CN108197012B - Remote sensing data distributed processing cluster scale and performance measurement method - Google Patents
Remote sensing data distributed processing cluster scale and performance measurement method Download PDFInfo
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Abstract
The invention provides a remote sensing data distributed processing cluster scale and performance measurement method, which comprises the following steps: acquiring configuration information of a single processing node in a cluster; establishing a capability scoring model of the processing nodes; establishing a cluster resource consumption increase S curve model; correcting through multiple iterations to obtain a best fit growth curve model of cluster resource consumption, and solving parameters in the model; calculating to obtain a cluster resource consumption estimated value of a set time period according to the model, and calculating a deviation value with the actually measured resource consumption according to the Bessel deviation evaluation model; and inputting the resource consumption prediction time interval of the cluster processing nodes into the model, calculating resource consumption prediction values, taking the maximum value of the sum of the resource consumption prediction values and the deviation value as the maximum consumption resource, inputting the maximum consumption resource into the capability scoring model of the processing nodes, and calculating to obtain configuration information and cluster gauge modulus required by a single processing node of the cluster. The invention effectively improves the scale and performance prediction and evaluation of the remote sensing data distributed processing cluster.
Description
Technical Field
The invention belongs to the field of remote sensing satellite distributed processing technology, and relates to a remote sensing data distributed processing cluster scale and performance measurement method.
Background
The remote sensing data processing system in China starts late, a plurality of existing ground processing systems use a centralized cluster computing mode to store and process image data, the cluster scale and performance are set according to the construction scale of the ground system, the set is set according to the artificial prior experience, the subjectivity is strong, and no unified standard or model exists, so that the phenomenon that the resource utilization rate of the existing various satellite data ground processing clusters is high or a large amount of resources are idle coexists, the cluster data access efficiency and the processing speed are further reduced, the unified model and standard built by the ground data processing clusters are built, and the cost performance of the utilization of the processing clusters is inevitably improved.
The satellite remote sensing data distributed processing cluster scale setting is mainly used for setting the scale and the performance of a satellite data processing cluster according to data quantity estimation values downloaded by satellites every day or experience of cluster building of an existing ground satellite data processing system. According to the data quantity of the satellites which are downloaded every day, for example, the data processing cluster building mode of the satellites of the environmental series and the daily drawing series in China is adopted; and (4) building a cluster according to the existing ground system, such as a wind and cloud series satellite, an ocean series satellite and the like. In order to realize the construction of an accurate distributed processing cluster, particularly under the requirement of ensuring the stability and reliability of a satellite data processing cluster, the reasonable cluster scale and performance setting are more and more necessary, and strong requirements are provided for improving the economic cost performance of the data processing cluster and reducing the ground engineering scale.
The traditional remote sensing data processing cluster is built to carry out estimation and prediction according to a prior model or daily processing satellite data volume, the prior model is not accurate enough in artificial experience estimation, and can not be updated timely along with the change of other factors influencing the prior model such as iterative update of IT performance, so that the scale and performance of the built distributed processing cluster are lost; estimation of cluster size and performance from daily satellite data volumes processed is supported by theoretical data but has no fixed model references. Firstly, depending on satellite data volume, uncontrollable factors generated in the processing process, such as intermediate result data generated by a certain processing module, are not taken into the whole processing flow in time, so that extra data is generated, and a large deviation occurs in the calculation distributed processing cluster model; in addition, the algorithm lacks a strict theoretical basis as a guide and cannot eliminate theoretical deviation and persuasion; finally, for the condition of severe fluctuation of satellite data, the stability and reliability of the whole distributed processing cluster scale cannot be ensured.
Therefore, how to establish accurate cluster scale and performance setting, the invention utilizes single node configuration and performance, combines the existing ground processing cluster resource consumption to calculate the gompertz curve growth model of cluster resource consumption, and realizes the fine processing cluster and performance setting of engineering services according to the calculation model.
Disclosure of Invention
The invention aims to solve the problem of how to establish accurate cluster scale and performance setting, and ensure the stability and reliability of the whole distributed processing cluster scale by utilizing single node configuration and performance and combining the resource consumption of the existing ground processing cluster.
The technical means for solving the problems are as follows:
the invention provides a remote sensing data distributed processing cluster scale and performance measurement method, which comprises the following steps:
constructing a remote sensing data ground processing cluster and acquiring configuration information of a single processing node in the cluster;
carrying out normalization processing according to the weight of the configuration parameters of the single processing node in the cluster, carrying out dynamic scoring on the processing nodes according to the percentage system, and establishing a capability scoring model of each processing node;
substituting the acquired resource consumption parameters of the processing nodes in the cluster into the established capability scoring model of the processing nodes for standard scoring to establish a cluster resource consumption increase S curve model and calculating curve coefficients in the model;
according to the cluster resource consumption of the processing nodes in a plurality of time intervals, correcting the established cluster resource consumption increase S curve model through multiple iterations to obtain a best fit increase curve model of the cluster resource consumption, and solving parameters in the model;
calculating to obtain a cluster resource consumption estimated value of a set time period according to the obtained best fitting growth curve model of cluster resource consumption, and calculating a deviation value between the cluster resource consumption estimated value and the actually measured resource consumption of the cluster in the set time period according to a Bessel deviation evaluation model;
inputting the resource consumption prediction time interval of the cluster processing nodes into a cluster resource consumption optimal fitting growth curve model, calculating to obtain resource consumption prediction values of the cluster processing nodes in the prediction time interval, taking the maximum value of the sum of the resource consumption prediction values and the deviation value as the maximum consumption resource, inputting the maximum consumption resource into a capability scoring model of the processing nodes, and calculating to obtain configuration information and cluster gauge number required by a single processing node of the cluster.
Further, as a preferred technical solution of the present invention, the configuration information of the single processing node acquired in the method includes: the CPU utilization rate and the main frequency, the memory capacity and the IO performance of the node, the network rate information, the exchange storage area and the hard disk IO information.
Further, as a preferred embodiment of the present invention, the method uses the following formula to calculate the deviation value according to the bessel deviation evaluation model:
wherein m is a deviation value; v is.
Further, as a preferred technical solution of the present invention, the method further includes determining whether the processing capacity of the cluster is satisfied with the configuration information and the cluster size required by the single processing node of the cluster obtained by calculation.
The invention has the following effects:
the invention discloses a method for measuring the scale and performance of a remote sensing data distributed processing cluster, which aims at the scale and performance setting of the remote sensing satellite data processing cluster and designs a Gompertz S curve resource consumption increase model according to the daily cluster resource consumption for production of the existing ground processing cluster.
The method for establishing the distributed cluster resource consumption increase S curve model in the algorithm of the invention is to establish a conversion relation between the scale and the performance of a single node and a cluster by utilizing the performance rating and the rating constraint relation between the performance rating of the single node and the performance of the cluster based on a Gompertz curve model and a Bessel deviation evaluation model, thereby realizing the estimation of the scale of the distributed processing cluster; meanwhile, iteration is carried out by taking the consumption of the cluster resources in different periods of busy hour and idle hour of 3 days as a period and taking 30 days as a period to generate a best fit growth curve model of the cluster resource consumption, and deviation evaluation is carried out on a predicted value and an observed value by using a Bessel deviation evaluation model, so that the scale and performance prediction and evaluation of the remote sensing data distributed processing cluster are effectively improved. The algorithm breaks through the limitation of the traditional prior model and the follow-up processing data volume to the cluster estimation, and provides a more rigorous and ideal technical scheme for processing the cluster with high reliability and high cost performance.
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FIG. 1 is a schematic flow chart of a distributed processing cluster scale and performance measurement method for remote sensing data in the invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the present invention provides a method for measuring scale and performance of a remote sensing data distributed processing cluster, which specifically comprises the following steps:
1. and constructing a remote sensing data ground processing cluster, initializing the cluster, and starting the management node and the processing node.
2. The configuration information of the processing node in the cluster is acquired, and preferably includes parameter information such as CPU utilization and master frequency, memory capacity and IO performance, network rate information, exchange storage area, hard disk IO information, and the like.
3. The weight value of the main frequency processing capacity m of a single CUP is 5, wherein the frequency of the main frequency processing capacity m is 1.2GHz<m<4.2 GHz; the weight of the memory capacity n is 2, wherein 1GB<n<1 TB; the weight of the network transmission rate s is 2, wherein 100Mb<s<10 Gb; the weight of the hard disk IO rate y is 3, wherein 3000Mb is read<y1<5000Mb write 1000Mb<y2<3000 Mb; the exchange buffer has a weight of 3, 256MB of it<c<2 GB; based on the aboveAnd (4) performing weighted normalization processing on the auxiliary data, multiplying the auxiliary data by 100, and establishing a cluster single processing node capability scoring model.
4. And acquiring resource consumption parameters in a set period in the processing cluster, such as parameters of resource consumption, time period distribution condition and the like in a busy and idle period of three days.
5. And (3) substituting the resource consumption parameters in the cluster obtained in the step (4) in the set time period into the cluster single processing node capability scoring model established in the step (3), establishing a stage cluster resource consumption increase S curve model, and calculating a curve coefficient in the model. In this embodiment, three days are taken as a set period, thirty days are taken as a cycle, and according to the maximum resource consumption amount during busy hours every day, the curve correlation unknown coefficient in the cluster resource consumption increase S curve model is calculated, and the curve increase model at this stage is determined.
And recording the predicted resource consumption of the next stage according to the determined cluster resource consumption increase S curve model, carrying out multiple iterations on the cluster resource consumption in busy and idle stages to bring the cluster resource consumption into the cluster resource consumption increase S curve model for correction, regenerating to obtain a cluster resource consumption optimal fitting increase curve model, constructing a new cluster resource consumption optimal fitting increase curve model with higher fitting degree, and solving parameters in the model. In this embodiment, the cluster resource consumption is corrected through multiple iterations in one cycle according to the cluster resource consumption in busy and idle times of multiple time intervals, so as to obtain a wireless approximated best-fit growth curve model of the cluster resource consumption, and obtain the relevant parameters of the curve model at this time.
The specific process is as follows:
(1) analyzing characteristics of a Gompertz curve of cluster resource consumption, comprising: the curve increases slowly in the initial stage and then increases gradually, and when the curve reaches a certain degree, the increase rate decreases gradually and finally approaches a horizontal line.
(2) Determining a general form of the cluster resource consumption growth S-curve model as follows:
k, a and b are unknown constants, and t is a time constant to be predicted; and, K >0, 0< a ≠ 1, 0< b ≠ 1.
The curve model has asymptotes at both ends, the upper asymptote is Y-K, and the lower asymptote is Y-0.
(3) And rewriting the formula I into a logarithmic form by utilizing the correction exponential curve and the characteristics of the correction exponential curve:
(4) and (3) a constant determination method for solving the curve by using the correction curve, wherein three local sums of the observed values are respectively set as S1, S2 and S3:
(5) obtaining the lg a, lg K and b according to a three-sum method:
(6) obtaining the values of a and K by taking the inverse logarithm of the obtained lg a and lg K;
(7) repeatedly and iteratively establishing a Model of the optimal fitting growth S curve of the cluster resource consumption;
(8) and calculating an estimated value of the cluster resource consumption for 30 days by using the Model of the obtained best-fit growth S curve of the cluster resource consumption.
6. And judging whether the resource consumption model of the cluster is met. Otherwise, carrying out observation value of one period, returning to the step 4, and if the best fitting curve model is satisfied, continuing to carry out the following processing.
7. Setting a set time interval to be 30 days, calculating an estimated value of the cluster resource consumption of the set time interval of 30 days by using the obtained best fitting growth S curve Model of the cluster resource consumption, acquiring the measured cluster resource consumption of the set time interval of 30 days, and calculating a deviation value between the estimated value of the cluster resource consumption and the measured cluster resource consumption in the set time interval according to the Bessel deviation evaluation Model; according to the Bessel deviation evaluation model formula:
8. wherein V in formula VII, the difference between the most or value and the observed value, is the difference between the arithmetic mean value and the observed value
L is an observed value, LiIs the observed value of the ith time.
9. According to the calculation in the steps 7 and 8, the deviation value m is m +/-27 resource amount, and the maximum error of any two observation values is mMost preferably± 50 resource amount.
10. Inputting a resource consumption prediction time interval according to cluster processing node hardware configuration information, calculating the maximum resource consumption of a cluster in specified prediction time, and simultaneously giving specific node number and cluster performance indexes; the method specifically comprises the following steps:
(1) inputting a resource consumption prediction time period of the cluster processing nodes to the cluster resource consumption best fit growth curve model obtained in the step 5, and calculating to obtain a resource consumption pre-estimated value of the cluster processing nodes in the prediction time period;
(2) and taking the maximum value of the sum of the resource consumption estimated value of the cluster processing node in the prediction period obtained by the calculation and the deviation value obtained in the step 9.
(3) And (3) inputting the maximum value serving as the maximum consumption resource into the capability scoring model of the processing node obtained in the step (3), calculating to obtain configuration information required by a single processing node of the cluster, and obtaining the specific specification and performance index of the cluster according to the node configuration information.
11. Judging whether the configuration information, the cluster specific specification number and the performance index output by the step 10 and required by the processing node meet the requirements, if so, continuing to execute, otherwise, skipping to the step 4 to continue to execute;
and finally, outputting the configuration information and the specific performance information of the cluster, and ending the processing.
In summary, the method is based on a Gompertz growth curve model, the cluster resource consumption growth curve model is established through iteration according to the cluster resource consumption of one period every three days and one period every thirty days, the evaluation model is corrected by utilizing the Bessel deviation, the deviation range is obtained through the estimated resource consumption measurement and the observed value, and reasonable cluster scale and configuration are recommended according to a single resource estimation model, so that the problems that the traditional remote sensing data processing cluster does not have a configuration standard, the cluster utilization rate is low and resources are wasted due to the fact that the traditional remote sensing data processing cluster is built depending on an empirical model are solved, and the scale and the configuration of the remote sensing ground data processing cluster are facilitated to be standardized and engineered.
It should be noted that the above description is only a preferred embodiment of the present invention, and it should be understood that various changes and modifications can be made by those skilled in the art without departing from the technical idea of the present invention, and these changes and modifications are included in the protection scope of the present invention.
Claims (4)
1. The remote sensing data distributed processing cluster scale and performance measurement method is characterized by comprising the following steps:
constructing a remote sensing data ground processing cluster and acquiring configuration information of a single processing node in the cluster;
carrying out normalization processing according to the weight of the configuration information of the single processing node in the cluster, carrying out dynamic scoring on the processing nodes according to the percentage system, and establishing a capability scoring model of each processing node;
substituting the acquired resource consumption parameters of the processing nodes in the cluster into the established capability scoring model of the processing nodes for standard scoring to establish a cluster resource consumption increase S curve model and calculating curve coefficients in the model;
according to the cluster resource consumption of the processing nodes in a plurality of time intervals, correcting the established cluster resource consumption increase S curve model through multiple iterations to obtain a best fit increase curve model of the cluster resource consumption, and solving parameters in the model;
calculating to obtain a cluster resource consumption estimated value of a set time period according to the obtained best fitting growth curve model of cluster resource consumption, and calculating a deviation value between the cluster resource consumption estimated value and the actually measured resource consumption of the cluster in the set time period according to a Bessel deviation evaluation model;
inputting the resource consumption prediction time interval of the cluster processing nodes into a cluster resource consumption optimal fitting growth curve model, calculating to obtain resource consumption prediction values of the cluster processing nodes in the prediction time interval, taking the maximum value of the sum of the resource consumption prediction values and the deviation value as the maximum consumption resource, inputting the maximum consumption resource into a capability scoring model of the processing nodes, and calculating to obtain configuration information and cluster gauge number required by a single processing node of the cluster.
2. The method for remote sensing data distributed processing cluster size and performance measurement according to claim 1, wherein the configuration information of a single processing node obtained in the method includes: the CPU utilization rate and the main frequency, the memory capacity and the IO performance of the node, the network rate information, the exchange storage area and the hard disk IO information.
3. The method for measuring remote sensing data distributed processing cluster size and performance according to claim 1, wherein the following formula is used for calculating the deviation value according to the Bessel deviation evaluation model in the method:
wherein m is a deviation value; v is the difference between the most probable value and the observed value, and n is the number of observations.
4. The method for distributed processing of cluster size and performance measurement of remote sensing data according to claim 1, further comprising determining whether the cluster computing processing capacity is satisfied for the configuration information and cluster size required for obtaining a single processing node of the cluster by computation.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101374076A (en) * | 2008-10-06 | 2009-02-25 | 中国移动通信集团设计院有限公司 | Method for forecasting and planning server handling ability of telecom business support system |
| CN102169494A (en) * | 2011-04-06 | 2011-08-31 | 北京师范大学 | Distributed remotely-sensed data managing system and method |
| CN105743783A (en) * | 2016-04-12 | 2016-07-06 | 同济大学 | Car-Networking Node Selecting Method based on BS-TS and Autoencoder Network, and Accessibility Routing Mechanism Thereof |
| CN106790099A (en) * | 2016-12-26 | 2017-05-31 | 大连大学 | Charge network survivability Measurement Method based on network survivability entropy |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101374076A (en) * | 2008-10-06 | 2009-02-25 | 中国移动通信集团设计院有限公司 | Method for forecasting and planning server handling ability of telecom business support system |
| CN102169494A (en) * | 2011-04-06 | 2011-08-31 | 北京师范大学 | Distributed remotely-sensed data managing system and method |
| CN105743783A (en) * | 2016-04-12 | 2016-07-06 | 同济大学 | Car-Networking Node Selecting Method based on BS-TS and Autoencoder Network, and Accessibility Routing Mechanism Thereof |
| CN106790099A (en) * | 2016-12-26 | 2017-05-31 | 大连大学 | Charge network survivability Measurement Method based on network survivability entropy |
Non-Patent Citations (1)
| Title |
|---|
| 遥感卫星在生态环境监测领域的应用满足度分析;胡沅等;《技术进展》;20141231;第1-9页 * |
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