input IP set to form the target dataset of the SAN storage network, includes the following steps:

[0053] S101. Based on the input IP set, query the link information from each host to the storage in the SAN storage network;

[0054] S102. Based on the link information, set a record number to obtain the corresponding link information dataset;

[0055] S103. Based on the link information dataset, form the target dataset for the SAN storage network.

[0056] In specific implementation, in this embodiment, based on the input IP set (IP address string), the management tool queries the link information from each host to the storage in the SAN storage network; each IP address forms a link record, and based on the link information, a record number is set to obtain the corresponding link information dataset; then, based on the link information dataset, the target dataset of the SAN storage network is formed.

[0057] The link information includes the host's cluster information, physical machine information, and component information; further, the component information includes the switches, switch ports, and switch cards connected to the host, as well as the physical components on the link such as the storage, storage ports, storage controllers, and switch ports connected to the storage ports configured on the host.

[0058] Further, please refer to Figure 3 The multi-host link hotspot analysis method, wherein step S200, using the Eclat algorithm to mine the target dataset and find the largest frequent itemset of the target dataset, includes the following steps:

[0059] S201. Pre-set the minimum support level of the link information dataset;

[0060] S202. Based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset.

[0061] In this specific implementation, firstly, the minimum support of the link information dataset is preset, wherein the minimum support is half the number of IPs in the IP set. Setting this parameter can speed up the iteration process of the Eclat algorithm. Secondly, based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset of the target dataset.

[0062] Specifically, for the target dataset generated from the entire IP set, the Eclat algorithm is used for mining to find the largest frequent itemset in the target dataset; the specific execution flow of the Eclat algorithm is as follows:

[0063] 1) Scan the target dataset once, convert the horizontal format data to a vertical format, that is, set each link information dataset as an item, and count the record number of the item;

[0064] 2) Compare the number of records under each item's sequence number, i.e., the support of the item and the minimum support. If the number of records is less than the minimum support, discard the item and obtain a frequent 1-itemset.

[0065] 3) Merge frequent 1-itemsets to obtain candidate 2-itemsets. Count the number of records with the sequence number in the candidate 2-itemsets. If the candidate 2-itemsets is empty, then the process ends.

[0066] 4) Compare the number of records in the candidate 2-itemsets with the minimum support, and delete items with a number of records less than the minimum support to obtain frequent 2-itemsets;

[0067] 5) Similarly, after repeating 3) and 4), we finally obtain the frequent k-itemsets;

[0068] The frequent k-itemset is the largest frequent itemset of the target dataset, and the components in the largest frequent itemset are the hotspots in the link path.

[0069] Furthermore, in the multi-host link hotspot analysis method, step S300 involves analyzing the hotspots of the SAN storage network based on the maximum frequent itemset.

[0070] In a specific implementation, in this embodiment, after obtaining the largest frequent itemset of the target dataset, the hotspot components of the SAN storage network can then be analyzed.

[0071] Furthermore, in the multi-host link hotspot analysis method, step S300, analyzing the hotspots of the SAN storage network based on the maximum frequent itemset, includes:

[0072] Examine the relevant information of the hotspot in detail and determine whether the hotspot is an anomaly.

[0073] In specific implementation, in this embodiment, after analyzing and obtaining the hotspot components of the SAN storage network, the relevant information of the hotspot components is checked in detail, including health status, performance data, hardware information, etc., and it is determined whether the hotspot components are abnormal points, that is, whether there is a fault or insufficient resources.

[0074] As can be seen from the above method embodiments, the multi-host link hotspot analysis method provided by the present invention firstly queries the link information from each host to the storage in the SAN storage network based on the input IP set, and sets record numbers for the link information to obtain the corresponding link information dataset. Then, based on the link information dataset, a target dataset of the SAN storage network is formed. Secondly, a minimum support of the link information dataset is preset, and then the Eclat algorithm is used to mine the target dataset based on the minimum support to find the maximum frequent itemset of the target dataset. Finally, based on the maximum frequent itemset, the hotspots of the SAN storage network are analyzed and obtained. At the same time, the relevant information of the hotspots is checked in detail, and it is determined whether the hotspots are anomalies. In this way, the multi-host link hotspot analysis method of the present invention can achieve rapid and accurate location of hotspots in the SAN storage network, and then determine whether anomalies have occurred.

[0075] It should be understood that although this application provides the method operation steps as described in the embodiments or flowcharts, conventional or non-inventive labor may include more or fewer operation steps, and these operation steps are not necessarily executed sequentially according to the order of the embodiments or flowcharts. The order of steps listed in the embodiments or flowcharts is merely one way of executing many steps and does not represent the only execution order. It should be noted that there is no necessary sequential order between the above steps. Those skilled in the art can understand from the description of the embodiments of the present invention that the above steps may have different execution orders in different embodiments, that is, they may be executed in parallel or in exchange, etc. Moreover, at least some steps in the embodiments or flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but may be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but may be executed in turn, alternately, or synchronously with other steps or at least a part of the sub-steps or stages of other steps.

[0076] Based on the above embodiments, please refer to Figure 4 Another embodiment of the present invention also provides a multi-host link hotspot analysis system, wherein the system includes:

[0077] The query module 11 is used to query the link information from each host to the storage in the SAN storage network based on the input IP set, so as to form the target dataset of the SAN storage network.

[0078] Mining module 12 is used to mine the target dataset using the Eclat algorithm to find the largest frequent itemset in the target dataset;

[0079] Analysis module 13 is used to analyze and obtain the hotspots of the SAN storage network based on the maximum frequent itemset.

[0080] Furthermore, in the aforementioned multi-host link hotspot analysis system, the step of querying the link information from each host to the storage within the SAN storage network based on the input IP set to form the target dataset of the SAN storage network includes:

[0081] Based on the input set of IPs, query the link information from each host to the storage in the SAN storage network;

[0082] Based on the link information, a record number is set to obtain the corresponding link information dataset;

[0083] Based on the link information dataset, a target dataset is formed to constitute the SAN storage network.

[0084] In specific implementation, in this embodiment, based on the input IP set (IP address string), the management tool queries the link information from each host to the storage in the SAN storage network; each IP address forms a link record, and based on the link information, a record number is set to obtain the corresponding link information dataset; then, based on the link information dataset, the target dataset of the SAN storage network is formed.

[0085] The link information includes the host's cluster information, physical machine information, and component information; further, the component information includes the switches, switch ports, and switch cards connected to the host, as well as the physical components on the link such as the storage, storage ports, storage controllers, and switch ports connected to the storage ports configured on the host.

[0086] Furthermore, in the aforementioned multi-host link hotspot analysis system, the step of using the Eclat algorithm to mine the target dataset and find the largest frequent itemset in the target dataset includes:

[0087] The minimum support level of the link information dataset is preset;

[0088] Based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset.

[0089] In this specific implementation, firstly, the minimum support of the link information dataset is preset, wherein the minimum support is half the number of IPs in the IP set. Setting this parameter can speed up the iteration process of the Eclat algorithm. Secondly, based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset of the target dataset.

[0090] Specifically, for the target dataset generated from the entire IP set, the Eclat algorithm is used for mining to find the largest frequent itemset in the target dataset; the specific execution flow of the Eclat algorithm is as follows:

[0091] 1) Scan the target dataset once, convert the horizontal format data to a vertical format, that is, set each link information dataset as an item, and count the record number of the item;

[0092] 2) Compare the number of records under each item's sequence number, i.e., the support of the item and the minimum support. If the number of records is less than the minimum support, discard the item and obtain a frequent 1-itemset.

[0093] 3) Merge frequent 1-itemsets to obtain candidate 2-itemsets. Count the number of records with the sequence number in the candidate 2-itemsets. If the candidate 2-itemsets is empty, then the process ends.

[0094] 4) Compare the number of records in the candidate 2-itemsets with the minimum support, and delete items with a number of records less than the minimum support to obtain frequent 2-itemsets;

[0095] 5) Similarly, after repeating 3) and 4), we finally obtain the frequent k-itemsets;

[0096] The frequent k-itemset is the largest frequent itemset of the target dataset, and the components in the largest frequent itemset are the hotspots in the link path.

[0097] Furthermore, in the multi-host link hotspot analysis system, the hotspots of the SAN storage network are analyzed based on the maximum frequent itemset.

[0098] In a specific implementation, in this embodiment, after obtaining the largest frequent itemset of the target dataset, the hotspot components of the SAN storage network can then be analyzed.

[0099] Furthermore, in the multi-host link hotspot analysis system, the step of analyzing and obtaining the hotspots of the SAN storage network based on the maximum frequent itemset includes:

[0100] Examine the relevant information of the hotspot in detail and determine whether the hotspot is an anomaly.

[0101] In specific implementation, in this embodiment, after analyzing and obtaining the hotspot components of the SAN storage network, the relevant information of the hotspot components is checked in detail, including health status, performance data, hardware information, etc., and it is determined whether the hotspot components are abnormal points, that is, whether there is a fault or insufficient resources.

[0102] As can be seen from the above system embodiments, the multi-host link hotspot analysis system provided by the present invention firstly queries the link information from each host to the storage in the SAN storage network based on the input IP set, and sets record numbers for the link information to obtain the corresponding link information dataset. Then, based on the link information dataset, a target dataset of the SAN storage network is formed. Secondly, a minimum support of the link information dataset is preset, and then the Eclat algorithm is used to mine the target dataset based on the minimum support to find the maximum frequent itemset of the target dataset. Finally, based on the maximum frequent itemset, the hotspots of the SAN storage network are analyzed and obtained. At the same time, the relevant information of the hotspots is checked in detail, and it is determined whether the hotspots are anomalies. In this way, the multi-host link hotspot analysis system of the present invention can achieve rapid and accurate location of hotspots in the SAN storage network, and then determine whether anomalies have occurred.

[0103] Based on the above embodiments, please refer to Figure 5 Another embodiment of the present invention also provides a computer device, wherein the computer device 10 includes:

[0104] Memory 120 and one or more processors 110, Figure 5 The following description uses a processor 110 as an example. The processor 110 and the memory 120 can be connected via a communication bus or other means. Figure 5 Taking the example of China and Israel being connected via a communication bus.

[0105] Processor 110 performs various control logic functions of computer device 10. It can be a general-purpose processor, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), microcontroller, ARM (AcornRISC Cachine) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination of these components. Furthermore, processor 110 can also be any conventional processor, microprocessor, or state machine. Processor 110 can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration.

[0106] The memory 120, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the computer program corresponding to the multi-host link hotspot analysis method in this embodiment of the invention. The processor 110 executes various functional applications and data processing of the computer device 10 by running the non-volatile software programs, instructions, and units stored in the memory 120, thereby implementing the multi-host link hotspot analysis method in the above-described method embodiment.

[0107] The memory 120 may include a program storage area and a data storage area, wherein the program storage area may store application programs required for operating the device and at least one function; and the data storage area may store data created based on the use of the computer device 10. Furthermore, the memory 120 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the memory 120 may optionally include memory remotely located relative to the processor 110, and these remote memories may be connected to the computer device 10 via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] One or more units are stored in memory 120. When executed by one or more processors 110, they can implement the multi-host link hotspot analysis method as described in any of the above method embodiments. For example, they can implement the method described above. Figure 1 The method steps S100 to S300.

[0109] Those skilled in the art will understand that Figure 5 The hardware structure diagram shown is only a schematic diagram of a part of the structure related to the present invention and does not constitute a limitation on the computer device on which the present invention is applied. The specific computer device may include more components than shown in the figure, or combine some components, or have different component arrangements.

[0110] Based on the above embodiments, the present invention also provides 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 at least one processor, it can implement the multi-host link hotspot analysis method as described in any of the above method embodiments, for example, it can implement the above-described method. Figure 1 The method steps S100 to S300.

[0111] As an example, non-volatile storage media can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) as an external cache memory. By way of illustration and not limitation, RAM can be obtained in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The memory components or memories disclosed in the operating environment described herein are intended to include one or more of these and / or any other suitable types of memory.

[0112] Another embodiment of the present invention provides a computer program product comprising a computer program stored on a non-volatile computer-readable storage medium, the computer program including program instructions that, when executed by a processor, can implement the multi-host link hotspot analysis method as described in any of the above method embodiments, for example, the method described above. Figure 1 The method steps S100 to S300.

[0113] The embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0114] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a general-purpose hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can exist in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0115] Among other things, conditional language such as “can,” “may,” “may,” or “may,” unless otherwise specifically stated or otherwise understood as in the context in which they are used, is generally intended to convey that a particular implementation may include (but not others) certain features, elements, and / or operations. Therefore, such conditional language is also generally intended to imply that features, elements, and / or operations are necessary for one or more implementations in any way, or that one or more implementations must include logic for determining, with or without input or prompting, whether such features, elements, and / or operations are included or will be performed in any particular implementation.

[0116] The contents already described herein in this specification and accompanying drawings include examples of a multi-host link hotspot analysis method, system, computer device, and non-volatile computer-readable storage medium. It is certainly not possible to describe every conceivable combination of elements and / or methods for the purpose of describing the various features of this disclosure, but it will be appreciated that many other combinations and substitutions of the disclosed features are possible. Therefore, it will be apparent that various modifications can be made to this disclosure without departing from the scope or spirit of this disclosure, but all such various modifications should fall within the protection scope of the appended claims. Furthermore, or in alternatives, other embodiments of this disclosure may become apparent from consideration of this specification and accompanying drawings and from practice of this disclosure as presented herein. It is intended that the examples presented in this specification and accompanying drawings be considered illustrative rather than restrictive in all respects. Although specific terminology is used herein, it is used in a general and descriptive sense and is not intended for limiting purposes.

Claims

1. A method for multi-host link hotspot analysis, characterized in that, include: Based on the input IP set, query the link information from each host to the storage in the SAN storage network to form the target dataset of the SAN storage network. The Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset; Based on the maximum frequent itemset, the hotspots of the SAN storage network are analyzed. The step of querying the link information from each host to the storage within the SAN storage network based on the input IP set to form the target dataset of the SAN storage network includes: Based on the input set of IPs, query the link information from each host to the storage in the SAN storage network; Based on the link information, a record number is set to obtain the corresponding link information dataset; Based on the link information dataset, a target dataset is formed to constitute the SAN storage network; The step of using the Eclat algorithm to mine the target dataset and find the largest frequent itemset in the target dataset includes: The minimum support level of the link information dataset is preset; Based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset; The minimum support of the link information dataset is preset, wherein the minimum support is half the number of IPs in the IP set.

2. The multi-host link hotspot analysis method according to claim 1, characterized in that, The step involves querying the link information from each host to the storage within the SAN storage network based on the input IP set. The link information includes the host's cluster information, physical machine information, and component information.

3. The multi-host link hotspot analysis method according to claim 2, characterized in that, The component information includes the switch, switch port, and switch board connected to the host, as well as the storage, storage port, storage controller, and switch port connected to the storage port configured on the host.

4. The multi-host link hotspot analysis method according to any one of claims 1-3, characterized in that, The analysis of hotspots in the SAN storage network based on the maximum frequent itemset includes: Examine the relevant information of the hotspot in detail and determine whether the hotspot is an anomaly.

5. A multi-host link hotspot analysis system, characterized in that, include: The query module is used to query the link information from each host to the storage in the SAN storage network based on the input IP set, so as to form the target dataset of the SAN storage network. The mining module is used to mine the target dataset using the Eclat algorithm to find the largest frequent itemset in the target dataset; An analysis module is used to analyze and obtain the hotspots of the SAN storage network based on the maximum frequent itemset; The step of querying the link information from each host to the storage within the SAN storage network based on the input IP set to form the target dataset of the SAN storage network includes: Based on the input set of IPs, query the link information from each host to the storage in the SAN storage network; Based on the link information, a record number is set to obtain the corresponding link information dataset; Based on the link information dataset, a target dataset is formed to constitute the SAN storage network; The step of using the Eclat algorithm to mine the target dataset and find the largest frequent itemset in the target dataset includes: The minimum support level of the link information dataset is preset; Based on the minimum support, the Eclat algorithm is used to mine the target dataset to find the largest frequent itemset in the target dataset; The minimum support of the link information dataset is preset, wherein the minimum support is half the number of IPs in the IP set.

6. A computer device, characterized in that, The computer device includes at least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. When the computer program is executed by the at least one processor, it can implement the multi-host link hotspot analysis method as described in any one of claims 1-4.

7. A non-volatile computer-readable storage medium, characterized in that, The non-volatile computer-readable storage medium stores a computer program that, when executed by at least one processor, can implement the multi-host link hotspot analysis method as described in any one of claims 1-4.

Images