CN106951351A - A kind of database loads tendency monitoring method - Google Patents

A kind of database loads tendency monitoring method Download PDF

Info

Publication number
CN106951351A
CN106951351A CN201710028002.5A CN201710028002A CN106951351A CN 106951351 A CN106951351 A CN 106951351A CN 201710028002 A CN201710028002 A CN 201710028002A CN 106951351 A CN106951351 A CN 106951351A
Authority
CN
China
Prior art keywords
baseline
database
resource
sql
year
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
Application number
CN201710028002.5A
Other languages
Chinese (zh)
Inventor
程永新
孙玉颖
胡杰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY Co Ltd
Original Assignee
SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY Co Ltd filed Critical SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY Co Ltd
Priority to CN201710028002.5A priority Critical patent/CN106951351A/en
Publication of CN106951351A publication Critical patent/CN106951351A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3003Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3003Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3024Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a central processing unit [CPU]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3003Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3037Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is a memory, e.g. virtual memory, cache
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/3003Monitoring arrangements specially adapted to the computing system or computing system component being monitored
    • G06F11/3041Monitoring arrangements specially adapted to the computing system or computing system component being monitored where the computing system component is an input/output interface
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/32Monitoring with visual or acoustical indication of the functioning of the machine
    • G06F11/324Display of status information
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording 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
    • G06F11/3466Performance evaluation by tracing or monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/21Design, administration or maintenance of databases

Abstract

The invention discloses a kind of database loads tendency monitoring method, including:Step S1:By configuring timing tasks, timed collection database performance data, and set up resource baseline;Step S2:Database performance data after collection is filtered, the year-on-year trend and ring that each resource of monitoring is used compare trend;Step S3:For the resource not in baseline library, it is pushed in baseline library correction, by system, dynamically generation baseline restores baseline library again;Step S4:Resource for having set up baseline, the performance data collected and baseline are contrasted, if performance change is without departing from specified threshold, baseline corrector are collected, for baseline correction;Otherwise, if performance change exceeds specified threshold, to the resource using sending performance early warning.The present invention can give warning in advance to database performance automatically, reduce fault rate, and meet the standardization of magnanimity machine, hardware and software platform management and monitoring demand.

Description

A kind of database loads tendency monitoring method
Technical field
Supervised the present invention relates to a kind of monitoring method of resource for computer system, more particularly to a kind of database loads tendency Prosecutor method.
Background technology
Information technology turns into a kind of vital productivity of telecommunications industry, and the quality of operation system directly influences enterprise The condition of production of industry.Database layer is a wherein the most key ring in current traditional forms of enterprises framework, how to find data in advance Storehouse performance issue simultaneously gives effective solution, is the highly important responsibility of business support department one of which.
In the case, during system maintenance, one or more kinds of combine in following several schemes is typically taken The failure that more or less database layer triggers.Fig. 1 is referred to, prior art mainly there are following three kinds:
1), using third party software (BOMC) system
Third party software (Business Operation Management Center), it is transported there is provided some databases Row index, such as enter number of passes, cpu memory usages, sql and call situation etc..Specific monitor control index is designed or many according to manufacturer Or there is little bit different less.
When the operation of certain class index reaches default monitoring threshold values, seriously put level policy with reference to configuration and alerted, it is ensured that Problem responds promptness, needs manual intervention to handle.
2)、Enterprise Manager
Enterprise manager is database management tools, and it provides a graphic user interface (GUI).Enterprise manager leads to Wieldy interface is crossed there is provided database management function.Database can be carried out by using Enterprise Manager Related maintenance work centralized management.
Using deployment Enterprise Manager (EM) can real-time query Data Data dictionary carry out core resource make Counted with situation, when certain class index exceedes alarm threshold value, then carry out red early warning, need manual intervention to handle.
3), manual inspection is handled
By manually periodically carrying out calling the inspection of the service conditions such as situation, and root into number of passes, cpu memory usages, sql According to service condition carry out manual intervention processing, it is ensured that the stabilization of database and efficiently.
Current telecommunications level database system, with the increase of client, the growth of business and the accumulation of time, system list Position time online transaction number and the complexity of every transaction have large-scale lifting, so also result in Database Systems Data change it is more frequent, also necessarily cause the lifting to data management requirement.Therefore core database is carried out perspective Performance evaluation is particularly important.
Prior art has the following disadvantages:
1) species is various, and security is poor
The BOMC systems of current all big enterprises are similar, but the monitor control index preciseness of each manufacturer, promptness, accuracy There are still a large amount of problems, and to the supports of various platforms nor very friendly.
2) extra performance cost
Enterprise Manager only play the monitoring effect of associated core resource, the data when cpu utilization rates 100% Storehouse is already affected, it is impossible to avoid the generation of failure, and deployment Enterprise Manager can produce extra performance and hold Pin.
3) experience is relied on
It is very high that requirement in database D BA is checked by artificial nucleus.Database D BA experiences are abundanter, then more can guarantee that Accuracy.Change for database is very careful, once core sql executive plans, which change, to trigger serious performance Problem.
, due to the mechanism of its realization, all there is same one, it is necessary to people during generation problem in three of the above technology Work intervention analysis is simultaneously operated, this series of operation, that is, needs the substantial amounts of time, it is also desirable to engineer's hand with rich experiences Work completion handle, to a certain extent the time of troubleshooting be highly dependent on the profile of engineer and to environment Familiarity.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of database loads tendency monitoring method, can be automatically right Database performance is given warning in advance, and reduces fault rate, and meet the standardization of magnanimity machine, hardware and software platform management and monitoring Demand.
The present invention is to provide a kind of database loads tendency prison to solve the technical scheme that above-mentioned technical problem is used Prosecutor method, including:Step S1:By configuring timing tasks, timed collection database performance data, and set up resource baseline;Step Rapid S2:Database performance data after collection is filtered, the year-on-year trend and ring that each resource of monitoring is used compare trend;Step S3:For the resource not in baseline library, it is pushed in baseline library correction, by system, dynamically generation baseline restores baseline again Storehouse;Step S4:Resource for having set up baseline, the performance data collected and baseline are contrasted, if performance change Without departing from specified threshold, then baseline corrector is collected, for baseline correction;Otherwise, if performance change exceeds specified threshold, Then to the resource using sending performance early warning.
Above-mentioned database loads tendency monitoring method, wherein, the database performance data in the step S1 includes Host CPU utilization rate, IO utilization rates, memory usage, the sql amounts of calling, logic are read and sql analysis feature data, the step S2 carries out real time monitoring to database performance data, and is contrasted with reference to history cycle data.
Above-mentioned database loads tendency monitoring method, wherein, the step S2 carries out sql tune on the whole in database Consumption is collected, and monitoring judges whether operation layer changes;Read by periodic collection logic, with reference to the sql amount of calling decision logics It is to be caused by fragment or the amount of calling causes to read change;And enter the database performance data formation real time load figure of timed collection Row display directly perceived and judgement.
Above-mentioned database loads tendency monitoring method, wherein, the step S1 timings are regarded from Database Dynamic performance Sql analysis feature data are gathered in figure, the Database Dynamic performance views include V $ SQL, V $ SQL_PLAN and V $ SQLARER;The sql analysis features data include PLAN_HASH_VALUE and PLAN_COST, if the current PLAN_ of system HASH_VALUE values change compared with history same period PLAN_HASH_VALUE values, then PLAN_COST values current to system with History same period PLAN_COST value progress is sought difference and is compared with default cost threshold values, if beyond cost threshold values, generation is same Than trending early warning message;If the current PLAN_HASH_VALUE values of system become compared with last PLAN_HASH_VALUE values Change, then PLAN_COST values current to system are sought difference with last issue PLAN_COST value progress and are compared with default cost threshold values, If beyond cost threshold values, generation ring is than trending early warning message.
Above-mentioned database loads tendency monitoring method, wherein, when generating year-on-year trending early warning message or ring compares trend After early warning message, also including by short message or lettergram mode, year-on-year trending early warning message or ring are pushed than trending early warning message To attendant.
Present invention contrast prior art has following beneficial effect:The database loads tendency monitoring side that the present invention is provided Method, can give warning in advance to database performance automatically, reduce fault rate, and meet magnanimity machine standardization, Hardware and software platform management and monitoring demand.
Brief description of the drawings
Fig. 1 is that existing database loading trends monitor schematic flow sheet;
Fig. 2 is database loads trend monitoring schematic flow sheet of the present invention;
Fig. 3 is database loads trend monitoring system architecture schematic diagram of the invention;
Fig. 4 parses the year-on-year trend analysis schematic flow sheet of data for the present invention to database sql;
Fig. 5 parses data ring to database sql than trend analysis schematic flow sheet for the present invention.
Embodiment
The invention will be further described with reference to the accompanying drawings and examples.
Fig. 2 is database loads trend monitoring schematic flow sheet of the present invention.
Fig. 2 is referred to, overall flow of the invention is realized as follows:
Step S1:By configuring timing tasks, timed collection database performance data, and set up resource baseline;
Step S2:Database performance data after collection is filtered, year-on-year trend and ring that each resource is used is monitored Compare trend;
Step S3:For the resource not in baseline library, it is pushed in baseline library correction, baseline is dynamically generated again by system Restore baseline library;
Step S4:Resource for having set up baseline, the performance data collected and baseline are contrasted, if performance Change then collects baseline corrector, for baseline correction without departing from specified threshold;Otherwise, if performance change is beyond specified Threshold values, then to the resource using sending performance early warning.
The deficiency of the existing three kinds of schemes of labor of the present invention, solves emphatically problems with:
Host resource is analyzed:Periodically to main frame IO, cpu, internal memory, etc. database related resource be acquired, according to number According to year-on-year ring is carried out than computing, bottom problem is strangled in cradle.
Database resource is analyzed:Can in database running, periodically to the database sql amounts of calling, logic read, The keystone resourceses such as sql parsings are collected and form real time load figure, and the load change of database just can be very clear.
The present invention is broadly divided into four layers to realize, as shown in Figure 3:
First, initialization layer:
The management of this layer of main responsible task, establishment, cancellation, renewal, deletion, inquiry and the backstage scheduling for mainly having task Deng operation.Monitor task is defined, definable is once performed or repeatedly circulation is performed, by filter kernel resource, by key message List is added, is recorded in information gathering storehouse.
2nd, information gathering layer:
Special baseline library is set up, for depositing keystone resources.Two major classes are included in baseline library:
Host resource:
1st, cpu information
By periodic collection main frame cpu utilization rate information, such as GT/s QPI, Run queue, process are carried out real-time Property monitoring contrasted with reference to history cycle data.
2nd, IO information
Pass through periodic collection main frame IO use informations, such as Disk device, %busy, r+w/s, avwait, avserv Deng, carry out real time monitoring combination history cycle data contrasted.
3rd, Mem information
Pass through periodic collection host memory information, such as free, page in, page out, swap in, swap out. Real time monitoring combination history cycle data are carried out to be contrasted.
Database resource:
1st, the Sql amounts of calling
The sql amounts of calling are carried out on the whole in database to collect, and can intuitively find whether operation layer changes, if go out Now exception sql is called.
EXECUTIONS
FETCHES
RUNTIME
2nd, Sql is parsed
Because database sql has a variety of executive plans, spy carries out specific parsing Data Collection for specific sql to be ensured to hold Row plan uniformity
PLAN_HASH_VALUE
CHILD_CURSOR
VERSION
3rd, logic is read
Read by periodic collection logic, and can intuitively find that logic reads change with reference to the sql amounts of calling is caused also by fragment It is that the amount of calling causes.
LOGICAL_READS
LOGICAL_BYTES
LOGICAL_WAIT
3rd, data analysis layer:
Reached in advance by carrying out analysis to resources such as main frame IO, cpu, internal memory, the sql amounts of calling, logic reading, sql parsings Pinpoint the problems
3.1 year-on-year trend
Example sql parses the algorithm of the year-on-year trending early warnings of PLAN_HASH_VALUE:
F=N0-N (N1, N2, N3 ... .Nn)=0
F1=N0cost-N (N1cost, N2cost, N3cost ... Nncost)>fg
Wherein, N0 is the current sql PLAN_HASH_VALUE values of system, and Nn represents history same period sql PLAN_HASH_ VALUE values;When F is not equal to 0, i.e. explanation sql PLAN_HASH_VALUE values change within the year-on-year phase, and Sql there may be Performance issue.
Difference and and fg are asked again according to N0cost (current sql plan cost values) and history same period N1cost value progress (cost threshold values) analyzes sql performance cost excursions, as shown in Figure 4.The Production trend early warning message if F1 is more than fg.In advance After warning message generation, by modes such as short message, mails, early warning message is pushed to attendant, advanced processing hidden danger, in order to avoid Trigger failure.Similarly, this is no longer going to repeat them for other monitor control indexs.
3.2 rings compare trend
Algorithm of the example sql parsing PLAN_HASH_VALUE rings than trending early warning:
F=N0-N1=0
F1=N0cost-N1cost>fg
Wherein, N0 is the current sql PLAN_HASH_VALUE values of system, and N1 represents last issue sql PLAN_HASH_VALUE Value;When F is not equal to 0, i.e. explanation sql PLAN_HASH_VALUE values in the phase in ring than changing, and Sql there may be performance Problem.
Again according to N0cost (current sql plan cost values) and last issue N1cost value progress ask it is poor and with fg (cost Threshold values) analyze sql performance cost excursions, as shown in Figure 5.The Production trend early warning message if F1 is more than fg.Precaution alarm After text generation, by modes such as short message, mails, early warning message is pushed to attendant, advanced processing hidden danger, in order to avoid trigger Failure.Other monitor control indexs are similarly.
4th, scheme implementation level:
After main frame, the analysis of database related resource service condition are finished, scheme implementation level is responsible for:
Problem is found in advance, by constantly analyzing after main frame, database associated core resource, can Real-time Feedback go out currently Loading condition, early warning is carried out if downward trend occurs in load in advance.
Problem advanced processing, is given warning in advance after hidden danger by the present apparatus, and operation maintenance personnel has grace time to carry out resource coordination, And carried out according to specific warning index for processing, in order to avoid trigger failure.
In summary, the present invention is read by periodic collection main frame IO, cpu, internal memory, the sql amounts of calling, logic, sql is parsed etc. Resource, sets up baseline library and builds the links such as information matrix;By analyzing related resource service condition and making with reference to index of correlation Reached with trend analysis and pinpoint the problems and give warning in advance in advance, so as to be given warning in advance automatically to database performance, drop Low fault rate, and meet the standardization of magnanimity machine, hardware and software platform management and monitoring demand.Specific advantage is as follows:
1) standardize:Because main frame is the host platform of database, present apparatus spy combines database and carries out full side with main frame The index of position combs and implements collection, integration specification unified management.
2) hardware and software platform:Because enterprise-level platform is all hundreds and thousands of set of environments, the baseline analysis center energy of present apparatus independence It is enough to manage magnanimity machine easily.Greatly reduce the manpower and materials demand of BOSS grades of O&Ms of telecommunications.
3) it is perspective:Early warning is produced before performance issue appearance, operation maintenance personnel has grace time to carry out resource coordination, And be accurately positioned for processing according to specific warning index, reduce fault rate.
Although the present invention is disclosed as above with preferred embodiment, so it is not limited to the present invention, any this area skill Art personnel, without departing from the spirit and scope of the present invention, when a little modification can be made and perfect, therefore the protection model of the present invention Enclose when by being defined that claims are defined.

Claims (5)

1. a kind of database loads tendency monitoring method, it is characterised in that comprise the following steps:
Step S1:By configuring timing tasks, timed collection database performance data, and set up resource baseline;
Step S2:Database performance data after collection is filtered, the year-on-year trend and ring ratio that each resource of monitoring is used become Gesture;
Step S3:For the resource not in baseline library, it is pushed in baseline library correction, by system, dynamically generation baseline is restored again To baseline library;
Step S4:Resource for having set up baseline, the performance data collected and baseline are contrasted, if performance change Without departing from specified threshold, then baseline corrector is collected, for baseline correction;Otherwise, if performance change exceeds specified threshold, Then to the resource using sending performance early warning.
2. database loads tendency monitoring method as claimed in claim 1, it is characterised in that the data in the step S1 Storehouse performance data includes host CPU utilization rate, IO utilization rates, memory usage, the sql amounts of calling, logic and read and sql analysis features Data, the step S2 carries out real time monitoring to database performance data, and is contrasted with reference to history cycle data.
3. database loads tendency monitoring method as claimed in claim 2, it is characterised in that the step S2 is in database Carry out the sql amounts of calling on the whole to collect, monitoring judges whether operation layer changes;Read by periodic collection logic, with reference to sql It is to be caused by fragment or the amount of calling causes that the amount of calling decision logic, which reads change,;And by the database performance data shape of timed collection Carry out intuitively showing and judging into real time load figure.
4. database loads tendency monitoring method as claimed in claim 2, it is characterised in that the step S1 is regularly from number According to sql analysis feature data are gathered in the dynamic performance views of storehouse, the Database Dynamic performance views include V $ SQL, V $ SQL_ PLAN and V $ SQLARER;The sql analysis features data include PLAN_HASH_VALUE and PLAN_COST, if system is worked as Preceding PLAN_HASH_VALUE values change compared with history same period PLAN_HASH_VALUE values, then PLAN_ current to system COST values are sought difference with history same period PLAN_COST value progress and are compared with default cost threshold values, if beyond cost threshold values, Then generate year-on-year trending early warning message;If the current PLAN_HASH_VALUE values of system and last issue PLAN_HASH_VALUE value phases Than changing, then PLAN_COST values current to system are sought difference with last issue PLAN_COST value progress and entered with default cost threshold values Row is compared, if beyond cost threshold values, generation ring is than trending early warning message.
5. database loads tendency monitoring method as claimed in claim 4, it is characterised in that when the year-on-year trending early warning of generation After message or ring are than trending early warning message, also including by short message or lettergram mode, year-on-year trending early warning message or ring ratio are become Gesture early warning message is pushed to attendant.
CN201710028002.5A 2017-01-16 2017-01-16 A kind of database loads tendency monitoring method Pending CN106951351A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710028002.5A CN106951351A (en) 2017-01-16 2017-01-16 A kind of database loads tendency monitoring method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710028002.5A CN106951351A (en) 2017-01-16 2017-01-16 A kind of database loads tendency monitoring method

Publications (1)

Publication Number Publication Date
CN106951351A true CN106951351A (en) 2017-07-14

Family

ID=59465350

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710028002.5A Pending CN106951351A (en) 2017-01-16 2017-01-16 A kind of database loads tendency monitoring method

Country Status (1)

Country Link
CN (1) CN106951351A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108388503A (en) * 2018-02-13 2018-08-10 中体彩科技发展有限公司 Data-base performance monitoring method, system, equipment and computer readable storage medium
CN109271373A (en) * 2018-09-14 2019-01-25 上海新炬网络信息技术股份有限公司 A kind of automation MYSQL performance capturing analysis method
CN109727897A (en) * 2018-12-29 2019-05-07 上海华力微电子有限公司 Defect monitoring analysis system and method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677934A (en) * 2004-03-31 2005-10-05 华为技术有限公司 Method and system for monitoring network service performance
CN103412911A (en) * 2013-08-02 2013-11-27 中国工商银行股份有限公司 Method and device for monitoring performance of database system
US20150066772A1 (en) * 2009-12-01 2015-03-05 Bank Of America Corporation Integrated risk assessment and management system
CN104699807A (en) * 2015-03-23 2015-06-10 上海新炬网络信息技术有限公司 Automatic monitoring and expansion method for ORACLE data table space
CN104820630A (en) * 2015-05-22 2015-08-05 上海新炬网络信息技术有限公司 System resource monitoring device based on business variable quantity

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1677934A (en) * 2004-03-31 2005-10-05 华为技术有限公司 Method and system for monitoring network service performance
US20150066772A1 (en) * 2009-12-01 2015-03-05 Bank Of America Corporation Integrated risk assessment and management system
CN103412911A (en) * 2013-08-02 2013-11-27 中国工商银行股份有限公司 Method and device for monitoring performance of database system
CN104699807A (en) * 2015-03-23 2015-06-10 上海新炬网络信息技术有限公司 Automatic monitoring and expansion method for ORACLE data table space
CN104820630A (en) * 2015-05-22 2015-08-05 上海新炬网络信息技术有限公司 System resource monitoring device based on business variable quantity

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108388503A (en) * 2018-02-13 2018-08-10 中体彩科技发展有限公司 Data-base performance monitoring method, system, equipment and computer readable storage medium
CN109271373A (en) * 2018-09-14 2019-01-25 上海新炬网络信息技术股份有限公司 A kind of automation MYSQL performance capturing analysis method
CN109727897A (en) * 2018-12-29 2019-05-07 上海华力微电子有限公司 Defect monitoring analysis system and method

Similar Documents

Publication Publication Date Title
US10592563B2 (en) Batch searches in data fabric service system
US20180075134A1 (en) Defining a new correlation search based on fluctuations in key performance indicators displayed in graph lanes
De Leoni et al. A general process mining framework for correlating, predicting and clustering dynamic behavior based on event logs
US10324773B2 (en) Processing events generated by internet of things (IoT)
US20170124487A1 (en) Systems, methods, and apparatuses for implementing machine learning model training and deployment with a rollback mechanism
CN104915407B (en) A kind of resource regulating method based under Hadoop multi-job environment
US10146592B2 (en) Managing resource allocation in a stream processing framework
US10620612B2 (en) Predictive maintenance and process supervision using a scalable industrial analytics platform
Zhang et al. On complexity and optimization of expensive queries in complex event processing
US9514387B2 (en) System and method of monitoring and measuring cluster performance hosted by an IAAS provider by means of outlier detection
US9842000B2 (en) Managing processing of long tail task sequences in a stream processing framework
US10776719B2 (en) Adaptive key performance indicator thresholds updated using training data
US10193775B2 (en) Automatic event group action interface
US10678601B2 (en) Orchestration service for multi-step recipe composition with flexible, topology-aware, and massive parallel execution
US10606711B2 (en) Recovery strategy for a stream processing system
US20180253335A1 (en) Maintaining throughput of a stream processing framework while increasing processing load
US20170329462A1 (en) Graphical user interface for static and adaptive thresholds
Van Der Aalst et al. Process mining put into context
US20180248960A1 (en) Automated service discovery in i.t. environments with entity associations
US20180241660A1 (en) Adjusting weights for aggregated key performance indicators that include a graphical control element of a graphical user interface
US10592093B2 (en) Anomaly detection
US9792169B2 (en) Managing alert profiles
US9557879B1 (en) System for inferring dependencies among computing systems
US10439922B2 (en) Service analyzer interface
WO2016101638A1 (en) Operation management method for electric power system cloud simulation platform

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
CB02 Change of applicant information
CB02 Change of applicant information

Address after: Qingpu 201707 waiqingsong road Shanghai City, No. 588 Lane 7548 Building 1 R zone 1 room 113

Applicant after: Shanghai new torch network information technology Limited by Share Ltd

Address before: Qingpu 201707 waiqingsong road Shanghai City, No. 588 Lane 7548 Building 1 R zone 1 room 113

Applicant before: SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY CO., LTD.

Address after: Qingpu 201707 waiqingsong road Shanghai City, No. 588 Lane 7548 Building 1 R zone 1 room 113

Applicant after: Shanghai new torch network information technology Limited by Share Ltd

Address before: Qingpu 201707 waiqingsong road Shanghai City, No. 588 Lane 7548 Building 1 R zone 1 room 113

Applicant before: SHANGHAI XINJU NETWORK INFORMATION TECHNOLOGY CO., LTD.

WD01 Invention patent application deemed withdrawn after publication
WD01 Invention patent application deemed withdrawn after publication

Application publication date: 20170714