CN101021810A - Software system performance estimating method - Google Patents
Software system performance estimating method Download PDFInfo
- Publication number
- CN101021810A CN101021810A CN 200710013763 CN200710013763A CN101021810A CN 101021810 A CN101021810 A CN 101021810A CN 200710013763 CN200710013763 CN 200710013763 CN 200710013763 A CN200710013763 A CN 200710013763A CN 101021810 A CN101021810 A CN 101021810A
- Authority
- CN
- China
- Prior art keywords
- database
- url
- time
- average
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 14
- 230000004044 response Effects 0.000 claims description 34
- 230000002354 daily effect Effects 0.000 claims description 30
- 238000004458 analytical method Methods 0.000 claims description 17
- 238000011156 evaluation Methods 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 9
- 230000003442 weekly effect Effects 0.000 claims description 8
- 238000000151 deposition Methods 0.000 claims description 6
- 238000009825 accumulation Methods 0.000 claims description 3
- 230000003203 everyday effect Effects 0.000 claims description 3
- 230000008676 import Effects 0.000 claims description 3
- 238000004452 microanalysis Methods 0.000 claims description 3
- 238000005201 scrubbing Methods 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 230000008859 change Effects 0.000 claims description 2
- 230000006872 improvement Effects 0.000 claims description 2
- 230000010354 integration Effects 0.000 claims description 2
- 238000005457 optimization Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
Abstract
The invention provides a method for colleting the operational information based on J2EE database management system, and analyzing the information to submit the information of per week about visit, server load, performance bottlenecks, fault reports, thereby it helps the serving personal to improve, updating, optimizing the information system.
Description
Technical field
The present invention relates to computer realm, is a kind of method of the J2EE software systems being carried out Performance Evaluation.
Technical background
Along with enterprise and public sector to infosystem use extensive, its various work more and more rely on the support of infosystem, and availability, the stability of infosystem has been proposed more and more higher requirement.Whether general software development is mainly paid attention to the function of software systems and is met consumers' demand, and does not almost have what concern for the performance of self-operating, stability etc.In this case, the maintenance difficulties of infosystem is increasing, such as phenomenons such as response is slack-off, the machine of delaying often occurring.When portfolio increased, most users then blindly solved by expanding hardware.Because software development company lacks the effective assessment to software performance, cause problem to make the user often at present, cause a large amount of wastes of fund blindly to hardware investment because of software performance.
Summary of the invention
Comprise Information Collection System, data handling system and reporting system, it is characterized in that: from the index of many description software systems operation conditionss, pick out crucial some and write down and store, and designed one and overlapped integration algorithm, be used for the performance of assessment software system, stable situation, by making the decision-making of improvement, upgrading and optimization to software systems with the comparison of historical information approximation system to assist managerial personnel, appraisal procedure comprises:
(1) the acquisition system operation information of gathering each link is analyzed, and information and analysis result stored, trading volume, data volume, peak load to system handles change, at any time compare with historical data, find out discrepancy, in time distribute rationally, for the many cover systems that move simultaneously, data compare between different system, find out the system of poor performance and improve;
Information Collection System comprises the daily record that complete daily record, operating system, software middleware, application software and the database of a cover carries; In the software systems based on J2EE, what application server was collected is that CPU occupied information, solicited message url_conn.log and JVM heap memory reclaim information native_stderr.log; What database server was collected is CPU occupied information, EMS memory occupation information, archive log, database snapshot;
(2) data handling system is calculated the back with the correlation data arrangement of complexity and is generated every Performance Evaluation index;
Data handling system imports to the information in the daily record in the database, and clean, put in order, gather, analyze, finally being aggregated in the summary sheet, summary sheet is a result calculated, also be the Data Source of reporting system, set up special database and handle the information that collects; The table of depositing raw information is in the flowing water table, and the table of depositing information after handling is summary sheet; The data volume of flowing water table is bigger, regularly arrive summary sheet to information transfer, then the data scrubbing in the flowing water table is fallen, and summary sheet divides day to gather, week gathers, peak value gathers etc.; It is that data are weekly gathered by the hour that peak value gathers, and purpose is the distribution situation of system load in the record one day;
(3) reporting system is in the same place the Performance Evaluation index comprehensive of operating system, software middleware, application system and database various aspects, adopt the unified method record, store and represent, generation demonstrates fully the synthetic performance evaluation of system's operation various aspects of performance index and describes report, will report and regularly report and submit system manager and relevant leader to make it adopt system performance comprehensive assessment algorithm that software systems are carried out synthetic performance evaluation;
Reporting system mainly is according to the information in summary sheet or the log sheet, presses theme, reader's difference output various report; The key index that reporting system relates to is as follows:
(1) the average degree of reliability MTBR of system, average degree of reliability is meant the ratio of system's failure free time and system operation time;
Computing method are to analyze the GC daily record, search key " JVMST080 ", use occur before the JVMST080 and afterwards the time interval of twice GC add a set time, as 3 minutes stop times as system, the working time of system was from 8:00 to 18:00;
(2) the average response time average response time computing method of system are to analyze the URL daily record, and the start and end time of each URL is accumulated in together, just obtain average response time divided by the number of times of URL;
Also should calculate the average data storehouse response time, method is that analytical database connects daily record, and the duration that all databases are connected is accumulated in together, connects number of times divided by database again;
Be connected totally according to URL sum with database, the database tie-time is shared on each URL, calculate the average url database response time, deduct the average url database response time, obtain average URL application response time with average response time;
(3) internal memory consumes
Analyze the GC daily record, the overall internal memory of Accumulation System consumes, and divided by the work fate, calculates per day internal memory and consumes;
(4) database cost
Analyze the URL_SQL daily record, accumulative total global database cost divided by the work fate, calculates daily average according to Kucheng originally;
(5) throughput analysis
Per hour the URL sum of Zhi Hanging, per hour the db transaction number of Chu Liing, per hour the database transaction number of transactions of Chu Liing;
(6) concurrent user number analysis
Calculate peak value concurrent user number and average concurrent user number according to the situation that URL begins, the concluding time is overlapping, upstairs framework can write down each URL concurrent number at first in the URL_DB daily record, and this has brought convenience for concurrent analysis afterwards;
Also calculate average server free time of every day, single concurrent time, two concurrent time, three concurrent times ... the concurrent time of N.And do a curve, the loading condition of expression system according to these data;
(7) URL micro-analysis
Report the average average response time of each URL, on average connect the number of times of database, average database tie-time;
(8) server load analysis
Can be according to " pressure index " of data computation application servers such as time, internal memory, database cost, concurrent user, handling capacity, database server, pressure index can draw a percentage according to the weight calculation of each index;
(9) server handling ability analysis
With the processing power of integer representation server, can be benchmark with the processing power of one or more servers, represent with integer 10000, calculate the processing power of other main frames.
During describing and report, the combination property of system's operation except comprising throughput of system, average response time, database response time, peak load information, goes back detail record system-down situation, in order to calculate the mean time between failures of software systems.
Illustration:
Table 1 is a project system flow weekly return;
Table 2 is the detailed weekly returns of project system flow;
Watch 3 is the slow request weekly returns of item response;
Table 4 is system performance assessment weekly returns.
5, embodiment:
Method of the present invention is used windows/unix operating system, DB2 or oracle database.The present invention mainly is made of three parts: Information Collection System, data handling system, reporting system.
Information Collection System has designed the complete daily record of a cover, have newly-increased, the daily record that also has the operating system utilized, software middleware, application software, database etc. to carry.
Data handling system imports to the information in the daily record in the database, and cleans, puts in order, gathers, analyzes, and finally is aggregated in the summary sheet.Summary sheet is a result calculated, also is the Data Source of reporting system.
Reporting system mainly is according to the information in summary sheet or the log sheet, presses theme, reader's difference output various report.
(1) information gathering
In the software systems based on J2EE, the effect of application server and database server is different, and the information of collection is also different.Application server will be collected CPU occupied information, solicited message (url_conn.log), JVM heap memory recovery information (native_stderr.log).Database server is collected CPU occupied information, EMS memory occupation information, archive log, database snapshot.
(2) data processing
Set up special database and handle the information that collects.The table of depositing raw information is in the flowing water table, and the table of depositing information after handling is summary sheet.The data volume of flowing water table is bigger, regularly arrive summary sheet to information transfer, then the data scrubbing in the flowing water table is fallen.Summary sheet divides day to gather, week gathers, peak value gathers etc.It is that data are weekly gathered by the hour that peak value gathers, and purpose is the distribution situation of system load in the record one day.
(3) reporting system
Several typical patterns have been provided in the accompanying drawing.The key index that reporting system relates to is as follows:
1. the average degree of reliability (MTBR) of system, average degree of reliability is meant the ratio of system's failure free time and system operation time.
Computing method are to analyze the GC daily record, and search key " JVMST080 " uses JVMST080 to occur before with the time interval of twice GC is added the stop time of a set time (as 3 minutes) as system afterwards.The working time of system is from 8:00 to 18:00.
2. the average response time of system (average response time)
Computing method are to analyze the URL daily record, and the start and end time of each URL is accumulated in together, just obtain average response time divided by the number of times of URL.
Also should calculate the average data storehouse response time, method is that analytical database connects daily record, and the duration that all databases are connected is accumulated in together, connects number of times divided by database again.
Be connected totally according to URL sum with database, the database tie-time is shared on each URL, calculate the average url database response time, deduct the average url database response time, obtain average URL application response time with average response time.
3. internal memory consumes
Analyze the GC daily record, the overall internal memory of Accumulation System consumes, and divided by the work fate, calculates per day internal memory and consumes.
4. database cost
Analyze the URL_SQL daily record, accumulative total global database cost divided by the work fate, calculates daily average according to Kucheng originally.
5. throughput analysis
Per hour the URL sum of Zhi Hanging, per hour the db transaction number of Chu Liing, per hour the database transaction number of transactions of Chu Liing.
6. concurrent user number analysis
Calculate peak value concurrent user number and average concurrent user number according to the situation that URL begins, the concluding time is overlapping.Upstairs framework can write down each URL concurrent number at first in the URL_DB daily record, and this has brought convenience for concurrent analysis afterwards.
Also can calculate average server free time of every day, single concurrent time, two concurrent time, three concurrent times ... the concurrent time of N.And do a curve, the loading condition of expression system according to these data.
7.URL micro-analysis
Report the average average response time of each URL, on average connect the number of times of database, average database connected by the time.
8. server load analysis
Can be according to " pressure index " of data computation application servers such as time, internal memory, database cost, concurrent user, handling capacity, database server.Pressure index can draw a percentage according to the weight calculation of each index.
9. server handling ability analysis
Processing power with the integer representation server.Can be benchmark (representing) with the processing power of one or more servers, calculate the processing power of other main frames with integer 10000.
The week report of * * * project system flow
* * * * year the * * week (* * month * * day-* * month * * day)
Table 1
The detailed week report of * * * project system flow
* * * * year the * * week (* * month * * day-* * month * * day)
Table 2
The slow URL of * * * item response week report
* * * * year the * * week (* * month * * day-* * month * * day)
Table 3
* * * project system Performance Evaluation weekly
* * * * year * * week
Table 4
Claims (2)
1. software system performance estimating method, comprise Information Collection System, data handling system and reporting system, it is characterized in that: from the index of many description software systems operation conditionss, pick out crucial some and write down and store, and designed one and overlapped integration algorithm, be used for the performance of assessment software system, stable situation, by making the decision-making of improvement, upgrading and optimization to software systems with the comparison of historical information approximation system to assist managerial personnel, appraisal procedure comprises:
(1) the acquisition system operation information of gathering each link is analyzed, and information and analysis result stored, trading volume, data volume, peak load to system handles change, at any time compare with historical data, find out discrepancy, in time distribute rationally, for the many cover systems that move simultaneously, data compare between different system, find out the system of poor performance and improve;
Information Collection System comprises the daily record that complete daily record, operating system, software middleware, application software and the database of a cover carries; In the software systems based on J2EE, what application server was collected is that CPU occupied information, solicited message url_conn.log and JVM heap memory reclaim information native_stderr.log; What database server was collected is CPU occupied information, EMS memory occupation information, archive log, database snapshot;
(2) data handling system is calculated the back with the correlation data arrangement of complexity and is generated every Performance Evaluation index;
Data handling system imports to the information in the daily record in the database, and clean, put in order, gather, analyze, finally being aggregated in the summary sheet, summary sheet is a result calculated, also be the Data Source of reporting system, set up special database and handle the information that collects; The table of depositing raw information is in the flowing water table, and the table of depositing information after handling is summary sheet; The data volume of flowing water table is bigger, regularly arrive summary sheet to information transfer, then the data scrubbing in the flowing water table is fallen, and summary sheet divides day to gather, week gathers, peak value gathers etc.; It is that data are weekly gathered by the hour that peak value gathers, and purpose is the distribution situation of system load in the record one day;
(3) reporting system is in the same place the Performance Evaluation index comprehensive of operating system, software middleware, application system and database various aspects, adopt the unified method record, store and represent, generation demonstrates fully the synthetic performance evaluation of system's operation various aspects of performance index and describes report, will report and regularly report and submit system manager and relevant leader to make it adopt system performance comprehensive assessment algorithm that software systems are carried out synthetic performance evaluation;
Reporting system mainly is according to the information in summary sheet or the log sheet, presses theme, reader's difference output various report; The key index that reporting system relates to is as follows:
(1) the average degree of reliability MTBR of system, average degree of reliability is meant the ratio of system's failure free time and system operation time;
Computing method are to analyze the GC daily record, search key " JVMST080 ", use occur before the JVMST080 and afterwards the time interval of twice GC add a set time, as 3 minutes stop times as system, the working time of system was from 8:00 to 18:00;
(2) the average response time average response time computing method of system are to analyze the URL daily record, and the start and end time of each URL is accumulated in together, just obtain average response time divided by the number of times of URL;
Also should calculate the average data storehouse response time, method is that analytical database connects daily record, and the duration that all databases are connected is accumulated in together, connects number of times divided by database again;
Be connected totally according to URL sum with database, the database tie-time is shared on each URL, calculate the average url database response time, deduct the average url database response time, obtain average URL application response time with average response time;
(3) internal memory consumes
Analyze the GC daily record, the overall internal memory of Accumulation System consumes, and divided by the work fate, calculates per day internal memory and consumes;
(4) database cost
Analyze the URL_SQL daily record, accumulative total global database cost divided by the work fate, calculates daily average according to Kucheng originally;
(5) throughput analysis
Per hour the URL sum of Zhi Hanging, per hour the db transaction number of Chu Liing, per hour the database transaction number of transactions of Chu Liing;
(6) concurrent user number analysis
Calculate peak value concurrent user number and average concurrent user number according to the situation that URL begins, the concluding time is overlapping, upstairs framework can write down each URL concurrent number at first in the URL_DB daily record, and this has brought convenience for concurrent analysis afterwards;
Also calculate average server free time of every day, single concurrent time, two concurrent time, three concurrent times ... the concurrent time of N.And do a curve, the loading condition of expression system according to these data;
(7) URL micro-analysis
Report the average average response time of each URL, on average connect the number of times of database, average database tie-time;
(8) server load analysis
Can be according to " pressure index " of data computation application servers such as time, internal memory, database cost, concurrent user, handling capacity, database server, pressure index can draw a percentage according to the weight calculation of each index;
(9) server handling ability analysis
With the processing power of integer representation server, can be benchmark with the processing power of one or more servers, represent with integer 10000, calculate the processing power of other main frames.
2. according to the described method of claim, it is characterized in that the combination property of system's operation is described in the report
Except comprising throughput of system, average response time, database response time, peak load information, go back detail record system-down situation, in order to calculate the mean time between failures of software systems.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710013763 CN101021810A (en) | 2007-03-08 | 2007-03-08 | Software system performance estimating method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 200710013763 CN101021810A (en) | 2007-03-08 | 2007-03-08 | Software system performance estimating method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN101021810A true CN101021810A (en) | 2007-08-22 |
Family
ID=38709584
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 200710013763 Pending CN101021810A (en) | 2007-03-08 | 2007-03-08 | Software system performance estimating method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101021810A (en) |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102236598A (en) * | 2010-05-07 | 2011-11-09 | 腾讯科技（深圳）有限公司 | Method and device for testing software |
| CN102257520A (en) * | 2008-10-16 | 2011-11-23 | 惠普开发有限公司 | Performance analysis of applications |
| CN102426590A (en) * | 2011-07-26 | 2012-04-25 | 乐活在线（北京）网络技术有限公司 | Quality evaluation method and device |
| CN102473132A (en) * | 2009-07-24 | 2012-05-23 | 伦敦大学玛丽女王和威斯特菲尔特学院 | Method of monitoring the performance of a software application |
| CN102752387A (en) * | 2012-06-29 | 2012-10-24 | 用友软件股份有限公司 | Data storage processing system and data storage processing method |
| CN103139007A (en) * | 2011-12-05 | 2013-06-05 | 阿里巴巴集团控股有限公司 | Method and system for detecting application server performance |
| CN105068924A (en) * | 2015-07-29 | 2015-11-18 | 浪潮电子信息产业股份有限公司 | Method and apparatus for testing performance of application |
| CN105141467A (en) * | 2015-09-30 | 2015-12-09 | 国网天津市电力公司 | Information acquisition method of information system running state of large enterprise |
| CN105260253A (en) * | 2015-09-06 | 2016-01-20 | 浪潮集团有限公司 | Server failure measurement and calculation method and device |
| CN105374002A (en) * | 2014-08-20 | 2016-03-02 | 中国移动通信集团广东有限公司 | Formula efficiency assessment method and apparatus for network evaluation index |
| CN105786682A (en) * | 2016-02-29 | 2016-07-20 | 上海新炬网络信息技术有限公司 | Implementation system and method for avoiding software performance failure |
| CN105843703A (en) * | 2015-01-30 | 2016-08-10 | 国际商业机器公司 | Extraction of system administrator actions to a workflow providing a resolution to a system issue |
| CN106604074A (en) * | 2016-12-16 | 2017-04-26 | 四川长虹电器股份有限公司 | Intelligent television user operation index analysis method |
| CN109685089A (en) * | 2017-10-18 | 2019-04-26 | 北京京东尚科信息技术有限公司 | The system and method for assessment models performance |
| CN110196805A (en) * | 2018-05-29 | 2019-09-03 | 腾讯科技（深圳）有限公司 | Data processing method, device, storage medium and electronic device |
| CN110955591A (en) * | 2019-10-18 | 2020-04-03 | 文思海辉智科科技有限公司 | System performance evaluation method and device, computer equipment and storage medium |
| CN112001623A (en) * | 2020-08-21 | 2020-11-27 | 中国建设银行股份有限公司 | Method, system, medium, and device for evaluating health degree of software load balancing |
| CN112783763A (en) * | 2021-01-08 | 2021-05-11 | 广州虎牙科技有限公司 | Software quality detection method and device, electronic equipment and storage medium |
-
2007
- 2007-03-08 CN CN 200710013763 patent/CN101021810A/en active Pending
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102257520A (en) * | 2008-10-16 | 2011-11-23 | 惠普开发有限公司 | Performance analysis of applications |
| CN102257520B (en) * | 2008-10-16 | 2018-02-06 | 慧与发展有限责任合伙企业 | The performance evaluation of application |
| CN102473132A (en) * | 2009-07-24 | 2012-05-23 | 伦敦大学玛丽女王和威斯特菲尔特学院 | Method of monitoring the performance of a software application |
| CN102473132B (en) * | 2009-07-24 | 2014-12-31 | 真实体验有限公司 | Method of monitoring the performance of a software application |
| CN102236598A (en) * | 2010-05-07 | 2011-11-09 | 腾讯科技（深圳）有限公司 | Method and device for testing software |
| CN102236598B (en) * | 2010-05-07 | 2014-04-16 | 腾讯科技（深圳）有限公司 | Method and device for testing software |
| CN102426590A (en) * | 2011-07-26 | 2012-04-25 | 乐活在线（北京）网络技术有限公司 | Quality evaluation method and device |
| CN102426590B (en) * | 2011-07-26 | 2017-05-10 | 百度在线网络技术（北京）有限公司 | Quality evaluation method and device |
| CN103139007A (en) * | 2011-12-05 | 2013-06-05 | 阿里巴巴集团控股有限公司 | Method and system for detecting application server performance |
| CN102752387A (en) * | 2012-06-29 | 2012-10-24 | 用友软件股份有限公司 | Data storage processing system and data storage processing method |
| CN105374002A (en) * | 2014-08-20 | 2016-03-02 | 中国移动通信集团广东有限公司 | Formula efficiency assessment method and apparatus for network evaluation index |
| CN105843703A (en) * | 2015-01-30 | 2016-08-10 | 国际商业机器公司 | Extraction of system administrator actions to a workflow providing a resolution to a system issue |
| US10346780B2 (en) | 2015-01-30 | 2019-07-09 | International Business Machines Corporation | Extraction of system administrator actions to a workflow providing a resolution to a system issue |
| CN105843703B (en) * | 2015-01-30 | 2019-01-15 | 国际商业机器公司 | Workflow is created to solve the method and system of at least one system problem |
| CN105068924A (en) * | 2015-07-29 | 2015-11-18 | 浪潮电子信息产业股份有限公司 | Method and apparatus for testing performance of application |
| CN105260253A (en) * | 2015-09-06 | 2016-01-20 | 浪潮集团有限公司 | Server failure measurement and calculation method and device |
| CN105141467A (en) * | 2015-09-30 | 2015-12-09 | 国网天津市电力公司 | Information acquisition method of information system running state of large enterprise |
| CN105786682A (en) * | 2016-02-29 | 2016-07-20 | 上海新炬网络信息技术有限公司 | Implementation system and method for avoiding software performance failure |
| CN106604074A (en) * | 2016-12-16 | 2017-04-26 | 四川长虹电器股份有限公司 | Intelligent television user operation index analysis method |
| CN109685089A (en) * | 2017-10-18 | 2019-04-26 | 北京京东尚科信息技术有限公司 | The system and method for assessment models performance |
| CN109685089B (en) * | 2017-10-18 | 2020-12-22 | 北京京东尚科信息技术有限公司 | System and method for evaluating model performance |
| CN110196805A (en) * | 2018-05-29 | 2019-09-03 | 腾讯科技（深圳）有限公司 | Data processing method, device, storage medium and electronic device |
| CN110955591A (en) * | 2019-10-18 | 2020-04-03 | 文思海辉智科科技有限公司 | System performance evaluation method and device, computer equipment and storage medium |
| CN110955591B (en) * | 2019-10-18 | 2022-01-14 | 文思海辉智科科技有限公司 | System performance evaluation method and device, computer equipment and storage medium |
| CN112001623A (en) * | 2020-08-21 | 2020-11-27 | 中国建设银行股份有限公司 | Method, system, medium, and device for evaluating health degree of software load balancing |
| CN112783763A (en) * | 2021-01-08 | 2021-05-11 | 广州虎牙科技有限公司 | Software quality detection method and device, electronic equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN101021810A (en) | Software system performance estimating method | |
| Chen | Information valuation for information lifecycle management | |
| US7500150B2 (en) | Determining the level of availability of a computing resource | |
| US6442533B1 (en) | Multi-processing financial transaction processing system | |
| US20030014464A1 (en) | Computer system performance monitoring using transaction latency data | |
| CN1771479A (en) | Method and system of configuring elements of a distributed computing system for optimized value | |
| US7908264B2 (en) | Method for providing the appearance of a single data repository for queries initiated in a system incorporating distributed member server groups | |
| CN102982489A (en) | Power customer online grouping method based on mass measurement data | |
| US20220114073A1 (en) | Systems and methods for modeling computer resource metrics | |
| US8825537B2 (en) | System and method for financial data management and report generation | |
| Vazhkudai et al. | GUIDE: a scalable information directory service to collect, federate, and analyze logs for operational insights into a leadership HPC facility | |
| Shi et al. | Power system data warehouses | |
| CN102411757B (en) | Method and system for forecasting capacity of large host central processing unit (CPU) | |
| US20070239587A1 (en) | System and Method For Dynamically Utilizing and Managing Financial, Operational, and Compliance Data | |
| CN105447069A (en) | BW platform based account checking platform data synchronization method and system | |
| CN110222039A (en) | Data storage and garbage data cleaning method, device, equipment and storage medium | |
| CN112181972A (en) | Data management method and device based on big data and computer equipment | |
| CN115455106B (en) | Power distribution monitoring method, service platform, equipment and storage medium for power distribution operation and maintenance | |
| Patil et al. | Data integration problem of structural and semantic heterogeneity: data warehousing framework models for the optimization of the ETL processes | |
| CN114155076A (en) | Method, device and equipment for checking business data and financial data | |
| CN112231304A (en) | Data processing system and method introducing data warehouse construction technology | |
| CN112732686A (en) | Operation method and device for improving data market based on GP (GP) cluster | |
| CN111177188A (en) | Rapid massive time sequence data processing method based on aggregation edge and time sequence aggregation edge | |
| CN106095656B (en) | A kind of backup of cloud and analysis method and system | |
| Zhang et al. | A multiscaling test of causality effects among international stock markets |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |