Difference between revisions of "PMQ"
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;<font size="3">Function</font> | ;<font size="3">Function</font> | ||
− | :For device with HDD failure prediction intelligent application, users can know | + | :For device with HDD failure prediction intelligent application, users can know related status information of HDD that RMM agent deliver. Status information contains predictive status of HDD, alert message and corresponding suggestion to help user check or maintain related equipments, then reduce situations that HDD fails in real. |
;<font size="3">Implementation</font> | ;<font size="3">Implementation</font> | ||
− | : | + | :The major function of HDD failure prediction intelligent application in device is beased on some data of Indicative factor (contain statistic of bbnormal power off count, HDD write/read issue and so on) to give predictive status of HDD and alert messages. For predictive status of HDD, it use build-in trained regression analysis model to predict for data of Indicative factor. For alert message, it defines a series of rules by practical test in advance, and regard them as the standard of alert determination. According to two informations, Once users enable scan function in RMM UI, it will show analysis results in current hour for each device. When users focus on one in analysis results , users can know some suggestions for predictive status and alert meassage, and<br/> |
+ | :these suggestions are related with some equipments, so RMM UI also shows these status of equipments (for instance, system CPU temperature and memory loading). for users to check. | ||
== <span style="font-size:large;">Trend Forecasting Monitor</span> == | == <span style="font-size:large;">Trend Forecasting Monitor</span> == |
Revision as of 06:07, 9 March 2017
HDD Health Analysis
- Function
- For device with HDD failure prediction intelligent application, users can know related status information of HDD that RMM agent deliver. Status information contains predictive status of HDD, alert message and corresponding suggestion to help user check or maintain related equipments, then reduce situations that HDD fails in real.
- Implementation
- The major function of HDD failure prediction intelligent application in device is beased on some data of Indicative factor (contain statistic of bbnormal power off count, HDD write/read issue and so on) to give predictive status of HDD and alert messages. For predictive status of HDD, it use build-in trained regression analysis model to predict for data of Indicative factor. For alert message, it defines a series of rules by practical test in advance, and regard them as the standard of alert determination. According to two informations, Once users enable scan function in RMM UI, it will show analysis results in current hour for each device. When users focus on one in analysis results , users can know some suggestions for predictive status and alert meassage, and
- these suggestions are related with some equipments, so RMM UI also shows these status of equipments (for instance, system CPU temperature and memory loading). for users to check.
Trend Forecasting Monitor
- Function
- Predict IPC/IOT Data in Future
- Automatically Monitor Alteration of IPC/IOT Data
- For sepecific device or sensor data(HDD Health, System CPU Usage and so on), RMM offers prediction function(Trend Forecasting Monitor). It can help users know abnormal data status as soon as possible to prevent some damages.Predictive Type of Trend Forecasting Monitor are hourly averages of data, and they contain averages of current hour and even next 3 hours in furtue.
- Otherwise, Trend Forecasting Monitor also offer function of automatical monitoring hourly averages of IPC/IOT data. If it happens situation that hourly average change significantly, RMM will save as events to notify users.
- Predictive Process
- The first condiction is predictive object has at least the newest 48 history hourly averages in Database, and these samples are calculated by time-series prediction algorithm. Time-series prediction algorithm RMM adopts is Exponential Smoothing, and is based on APIs in Weka machinne learning java libary. By the above Process, it can produce values of prediction(averages of current hour and next 3 hours), and Trend Forecasting Monitor takes these values and the newest 3 history hourly averages to compare with hourly averages in yesterday. If there are significant differences between values of prediction and hourly averages in yesterday, the status of analytic result is "Potential Risk". If there are significant differences between the newest 3 history hourly averages and hourly averages in yesterday, the status is "Doubtful Risk". The standard of significant differences is based on Tukey's Range Test, and the formula is: [Q1-3(Q3-Q1), Q3+3(Q3-Q1)]. Q1 and Q3 are the lower and upper quartile of hourly averages in yesterday. If objects are in the above range, there are not significant differences between them, then we can call status of Object is "Normal".
- Predictive Result
- Result for each object contains 3 parts: result status, status information and event record list. First, for result status. except "Potential Risk", "Doubtful Risk" and "Normal" in the above statement, there is a "Data Error" situation, and it means disable predicting because of some data problem. For instance, miss some data in the newest 48 hours or query data from database error. Second, according to result status, web UI will show corresponding information, and major statement is explain reason for status and give some suggestions to users.Finally, event record list will show total records for significant change, then it can help users to trace initial time of abnormal data.