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Method and device for predicting system computing resource based on exponential smoothing prediction

A computing resource and exponential smoothing technology, applied in the field of distributed computing, can solve problems such as job failure and insufficient computing resources, and achieve the effect of reducing the probability of task timeout or failure

Inactive Publication Date: 2016-08-31
LETV INFORMATION TECH BEIJING
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Embodiments of the present invention provide a method and device for predicting system computing resources based on exponential smoothing prediction, to solve the problem of job failure caused by insufficient computing resources in the prior art

Method used

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  • Method and device for predicting system computing resource based on exponential smoothing prediction
  • Method and device for predicting system computing resource based on exponential smoothing prediction
  • Method and device for predicting system computing resource based on exponential smoothing prediction

Examples

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Embodiment 1

[0024] figure 1 It is a technical flow chart of Embodiment 1 of the present invention, combined with figure 1 , the embodiment of the present invention mainly includes the following steps:

[0025] Step 101: the central server continuously receives a plurality of monitoring data in the first time period before the current moment;

[0026] Each monitoring data is the data obtained by monitoring the system computing resources of the server in each monitoring period, specifically including the usage of the system computing resources of the server in each monitoring period, and the first time period includes multiple the monitoring period;

[0027] In the embodiment of the present invention, a detection program can be installed as a detector on the server to be monitored so as to monitor the use of system computing resources by the server. Of course, the present invention is not limited to the detection program.

[0028] Time series mining in data mining refers to finding stati...

Embodiment 2

[0035] figure 2 It is a technical flow chart of Embodiment 1 of the present invention, combined with figure 2 , the embodiment of the present invention can be further refined into the following steps:

[0036] Step 201: install a detector on each server to be monitored, and monitor the usage of computing resources of the server, wherein the detector runs in a monitoring cycle;

[0037] The composition of the server includes a processor, hard disk, memory, system bus, etc. Therefore, in the embodiment of the present invention, monitoring the usage of computing resources of the server mainly includes nearly 40 kinds of system data such as CPU, memory, disk, and TCP connection number.

[0038] Step 202: the detector sends the usage monitoring data back to the central server;

[0039] In the embodiment of the present invention, if the monitoring period of the detector is set to one minute, then the detector runs once every minute, and each server in the cluster sends the detec...

Embodiment 3

[0064] image 3 is a schematic diagram of establishing a short-term forecast model in an embodiment of the present invention, combined with image 3 , the embodiment of the present invention further includes the following steps:

[0065] Step 301: Set the first time period as X hours before the current moment, install a detector on each server to be monitored, and monitor the usage of computing resources of the server system, where X belongs to the first range;

[0066] In the embodiment of the present invention, the following example can be cited: the detector is installed on a certain server to predict the CPU occupancy in the next 15 minutes, and the first time period can be set to 1 hour (X=1 ), setting the monitoring period to 1 minute, then within the first time, the central server will receive 60 CPU usage data values ​​sent from the certain server.

[0067] It should be noted that the first range is determined by experience, and the embodiment of the present inventio...

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Abstract

Embodiments of the invention provide a method and a device for predicting system computing resources based on exponential smoothing prediction. The method comprises: a central server continuously receiving a plurality of pieces of monitoring data in a first time period before a current moment, each piece of the monitoring data being data obtained to monitor system computing resources of the server in each monitoring cycle; the central server establishing a computing resource prediction model according to the smoothing values of a single exponent and a quadratic exponent of the plurality of pieces of monitoring data received in the first time period; according to the model, predicting the application amount of system computing resource of the server at a certain moment in a second time period after the current moment, and showing a prediction result. Through collection and prediction of the monitoring data, the method helps a scheduling system to avoid a congestion peak, so as to reduce probability of overtime or failure of a task. The method and the device have great help for using computing resources rationally and ensuring stability of data calculation.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of distributed computing, and in particular to a computing resource prediction method and system based on exponential smoothing prediction. Background technique [0002] In the big data scheduling system, hundreds of tasks are run every day, and the CPU or memory load of the server often occurs at certain moments, which affects the execution of computing tasks. The current monitoring system can obtain the near-real-time usage of resources through statistics, but in actual situations, not only the resource usage of the "last moment", but also the situation that resources may be occupied in the "next moment" are often more meaningful to the scheduling system. Big data computing tasks usually need to run for a long time and consume a lot of computing resources. If the scheduling system can predict whether the "next moment" resources of the cluster can meet its operation needs before the jo...

Claims

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Application Information

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IPC IPC(8): G06F11/30
Inventor 许鹭清陈抒
Owner LETV INFORMATION TECH BEIJING
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