CN113254324A - LPAR performance acquisition method and system - Google Patents
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- CN113254324A CN113254324A CN202110792462.1A CN202110792462A CN113254324A CN 113254324 A CN113254324 A CN 113254324A CN 202110792462 A CN202110792462 A CN 202110792462A CN 113254324 A CN113254324 A CN 113254324A
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- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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Abstract
The application relates to the technical field of data storage, in particular to a method and a system for acquiring LPAR performance, comprising the following steps: acquiring LPAR performance data through a buried point at the bottom layer of a resource pool; analyzing the collected LPAR performance data and transmitting the LPAR performance data to a background server through a data analysis transmission tool of a resource pool access layer; and a data analysis generation tool is arranged through the background server, the received LPAR performance data is analyzed and stored, and a performance display report is generated according to the analyzed and stored data for displaying. The method and the device can enable the LPAR performance to be comprehensively collected, and the occupation ratio of the CPU and the memory is low.
Description
Technical Field
The application relates to the technical field of data storage, in particular to a method and a system for acquiring LPAR performance.
Background
An LPAR is a logical partition, simply a partition of a physical host into multiple logical partitions, running independent applications from each other. The LPAR performance collection is mainly the performance data monitoring and data collection of the OS system layer of the LPAR, such as the performance collection of the OS system layer of the LPAR through an Lpar2rrd tool, an Oswatch tool, an atom tool and the like.
The Lpar2rrd tool may collect historical CPU usage data for shared processor partitions and systems. The Lpar2rrd tool is applicable to micro-partitioned systems using a shared pool of processors interfaced with an HMC (graphical interface software) and does not require the installation of agents on each Lpar. Specifically, the Lpar2RRD tool is connected to a designated HMC via SSH (remote connection tool) to collect performance data, and the collected performance data is stored in an RRD database (cyclic database) in the Lpar2RRD tool system. In addition, the Lpar2rrd tool processes the collected performance data to generate a shared processor usage map for each Lpar and shared processor pool. In addition, if a client of the Lpar2rrd tool is installed in each LPAR, the memory and CPU utilization rate of the OS system can be monitored. However, the Lpar2rrd tool mainly monitors the utilization rate of the memory or cpu, and the performance indexes of monitoring and acquisition are not specific and comprehensive.
The Oswatch tool completes the collection of performance data by calling a system command, wherein the system command is as follows: a ps command (for listing snapshots of processes currently running in the system), a top command (for displaying resource occupation of each process in the system in real time), an mpstat command (for displaying the state of each available CPU in a multi-CPU environment), an iostat command (for outputting CPU and disk I/O related statistics), a netsta command (for displaying the network state of the entire Linux system), a traceroute command (for tracking the line taken by a target address), a vmstat command (for displaying the state value of a server for a given time interval), and the like. Specifically, through a simple script, the performance data is retained in the corresponding directory file at regular intervals, and parameters can also be specified to ensure that the latest file is retained. In order to facilitate drawing, a jar file is also provided in the Oswatch toolkit, and collected data can be conveniently shown in a drawing form through the jar file. However, when the Oswatch tool collects performance data, the performance ratio of the Oswatch tool to cpu, memory and the like is high, and a corresponding client needs to be deployed on each monitoring node, so that the deployment is not convenient and fast, and the later-stage operation and maintenance cost is high.
The Atop tool is a tool for monitoring the resources and the processes of the Linux system. The Atop tool can record the running state of the system at a certain frequency, and the collected performance data comprises the use condition of system resources (CPU, memory, disk and network) and the running condition of the process, and can be stored in the disk in a log file manner. In addition, if the server has problems, corresponding atom log files can be obtained for analysis, and processing such as sorting, view switching, regular matching and the like can be carried out when data are analyzed. However, when the Atop tool collects performance data, the ratio of performance to cpu, memory and the like is high, and the monitoring and collecting indexes are not specific and comprehensive enough.
Therefore, how to collect the performance of the LPAR more comprehensively and occupy less CPU and memory is a technical problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
The application provides a method and a system for acquiring LPAR performance, so that the LPAR performance acquisition is relatively comprehensive, and the occupation ratio of a CPU (Central processing Unit) to a memory is relatively low.
In order to solve the technical problem, the application provides the following technical scheme:
a LPAR performance collection method comprising the steps of: step S110, collecting LPAR performance data through a buried point at the bottom layer of a resource pool; step S120, analyzing the collected LPAR performance data and transmitting the LPAR performance data to a background server through a data analysis transmission tool of a resource pool access layer; and step S130, analyzing and storing the received LPAR performance data through a data analysis and generation tool of the background server, and generating a performance display report according to the analyzed and stored data for displaying.
The LPAR performance collection method described above, wherein the code to collect the data is preferably planted at the bottom of the Power resource pool for landfilling.
The method for acquiring the performance of the LPAR is preferably that the acquisition of the LPAR performance data through the embedded point has working modes of real-time monitoring acquisition, background monitoring acquisition, periodic monitoring acquisition and the like.
The method for acquiring the performance of the LPAR as described above, wherein preferably, a data analysis transmission tool is deployed in an access layer of the resource pool to analyze the performance data of the LPAR acquired by the buried point at the bottom of the resource pool, perform time interception calculation on the analyzed performance data, compress the intercepted data, and send the compressed data to the background server.
The LPAR performance collection method as described above, wherein preferably, a data parsing and generating tool is deployed in the backend server to parse the compressed data transmitted to the backend server by the access layer of the resource pool, store the parsed LPAR performance data in the data center of the backend server, generate a performance display report according to the LPAR performance data stored in the data center, and display the performance display report on a display connected to the backend server more intuitively.
An LPAR performance acquisition system comprising: the system comprises a resource pool, a resource pool access layer and a background server; the method comprises the following steps that (1) a buried point is buried in the bottom layer of a resource pool, and LPAR performance data are collected through the buried point; a data analysis transmission tool is deployed in the resource pool access layer, and the acquired LPAR performance data is analyzed and transmitted to the background server through the data analysis transmission tool; and a data analysis generating tool is deployed in the background server, the received LPAR performance data is analyzed and stored through the data analysis generating tool, and a performance display report is generated according to the analyzed and stored data for displaying.
The LPAR performance collection system described above, wherein the code to collect the data is preferably planted at the bottom of the Power resource pool for landfilling.
The LPAR performance acquisition system as described above, wherein, preferably, the acquisition of LPAR performance data by the embedded point has working modes of real-time monitoring acquisition, background monitoring acquisition, periodic monitoring acquisition, and the like.
The system for acquiring the performance of the LPAR as described above, wherein preferably, a data analysis transmission tool is deployed in an access layer of the resource pool to analyze the performance data of the LPAR acquired by the buried point at the bottom of the resource pool, perform time interception calculation on the analyzed performance data, compress the intercepted data, and send the compressed data to the background server.
The LPAR performance collection system as described above, wherein preferably, a data parsing and generating tool is deployed in the backend server to parse the compressed data transmitted to the backend server by the access layer of the resource pool, store the parsed LPAR performance data in the data center of the backend server, generate a performance display report according to the LPAR performance data stored in the data center, and display the performance display report on a display connected to the backend server more intuitively.
In order to solve the technical problems, the analysis calculation and the drawing display of the collected LPAR performance data are carried out in a background server, so that the performance data of an OS system layer of the LPAR is collected in the application, and the performance of a CPU, a memory and the like is less; in addition, the method and the device have the advantage that the LPAR performance data types collected and monitored can be more comprehensive by embedding points at the bottom layer of the resource layer.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
FIG. 1 is a flow chart of a method of LPAR performance collection provided by an embodiment of the present application;
FIG. 2 is a schematic diagram of an LPAR performance acquisition system provided by an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative only and should not be construed as limiting the invention.
Example one
As shown in FIG. 1, the present application provides a LPAR performance collection method comprising the steps of:
step S110, collecting LPAR performance data through a buried point at the bottom layer of a resource pool;
specifically, a code for collecting data is implanted into the bottom layer of the Power resource pool to perform embedding, and embedding is a private data collection deployment mode. LPAR performance data is collected by a buried point at the bottom of the Power resource pool, for example: the method comprises the steps of collecting performance data such as CPU utilization rate, memory use condition, kernel statistical information, running queue information, disk I/O speed, transmission and read/write rate, available space in a file system, disk adapters, network I/O speed, transmission and read/write rate, page space and page speed, CPU specification, a process consuming most resources, HTTP Web cache, a user-defined disk group, computer detailed information and resources, asynchronous I/O and the like. According to the method and the device, the points are buried in the bottom layer of the resource layer, so that the LPAR performance data types collected and monitored can be more comprehensive.
In addition, the embedded point has working modes of real-time monitoring acquisition, background monitoring acquisition, periodic monitoring acquisition and the like for LPAR performance data acquisition. Through different working modes, the accuracy and the real-time performance of LPAR performance data collected by the embedded point can be realized.
Wherein, the real-time monitoring acquisition is to acquire LPAR performance data in real time from a buried point at the bottom of a resource pool.
For example: the Linux system CPU, the memory and the process information are displayed in real time, the Linux system CPU, the memory and the process information comprise detailed indexes of a CPU user, a system, a waiting state value, an idle state value, an available memory, a cache size, a process CPU consumption and the like, and the operating condition of the system under the bearing pressure can be mastered in time, wherein the utilization rate of each CPU, the use amount of the memory, the network flow and the disk reading and writing are realized.
And the background monitoring acquisition is to acquire LPAR performance data by a command sent by a background server through a buried point at the bottom layer of the resource pool.
For example: the embedded point receives a command "/acquisition-f-s 10-c 360" sent by the background server, wherein the forward and backward-f: outputting the file name according to a standard format: < hostname > _ yyyyymmdd _ hhmm. acquisition, center and forward-s: sampling every n seconds, preferably 10 seconds in this application, -c: how many samples are taken, preferably 360 in this application, i.e. monitoring =10 × 360/3600=1 hour.
After the embedded point starts the command, a monitoring file is generated under the directory where the collection is located, and the resource data is continuously written in until the collection of 360 monitoring points is completed, namely the monitoring is completed for 1 hour, and the operations are automatically completed without manual intervention.
The periodic monitor collection is the collection of LPAR performance data by a predetermined deadline from a buried point at the bottom of the resource pool.
For example: fixed-time monitoring of the buried points can be realized by a command # crontab-e, and the last row is added with 08 × 1,2,3,4, 5/acquisition-f-s 30-c 1200', which indicates that: monday through Friday, starting at 08 am, monitor for 10 hours (to 18:00 en.).
Step S120, analyzing the collected LPAR performance data and transmitting the LPAR performance data to a background server through a data analysis transmission tool of a resource pool access layer;
specifically, a data analysis transmission tool is deployed in an access layer (namely an OS system layer of the LPAR) of the resource pool to analyze LPAR performance data collected by a buried point at the bottom layer of the resource pool, then time interception calculation is performed on the analyzed performance data, then the intercepted data is compressed, and the compressed data is sent to a background server.
For example: the method comprises the steps that an LPAR2ftp-original. pl tool is deployed in an access layer of a resource pool and analyzes collected LPAR performance data, wherein time interception calculation is only carried out on the collected LPAR performance data, then intercepted data are compressed into a gzip format, and then data in the gzip format are sent to an ftp server by using an ftp protocol.
The lpar2ftp-original.pl tool only performs data analysis and time interception calculation, so that the lpar2ftp-original.pl tool only uses few CPU and memory resources, the occupation of CPU and memory is reduced (the occupation ratio of CPU and memory is less than 2%), and in addition, the data in the gzip format is small, the data in the gzip format is less than 20KB in the application, so that the transmission pressure is reduced, and the transmission time is short (less than 2 seconds).
Step S130, a data analysis generation tool is used for analyzing and storing the received LPAR performance data through a background server, and a performance display report is generated according to the analyzed and stored data for displaying;
specifically, a data analysis generation tool is deployed in the background server to analyze compressed data transmitted to the background server by the access layer of the resource pool, then the analyzed LPAR performance data is stored in the data center of the background server, and finally a performance display report is generated according to the LPAR performance data stored in the data center and is displayed on a display connected with the background server more intuitively.
For example: deploying an lpar2ftp-server.pl tool in an ftp server, continuously scanning an ftp shared directory by the lpar2ftp-server.pl tool, analyzing data in a gzip format, and generating standard json monitoring data; then using socket protocol to send standard json monitoring data to powermanager; a powermanager (data management program) sends json monitoring data to a data server (data processing program) of a data center of the ftp server, and the json monitoring data are stored in the data center of the ftp server through the data server (data processing program); and analyzing the collected monitoring data, outputting a generated graph and creating a gif file and an instrument graph which can be displayed on a Web site, and drawing the data can be easily realized through the macro function of excel.
Example two
As shown in fig. 2, the present application provides an LPAR performance acquisition system comprising: resource pool 210, resource pool access layer 220, and backend server 230.
Where resource pool 210 is buried with buried points at the bottom, LPAR performance data is collected through the buried points.
Specifically, a code for collecting data is implanted into the bottom layer of the Power resource pool to perform embedding, and embedding is a private data collection deployment mode. LPAR performance data is collected by a buried point at the bottom of the Power resource pool, for example: the method comprises the steps of collecting performance data such as CPU utilization rate, memory use condition, kernel statistical information, running queue information, disk I/O speed, transmission and read/write rate, available space in a file system, disk adapters, network I/O speed, transmission and read/write rate, page space and page speed, CPU specification, a process consuming most resources, HTTP Web cache, a user-defined disk group, computer detailed information and resources, asynchronous I/O and the like. According to the method and the device, the points are buried in the bottom layer of the resource layer, so that the LPAR performance data types collected and monitored can be more comprehensive.
In addition, the embedded point has working modes of real-time monitoring acquisition, background monitoring acquisition, periodic monitoring acquisition and the like for LPAR performance data acquisition. Through different working modes, the accuracy and the real-time performance of LPAR performance data collected by the embedded point can be realized.
Wherein, the real-time monitoring acquisition is to acquire LPAR performance data in real time from a buried point at the bottom of a resource pool.
For example: the Linux system CPU, the memory and the process information are displayed in real time, the Linux system CPU, the memory and the process information comprise detailed indexes of a CPU user, a system, a waiting state value, an idle state value, an available memory, a cache size, a process CPU consumption and the like, and the operating condition of the system under the bearing pressure can be mastered in time, wherein the utilization rate of each CPU, the use amount of the memory, the network flow and the disk reading and writing are realized.
And the background monitoring acquisition is to acquire LPAR performance data by a command sent by a background server through a buried point at the bottom layer of the resource pool.
For example: the embedded point receives a command "/acquisition-f-s 10-c 360" sent by the background server, wherein the forward and backward-f: outputting the file name according to a standard format: < hostname > _ yyyyymmdd _ hhmm. acquisition, center and forward-s: sampling every n seconds, preferably 10 seconds in this application, -c: how many samples are taken, preferably 360 in this application, i.e. monitoring =10 × 360/3600=1 hour.
After the embedded point starts the command, a monitoring file is generated under the directory where the collection is located, and the resource data is continuously written in until the collection of 360 monitoring points is completed, namely the monitoring is completed for 1 hour, and the operations are automatically completed without manual intervention.
The periodic monitor collection is the collection of LPAR performance data by a predetermined deadline from a buried point at the bottom of the resource pool.
For example: fixed-time monitoring of the buried points can be realized by a command # crontab-e, and the last row is added with 08 × 1,2,3,4, 5/acquisition-f-s 30-c 1200', which indicates that: monday through Friday, starting at 08 am, monitor for 10 hours (to 18:00 en.).
A data parsing transport tool is deployed in the resource pool access layer 220, and the collected LPAR performance data is parsed and transmitted to the backend server 230 through the data parsing transport tool.
Specifically, a data analysis transmission tool is deployed in an access layer (namely an OS system layer of the LPAR) of the resource pool to analyze LPAR performance data collected by a buried point at the bottom layer of the resource pool, then time interception calculation is performed on the analyzed performance data, then the intercepted data is compressed, and the compressed data is sent to a background server.
For example: the method comprises the steps that an LPAR2ftp-original. pl tool is deployed in an access layer of a resource pool and analyzes collected LPAR performance data, wherein time interception calculation is only carried out on the collected LPAR performance data, then intercepted data are compressed into a gzip format, and then data in the gzip format are sent to an ftp server by using an ftp protocol.
The lpar2ftp-original.pl tool only performs data analysis and time interception calculation, so that the lpar2ftp-original.pl tool only uses few CPU and memory resources, the occupation of CPU and memory is reduced (the occupation ratio of CPU and memory is less than 2%), and in addition, the data in the gzip format is small, the data in the gzip format is less than 20KB in the application, so that the transmission pressure is reduced, and the transmission time is short (less than 2 seconds).
A data analysis and generation tool is deployed in the background server 230, the received LPAR performance data is analyzed and stored by the data analysis and generation tool, and a performance display report is generated according to the analyzed and stored data for display.
Specifically, a data analysis generation tool is deployed in the background server to analyze compressed data transmitted to the background server by the access layer of the resource pool, then the analyzed LPAR performance data is stored in the data center of the background server, and finally a performance display report is generated according to the LPAR performance data stored in the data center and is displayed on a display connected with the background server more intuitively.
For example: deploying an lpar2ftp-server.pl tool in an ftp server, continuously scanning an ftp shared directory by the lpar2ftp-server.pl tool, analyzing data in a gzip format, and generating standard json monitoring data; then using socket protocol to send standard json monitoring data to powermanager; a powermanager (data management program) sends json monitoring data to a data server (data processing program) of a data center of the ftp server, and the json monitoring data are stored in the data center of the ftp server through the data server (data processing program); and analyzing the collected monitoring data, outputting a generated graph and creating a gif file and an instrument graph which can be displayed on a Web site, and drawing the data can be easily realized through the macro function of excel.
Since the analysis calculation and the drawing display of the collected LPAR performance data are both performed in a background server (ftp server), the performance data of the OS system layer of the LPAR is collected in the method, and the performance data of the OS system layer of the LPAR occupies little performance such as a CPU (Central processing Unit), a memory and the like. In addition, analytical calculations for collecting LPAR performance data are all performed in a background server (ftp server), so the unified calculation can be convenient for later maintenance.
In addition, since the compressed data is transmitted to the background server in the application, the transmitted data is small (gzip data size is less than 20 KB), and the pressure of data transmission is reduced.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (10)
1. A method of LPAR performance collection comprising the steps of:
step S110, collecting LPAR performance data through a buried point at the bottom layer of a resource pool;
step S120, analyzing the collected LPAR performance data and transmitting the LPAR performance data to a background server through a data analysis transmission tool of a resource pool access layer;
and step S130, analyzing and storing the received LPAR performance data through a data analysis and generation tool of the background server, and generating a performance display report according to the analyzed and stored data for displaying.
2. The LPAR performance collection method of claim 1 wherein the code to collect the data is planted at the bottom of the Power resource pool for landfilling.
3. The LPAR performance collection method of claim 1 or 2 wherein LPAR performance data collection by the burying has operating modes of real-time monitoring collection, background monitoring collection, periodic monitoring collection, and the like.
4. The LPAR performance collection method of claim 1 or 2 wherein a data parsing transport is deployed in the access layer of the resource pool to parse LPAR performance data collected at buried points at the bottom of the resource pool, perform a time interception calculation on the parsed performance data, compress the intercepted data, and send the compressed data to the background server.
5. The LPAR performance collection method of claim 1 or 2 wherein the backend server has deployed therein a data parsing and generation tool to parse compressed data transmitted to the backend server by the access layer of the resource pool, store the parsed LPAR performance data in the data center of the backend server, generate a performance presentation report based on the LPAR performance data stored in the data center, and present the performance presentation report more visually on a display connected to the backend server.
6. An LPAR performance acquisition system comprising: the system comprises a resource pool, a resource pool access layer and a background server;
the method comprises the following steps that (1) a buried point is buried in the bottom layer of a resource pool, and LPAR performance data are collected through the buried point;
a data analysis transmission tool is deployed in the resource pool access layer, and the acquired LPAR performance data is analyzed and transmitted to the background server through the data analysis transmission tool;
and a data analysis generating tool is deployed in the background server, the received LPAR performance data is analyzed and stored through the data analysis generating tool, and a performance display report is generated according to the analyzed and stored data for displaying.
7. The LPAR performance collection system of claim 6 wherein the code to collect data is planted at the bottom of the Power resource pool for landfilling.
8. The LPAR performance collection system of claim 6 or 7 wherein LPAR performance data collection by the burying has operating modes of real time monitoring collection, background monitoring collection, periodic monitoring collection, and the like.
9. The LPAR performance acquisition system of claim 6 or 7 wherein a data parsing transport is deployed in the access layer of the resource pool to parse LPAR performance data acquired at buried points at the bottom of the resource pool, perform a time interception calculation on the parsed performance data, compress the intercepted data, and send the compressed data to the background server.
10. The LPAR performance collection system of claim 6 or 7 wherein the backend server has deployed therein a data parsing and generation tool to parse compressed data transmitted to the backend server by the access layer of the resource pool, store the parsed LPAR performance data in the data center of the backend server, generate a performance exposure report based on the LPAR performance data stored in the data center, and more intuitively expose the performance exposure report on a display connected to the backend server.
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| US20140046968A1 (en) * | 2011-04-15 | 2014-02-13 | Tencent Technology (Shenzhen) Company Limited | Data acquisition method, device and system |
| CN102843418A (en) * | 2012-07-03 | 2012-12-26 | 广东电网公司信息中心 | Resource dispatching system |
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