Patent Application
20200356297
The present disclosure is generally directed to storage systems, and more specifically, to storage control based on log data.
The number of companies that hold and analyze a large amount of log data of services for customers has increased. Since such companies desire to keep as much log data as possible to obtain high analytical accuracy, they take various countermeasures to suppress the retention cost of log data; for instance, by moving old log data to cheap and low speed devices, or though data compression of old log data.
However, as the content of the analysis changes frequently, there is difficulty in migrating and compressing log data without degrading analysis performance. In other words, the analysis performance may be degraded if there is no consideration regarding which logs are analyzed. Furthermore, manually designating log data to be moved/compressed based on this consideration is complicated, and there can be difficulty to keep up with the change of analysis contents.
In the related art, there is a system management unit which plans data migration according to a computation job execution schedule. In such a related art implementation, the system management unit creates a computation job execution schedule for a plurality of the computing units or obtains the computation job execution schedule from other unit in the computing system, plans a data migration in the tiered storage unit according to the execution schedule using a predetermined method, and instructs the tiered storage unit to migrate a data based on the plan.
However, in the related art, there is no differentiation between log data based on access characteristics. Example implementations described herein are directed to a storage system that performs moving/compression of log data focusing on the fact that the access characteristics are different for each log data type. Data movement and compression between storage tiers are performed on the storage in example implementations described herein, based on the access frequency for each log data type.
Aspects of the present disclosure involve a method, which can include classifying received log data for storage into a storage system involving a high tier storage and a low tier storage; for the classification indicative of storage into the high tier storage of the storage system, storing the log data into the high tier storage, and moving related log data stored in the low tier storage to the high tier storage; and for the classification indicative of storage into the low tier storage of the storage system, storing the log data into the low tier storage and moving the related log data stored in the high tier storage to the low tier storage.
Aspects of the present disclosure involve a computer program, storing instructions which can involve include classifying received log data for storage into a storage system involving a high tier storage and a low tier storage; for the classification indicative of storage into the high tier storage of the storage system, storing the log data into the high tier storage, and moving related log data stored in the low tier storage to the high tier storage; and for the classification indicative of storage into the low tier storage of the storage system, storing the log data into the low tier storage and moving the related log data stored in the high tier storage to the low tier storage. The instructions can be stored in a non-transitory computer readable medium and configured to be executed by one or more processors.
Aspects of the present disclosure further involve a server configured to manage a storage system involving high tier storage and low tier storage, the server involving a processor, configured to classify received log data for storage into a storage system; for the classification indicative of storage into the high tier storage of the storage system, store the log data into the high tier storage, and move related log data stored in the low tier storage to the high tier storage; and for the classification indicative of storage into the low tier storage of the storage system, store the log data into the low tier storage and move the related log data stored in the high tier storage to the low tier storage.
Aspects of the present disclosure involve a system, which can include means for classifying received log data for storage into a storage system involving a high tier storage and a low tier storage; for the classification indicative of storage into the high tier storage of the storage system, means for storing the log data into the high tier storage, and moving related log data stored in the low tier storage to the high tier storage; and for the classification indicative of storage into the low tier storage of the storage system, means for storing the log data into the low tier storage and moving the related log data stored in the high tier storage to the low tier storage.
The following detailed description provides further details of the figures and example implementations of the present application. Reference numerals and descriptions of redundant elements between figures are omitted for clarity. Terms used throughout the description are provided as examples and are not intended to be limiting. For example, the use of the term “automatic” may involve fully automatic or semi-automatic implementations involving user or administrator control over certain aspects of the implementation, depending on the desired implementation of one of ordinary skill in the art practicing implementations of the present application. Selection can be conducted by a user through a user interface or other input means, or can be implemented through a desired algorithm. Example implementations as described herein can be utilized either singularly or in combination and the functionality of the example implementations can be implemented through any means according to the desired implementations.
In a first example implementation, there is a log data generator server that creates a Generated Log Data Table, determines how log data are classified and further determines how a storage system determines a storage tier for stored data and so on.
Storage system 110 may also involve storage nodes. Further, the number of tiers may be other than two in accordance with a desired implementation. Also, the server may be a virtual machine or be one of the compute engines depending on the desired implementation. The log generator function may also be in the storage system 110 in accordance with a desired implementation. Further, there may be more than one log data generator servers 120, in accordance with a desired implementation.
The generated log table 400 can involve several columns for the entries. Column 410 shows log IDs. Column 420 shows time of occurrence. Column 430 shows application names. Column 440 shows action types. Column 450 shows success or failure of the action. Column 460 shows user IDs. Column 470 shows the compute machine ID that conducts the action. Column 480 shows IP address of the compute machine. Column 490 shows log group ID.
Log group ID definition 500 is a table for managing log data types and the elements included in this table depends on how a user would like to classify log data. For instance, the log group ID definition 500 can include log group ID 490, action type of log data 520 and application name 530, as example implementations can analyze the contents of the log data and merge log data of similar applications as one group in accordance with the desired implementation. Alternatively, log data can be of different types for each log data generator server or for each group of log data generator servers depending on the desired implementation.
Classification type of log data 555 shows a selected log data classification type. Some classification types are showed, and a user may select one of them or a log data generator server 120 may propose one of them to a user or log data generator server 120 may choose one of them automatically.
Tier information table 720 manages where log group IDs 490 are stored, and in which tiers. This table 720 can include the log group ID 490, total data amount of a log group ID 740, access counter 750 and tier 760. Depending on the desired implementation, access counter 740 may be further broken down to a counter for read operations and a counter for write operations from compute nodes, in accordance with the desired implementation.
Tier threshold table 780 manages tier threshold information to determine the moving data between tier 1 and tier 2. This table 780 involves minimum counts for tier 1 790 and limit data amount in tier 1 800.
At 910, the log data generator server 120 confirms the classification type of the log data. If no type of log data is selected, the log data generator server 120 suggests the optimal one for a user, or can conduct a selection in accordance with the desired implementation. At 920, the log data generator server 120 creates the log group ID definition based on the classification type. At 930, the log data generator server 120 sets log group ID 490 for each log ID 410 in generated log table 400 based on the information of log group ID definition 500. At 940, the log data generator server 120 sends the information of the log data with log group IDs to the storage system 110. Then, the storage system 110 sets log group ID 490 for each log ID 150A, 150B, 150C, in log data table 600 and sets log group ID 490 in tier information table 760. Elements other than log group ID 490 is blank in tier information table 720. Storage system 110 may set the default to tier 2 for all group IDs or may automatically determine tiers for group IDs depending on the desired implementation. Once conducted, the process ends at 950.
In example implementations, the table can be created in advance manually. If new log data is generated by log data generator server 120, the server should determine the classification type of the new log data.
At 1210, the log data generator server 120 determines the classification type of this log data based on log group ID definition 500 and decide a log group ID for it. Then the server sets the information of this log data in generated log table 400. At 1220, the log data generator server 120 then sends updated log data information to the cache area 340 of the storage system 110.
At 1230, the storage system 110 confirms the group ID of this log data and determines the tier to store the log data based on tier information table 720. If the tier for the log data is tier 1 (Yes), then the process proceeds to 1240, otherwise (No), the process proceeds to 1250.
At 1240, the storage system 110 confirms the tier 1 status based on tier threshold table 780. If the tier 1 status is equal to or less than the threshold value of tier 1 (Yes), the process proceeds to 1250. Otherwise, if the tier 1 status is over than the threshold value of tier 1 (No), the process proceeds to 1260.
At 1250, the storage system 110 writes updated log data to the specified tier based on the tier information table 720 and updates the logical address 690/physical address 700 in the log data table 600. At 1260, the storage system 110 writes updated log data to tier 2 and updates logical address 690/physical address 700 in log data table 600.
At 1310, the storage system 110 searches the tier information table 720 with minimum counts for tier 1 790 and selects log data for moving to another tier.
At 1320, the storage system 110 determines whether the estimated log data amount in tier 1 is equal or less than the limit data amount in tier 1 730 or not. If the answer is yes, the process proceeds to 1340. If the answer is no, the process proceeds to 1330.
At 1330, the storage system 110 selects log data for tier 1 storage by using the time of occurrence 420 information. The storage system 110 sequentially selects the log data to remain in tier 1 storage until the threshold value of tier 1 storage capacity is reached, starting with the log data having the earlier access time.
At 1340, the storage system 110 moves the log data between tier 1 and tier 2 as applicable.
At 1350, the storage system 110 resets the all counters 750 in tier information table 720 to zeros. The storage system 110 may compresses log data that was selected for tier 2 storage from the beginning and stores the compressed log data in tier 2 storage.
As described herein, there is a storage system that has high tier storage (tier 1), and low tier storage (tier 2) as described in
In example implementations, the storage system 110 can be configured to, through execution of log data analytics and data replacement application 180 by CPU 320 as illustrated in
In example implementations, the classification of the log ID can be conducted according to one or more of an application, an action, or a compute machine associated with the log data as set by log group ID definition 500 of
Depending on the desired implementation, the access threshold can be defined through a user interface on the log data generator server 120. For example, the interface can be configured to change the value of the access threshold 790 of tier threshold table 780 through user input.
In example implementations, storage system 110 can be configured to, through execution of log data analytics and data replacement application 180 by CPU 320 as illustrated in
Depending on the desired implementation, storage system 110 can be configured to, through CPU 320, compress log data that is to be moved to or stored in low tier storage so as to save storage space for the log data. Similarly, when log data is moved from low tier storage to high tier storage, storage system 110 can be configured to, through CPU 320, decompress the log data to be moved from the low tier storage before moving the data to the high tier storage.
Through the example implementations described herein, there is a log data holding method for suppressing the holding cost to a company that holds a large amount of log data for customers and uses it for analysis. Tier management program for log data provides a user interface through which users can select the tier threshold values. Log data management program in log data generator server may provide a user interface which users can select a classification type of log data, if users can select it as a specification.
Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations within a computer. These algorithmic descriptions and symbolic representations are the means used by those skilled in the data processing arts to convey the essence of their innovations to others skilled in the art. An algorithm is a series of defined steps leading to a desired end state or result. In example implementations, the steps carried out require physical manipulations of tangible quantities for achieving a tangible result.
Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout the description, discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining,” “displaying,” or the like, can include the actions and processes of a computer system or other information processing device that manipulates and transforms data represented as physical (electronic) quantities within the computer system's registers and memories into other data similarly represented as physical quantities within the computer system's memories or registers or other information storage, transmission or display devices.
Example implementations may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may include one or more general-purpose computers selectively activated or reconfigured by one or more computer programs. Such computer programs may be stored in a computer readable medium, such as a computer-readable storage medium or a computer-readable signal medium. A computer-readable storage medium may involve tangible mediums such as, but not limited to optical disks, magnetic disks, read-only memories, random access memories, solid state devices and drives, or any other types of tangible or non-transitory media suitable for storing electronic information. A computer readable signal medium may include mediums such as carrier waves. The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Computer programs can involve pure software implementations that involve instructions that perform the operations of the desired implementation.
Various general-purpose systems may be used with programs and modules in accordance with the examples herein, or it may prove convenient to construct a more specialized apparatus to perform desired method steps. In addition, the example implementations are not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the example implementations as described herein. The instructions of the programming language(s) may be executed by one or more processing devices, e.g., central processing units (CPUs), processors, or controllers.
As is known in the art, the operations described above can be performed by hardware, software, or some combination of software and hardware. Various aspects of the example implementations may be implemented using circuits and logic devices (hardware), while other aspects may be implemented using instructions stored on a machine-readable medium (software), which if executed by a processor, would cause the processor to perform a method to carry out implementations of the present application. Further, some example implementations of the present application may be performed solely in hardware, whereas other example implementations may be performed solely in software. Moreover, the various functions described can be performed in a single unit, or can be spread across a number of components in any number of ways. When performed by software, the methods may be executed by a processor, such as a general purpose computer, based on instructions stored on a computer-readable medium. If desired, the instructions can be stored on the medium in a compressed and/or encrypted format.
Moreover, other implementations of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the teachings of the present application. Various aspects and/or components of the described example implementations may be used singly or in any combination. It is intended that the specification and example implementations be considered as examples only, with the true scope and spirit of the present application being indicated by the following claims.
