CN116541175A - Big data information processing system and method based on computer - Google Patents
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- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
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Abstract
The invention discloses a big data information processing system and method based on a computer, and belongs to the technical field of computer system optimization. The system comprises a data acquisition module, a data processing module, an operation management module and a data storage module; the data acquisition module is used for acquiring service information, processor temperature information and running state information of the computer; the data processing module establishes a temperature change prediction model according to the service information, and adjusts the temperature change prediction model in real time through the temperature information of the processor; the operation management module is used for adjusting the operation parameters of the heat radiation equipment to adapt to computers in different operation states, analyzing the calculation power utilization rate of each computer through the operation state information, sequencing according to the calculation power utilization rate, dividing the service with the predicted temperature overrun into a plurality of subtasks, and distributing part of subtasks to the computers with low calculation power utilization rate for execution; the data storage module is used for carrying out backup storage on all the information.
Description
Technical Field
The invention relates to the technical field of computer system optimization, in particular to a big data information processing system and method based on a computer.
Background
With the development of informatization, the importance of big data in life is higher and higher, and massive data becomes valuable data through collection, cleaning, conversion and storage. Data centers are locations that are dedicated to processing large amounts of data, including large numbers of computer devices that consume large amounts of computing power and electrical energy to generate large amounts of heat when performing data processing. In order to keep the normal operation of the computer, an efficient heat dissipation and cooling system is required to timely discharge heat generated in the operation process of the equipment, otherwise, the performance of a computer processor is reduced or even damaged, so that the aspect of data safety cannot be ensured.
At present, the heat dissipation and cooling system of the data center mainly uses air cooling, and heat is taken away from the surface of the processor through air flow generated by the heat dissipation fan, so that the temperature of the processor is reduced. The temperature sensor is adopted to sense the temperature of the processor in real time, and the rotating speed of the fan is dynamically regulated, so that the temperature of the processor is controlled in a normal interval. However, in some cases, this approach does not allow for good temperature control, for example: because the rotating speed of the cooling fan is passively regulated along with the temperature of the processor, the regulation is a hysteresis operation, when the service difficulty is increased to cause calculation force sudden increase, the temperature of the processor is rapidly increased in a short time, when the cooling fan monitors the temperature increase and then gradually regulates the rotating speed of the fan, a large amount of heat cannot be rapidly discharged, so that the heat in the processor is stacked, the temperature breaks through a normal interval, and even the degree of hardware damage is achieved; or when the service difficulty is too high due to the fact that the environment temperature is too high, the temperature overrun can be caused by the fact that the computer executes certain service with the greatest probability, but due to the passivity of a heat dissipation mode, the situation cannot be estimated accurately in advance, and therefore potential safety hazards are caused. Therefore, a heat dissipation mode which can actively dissipate heat, prevent in advance and timely handle unreasonable conditions is needed at the present stage, and intelligent heat dissipation adjustment is carried out on a computer of a data center.
Disclosure of Invention
The invention aims to provide a big data information processing system and method based on a computer, which are used for solving the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a big data information processing system based on a computer comprises a data acquisition module, a data processing module, an operation management module and a data storage module.
The data acquisition module is used for acquiring service information, processor temperature information and running state information; the data processing module establishes a temperature change prediction model according to the service information, predicts the temperature change information of the computer processor in the service execution time, and adjusts the temperature change prediction model in real time according to the temperature information of the processor; the operation management module is used for adjusting the operation parameters of the heat radiation equipment to adapt to computers in different operation states, analyzing the calculation power utilization rate of each computer through the operation state information, sequencing according to the calculation power utilization rate, dividing the service with the predicted temperature overrun into a plurality of subtasks, and distributing part of subtasks to the computers with low calculation power utilization rate for execution; the data storage module is used for carrying out backup storage on all the information.
The data acquisition module comprises a service data acquisition unit, a temperature data acquisition unit and an equipment data acquisition unit.
The service data acquisition unit is used for acquiring service data to be executed by all computers in the large data center area, and the service data are acquired through test software installed on each computer. The business data comprises business data volume information and business difficulty information, the quantity of the business data volume determines the length of processing time, and the quantity of business difficulty determines the amount of business consumption of a computer.
The business data volume refers to the number of records that the computer needs to execute, and the longer the number of records is, the longer the execution time is, and the longer the heating time of the computer processor is. Business difficulty refers to the spatial complexity and the temporal complexity of the algorithm used to execute each record, with higher spatial complexity and temporal complexity, greater computational effort being consumed per record executed, and higher computer processor temperature.
The temperature data acquisition unit is used for acquiring temperature information of all computer processors in the large data center area, and temperature information is acquired through temperature sensors arranged on the computer processors.
The equipment data acquisition unit is used for acquiring the running state information of all computers in the large data center area, wherein the running state information comprises the calculation power utilization rate.
Large data centers are typically high-load operations that require processing large amounts of data and requests simultaneously. Depending on the structure and manner of operation of the data center, there may be local high load and other low load conditions. Some computers may handle more requests, while other computers may handle fewer requests. Therefore, the computing power utilization rate of each computer is different, and the heat generated by the computer processor is different.
The data processing module comprises a temperature prediction unit and a model debugging unit.
The temperature prediction unit is used for predicting the temperature of each computer processor in the large data center area. Fitting the relation data of the temperature and the service data in the history record to obtain a relation formula, and establishing a temperature change prediction model according to the service data volume information and the service difficulty information of each computer. The model can show the change trend of the temperature of all computer processors in a large data center area along with the time in the service execution time.
When the computer processor is in a high-load state, the computing power utilization rate is high, and the processor can consume more electric energy, so that more heat is generated. This heat needs to be dissipated through heat dissipation, which can eventually lead to processor damage if the heat dissipation is inefficient or out of time, and the processor temperature increases over time. In addition, the temperature of the processor is increased continuously along with the use time, and the heat dissipation device may lose efficiency due to ageing of the device, dust or fan failure in the long-term use process, so that insufficient heat dissipation is caused.
The model debugging unit is used for calibrating the temperature change prediction model. Acquiring temperature information of each computer processor in real time through a temperature sensor, performing difference value operation on the predicted temperature corresponding to the time, judging whether an operation result is in an error interval, and if the operation result is in the error interval, indicating that the predicted result is accurate and not processing; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the corresponding formula in the temperature change prediction model is adjusted.
Because the performance, the operation time length and various parameters of each computer are different in the big data center, in order to more accurately predict the temperature change condition of each computer, the temperature change prediction model stores the temperature calculation formulas of all the computers, each formula corresponds to one computer, when the operation result is deviated, only the influence parameters of the temperature prediction formulas corresponding to the computers are required to be adjusted, and the temperature prediction formulas corresponding to other computers are not changed.
When the predicted temperature at the same moment is greater than the actual measured temperature, the temperature change prediction model is used for overestimating the temperature change value, and the corresponding influence parameters in the formula are required to be reduced; when the predicted temperature at the same moment is smaller than the actual measured temperature, the temperature change prediction model is used for underestimating the temperature change value, and the corresponding influence parameters in the formula are required to be enlarged.
The operation management module comprises a heat dissipation management unit and a task scheduling unit.
The heat dissipation management unit is used for dynamically adjusting the operation parameters of the heat dissipation equipment; fitting the relation data of the temperature and the operation parameters of the heat radiation equipment in the history record to obtain a relation formula, and dynamically calculating the operation parameters of the heat radiation equipment according to the temperature change trend given by the temperature change prediction model.
The operation parameters of the heat dissipating device generally refer to the rotation speed of the heat dissipating fan, and the high and low rotation speeds of the fan are beneficial and bad:
the high-rotation-speed fan can improve the heat dissipation effect, protect the stable operation of the processor, prolong the service life and maintain the working performance of the processor under the high-load condition. However, at the same time, the fan rotating at a high speed generates a large noise and consumes more electric energy, increasing the electric charge expense, and the heat dissipating fan itself may be damaged due to an excessively high fan rotation speed, resulting in a shortened fan life.
The advantage of low fan rotation speed is that noise and power consumption can be reduced, and electric charge expenditure is reduced. However, at the same time, the low rotation speed of the fan can cause untimely heat dissipation of the processor, thereby causing temperature rise and damaging the processor. In the actual running process, the user-defined adjustment is often carried out according to the actual requirements.
The task scheduling unit is used for distributing the split service again; when the temperature change amplitude given by the temperature change prediction model exceeds a normal interval, the computing power utilization rate of each computer is analyzed through the running state information, the computers are ordered according to the computing power utilization rate, the service with the predicted temperature overrun is split into a plurality of subtasks, and part of subtasks are distributed to computers with low computing power utilization rate for execution.
The heat radiation device is a heat radiation fan, the operation parameter is the fan rotating speed, the fan rotating speed and the temperature are in a direct proportion relation, and the higher the temperature is, the faster the fan rotating speed is, and the more obvious the cooling effect is.
Generally, the higher the fan speed, the better the heat dissipation effect. This is because the main function of the fan is to increase the air flow by generating an air flow, thereby enhancing the heat dissipation effect. When the fan speed is higher, a greater airflow may be generated, drawing heat away from the processor surface in a shorter time, thereby reducing the processor temperature more quickly.
A computer-based big data information processing method, the method comprising the steps of:
s1, collecting service information of all computers in a large data center area;
s2, predicting the temperature change of each computer processor according to the service information;
s3, under the condition that the predicted temperature exceeds the limit, splitting the service in advance to perform relevant processing;
s4, executing a service by a computer, and simultaneously adjusting the temperature change prediction model in real time;
s5, adjusting the operation parameters of the heat dissipation device according to the prediction result.
In S1, the service information refers to service data to be executed by the computer, where the service data includes service data amount information and service difficulty information, and service data acquisition is performed by test software installed on each computer.
In S2, the temperature change of the processor is related to the traffic data volume and the traffic difficulty. The larger the traffic data volume, the longer the execution time and the longer the temperature maintenance time. The greater the business difficulty, the higher the spatial and temporal complexity of the algorithm used, the greater the computer processor power and the higher the processor temperature.
When the processor executes the service, the heat dissipation device synchronously operates, and the temperature of the processor is maintained in a normal interval. When the service is executed, the next service with higher service difficulty level starts to be executed, the space complexity and the time complexity increase to increase the computer load along with algorithm change, the temperature of the processor starts to rise, the running state of the heat radiation equipment is synchronously adjusted, and when the heat radiation speed is not in line with the heating speed, the temperature of the processor exceeds a normal interval to cause temperature overrun.
In order to prevent the occurrence of the condition that the temperature of the processor exceeds the limit, before the processor starts to execute the service, service information is required to be brought into a formula, the temperature change in the service execution time is calculated, whether the predicted temperature exceeds the limit is judged, and the temperature prediction formula is as follows:
W t ＝w a +log a [P+d(D-P)]×h×t
in which W is t The temperature of the processor is expressed when the prediction time is t, and the unit is the temperature; w (w) a The current ambient temperature is represented in degrees celsius; a is a temperature influence coefficient, and the value is more than 1; p represents the power of the processor when the processor does not execute the service, and the unit is watt; d is the service difficulty level and the value intervalIs (0-1)]The method comprises the steps of carrying out a first treatment on the surface of the D is the maximum power of the processor, and the unit is watt; h represents the thermal resistance between the processor and the heat sink in degrees celsius/watt; t represents the duration of the service executed by the processor in seconds.
When the temperature exceeds the limit, the processor usually starts to limit the performance to slow down the temperature rise, but as the temperature at this time reaches the degree that damage can be brought to hardware, and service execution cannot be automatically stopped, the performance is limited to realize the low efficiency of reducing the temperature, the processor cannot be immediately cooled, the temperature still continues to maintain the overrun state within a period of time, and irreversible damage can still be brought to the processor.
In S3, the split service steps are as follows:
s301, searching the calculation force utilization rate of all computers of the big data center, marking all computers which are equal to or smaller than the calculation force threshold value, and sequencing the marked computers according to the calculation force utilization rate from small to large.
S302, under the current temperature, the computer for calculating the predicted temperature overrun predicts the maximum service difficulty N which can be executed in the temperature variation range not exceeding the normal interval, the service is split into two parts according to the calculated service difficulty information, one part is the calculated maximum service difficulty N, the part is processed by the local machine, the other part is the original service difficulty minus the maximum service difficulty N to obtain the allocated service difficulty M, and the next step is carried out to continue to judge.
S303, when judging that the computer with the minimum calculation power utilization rate executes the distribution service difficulty M in the current environment, predicting whether the temperature change amplitude exceeds a normal interval, and if not, transmitting the distribution service difficulty M to the computer for execution; if the predicted temperature variation amplitude of the computer does not exceed the maximum service difficulty R which can be executed in a normal interval, continuously dividing the allocated service difficulty M into two parts, wherein one part is the calculated maximum service difficulty R, the part is processed by the computer, the other part is the allocated service difficulty M minus the maximum service difficulty R to obtain the allocated service difficulty Q, and the judgment and allocation are sequentially carried out from small to large according to the utilization ratio of the calculated force, and the like until the service is completely allocated.
A service often includes a plurality of records, and each record is executed in the same way; splitting the service difficulty refers to analyzing the execution step of each record in the service, finding out the step of splitting calculation, combining the steps of splitting each record into a subtask, and returning a result after the subtask is executed by other computers; the splitting is performed for all records in the service, after the splitting is completed, the service difficulty is reduced, the space complexity and the time complexity of an algorithm used by a computer for executing the service again are reduced, and the consumption and the calculation power are reduced.
Firstly, defining a business target, listing all business processes, including all steps in the processes and required data; secondly, evaluating each business process, determining the difficulty of each process, identifying which business processes can be split and which business processes cannot be split according to the evaluation result, and making a detailed splitting plan for the split business processes according to the business difficulty; and finally, splitting the service according to a splitting plan, splitting the service into a plurality of subtasks with unequal service difficulties, executing the subtasks by other computers, and returning result data after the execution is finished.
In S4, the temperature change prediction model is adjusted to obtain more accurate predicted temperature information, a temperature sensor is used for collecting the temperature information of each computer processor in real time and carrying out difference value operation on the predicted temperature corresponding to the time, whether an operation result is in an error interval or not is judged, if the operation result is in the error interval, the prediction result is accurate, and the operation is not processed; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the temperature influence coefficient in the temperature change prediction model is adjusted.
In S5, the operation parameter of the heat dissipating device refers to the rotation speed of the heat dissipating fan, and the heat dissipating efficiency is adjusted by increasing the rotation speed of the fan; according to the prediction result, when the predicted temperature is smaller than the current temperature, the rotating speed of the fan is not adjusted, and after the service execution is finished, the rotating speed of the fan returns to a set value; when the predicted temperature is greater than the current temperature, the fan rotating speed is increased in advance to realize temperature control, so that the situation that the temperature of the processor breaks through a normal interval due to the fact that the service difficulty is suddenly increased and the fan rotating speed is not adjusted in time is avoided, and the fan rotating speed is calculated according to the following formula:
wherein f is the rotation speed of the cooling fan after adjustment, and the unit is rotation/min; e is the current rotation speed of the cooling fan, and the unit is rotation/min; w (w) yc To predict processor temperature, in degrees celsius; w (w) dq The unit is the current processor temperature in degrees celsius; t (T) yc The unit is minutes for predicting the corresponding time of the temperature; t (T) dq The current time is given in minutes; s is a temperature change speed influence coefficient, and g is a cooling fan rotation speed influence coefficient.
Compared with the prior art, the invention has the following beneficial effects:
1. before a computer executes a service, the temperature of a processor is predicted through service data, the running state of heat dissipation equipment is adjusted in advance according to the temperature change trend, heat dissipation is carried out in an active heat dissipation mode, the condition that the temperature of the processor is suddenly increased due to heat dissipation in a passive mode is prevented, and the temperature of the processor is controlled in a normal interval.
2. Before a computer executes a service, the invention predicts the temperature of a processor through service data, splits the high-difficulty service with possibly overrun temperature, sequentially distributes the service to the computers with lower computing power utilization rate, and executes the service in a distributed mode, thereby reducing the execution pressure of the computer and avoiding the waste of other computer resources.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a schematic diagram of a system and method for processing big data information based on a computer;
FIG. 2 is a flow chart of a system and method for processing big data information based on a computer according to the present invention;
FIG. 3 is a schematic diagram of a business splitting flow based on the system and method for processing big data information of a computer.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides the following technical solutions: a big data information processing system based on a computer comprises a data acquisition module, a data processing module, an operation management module and a data storage module.
The data acquisition module is used for acquiring service information, processor temperature information and running state information; the data processing module establishes a temperature change prediction model according to the service information, predicts the temperature change information of the computer processor in the service execution time, and adjusts the temperature change prediction model in real time according to the temperature information of the processor; the operation management module is used for adjusting the operation parameters of the heat radiation equipment to adapt to computers in different operation states, analyzing the calculation power utilization rate of each computer through the operation state information, sequencing according to the calculation power utilization rate, splitting the service with the predicted temperature overrun into a plurality of subtasks, and distributing part of subtasks to the computers with low calculation power utilization rate for execution; the data storage module is used for carrying out backup storage on all the information.
The data acquisition module comprises a service data acquisition unit, a temperature data acquisition unit and an equipment data acquisition unit.
The service data acquisition unit is used for acquiring service data to be executed by all computers in the large data center area, and the service data are acquired through test software installed on each computer. The business data comprises business data volume information and business difficulty information, the quantity of the business data volume determines the length of processing time, and the quantity of business difficulty determines the amount of business consumption of a computer.
The business data volume refers to the number of records that the computer needs to execute, and the longer the number of records is, the longer the execution time is, and the longer the heating time of the computer processor is. Business difficulty refers to the spatial complexity and the temporal complexity of the algorithm used to execute each record, with higher spatial complexity and temporal complexity, greater computational effort being consumed per record executed, and higher computer processor temperature.
The temperature data acquisition unit is used for acquiring temperature information of all computer processors in the large data center area, and temperature information is acquired through temperature sensors arranged on the computer processors.
The equipment data acquisition unit is used for acquiring the running state information of all computers in the large data center area, wherein the running state information comprises the calculation power utilization rate.
Large data centers are typically high-load operations that require processing large amounts of data and requests simultaneously. Depending on the structure and manner of operation of the data center, there may be local high load and other low load conditions. Some computers may handle more requests, while other computers may handle fewer requests. Therefore, the computing power utilization rate of each computer is different, and the heat generated by the computer processor is different.
The data processing module comprises a temperature prediction unit and a model debugging unit.
The temperature prediction unit is used for predicting the temperature of each computer processor in the large data center area. Fitting the relation data of the temperature and the service data in the history record to obtain a relation formula, and establishing a temperature change prediction model according to the service data volume information and the service difficulty information of each computer. The model can show the change trend of the temperature of all computer processors in a large data center area along with the time in the service execution time.
When the computer processor is in a high-load state, the computing power utilization rate is high, and the processor can consume more electric energy, so that more heat is generated. This heat needs to be dissipated through heat dissipation, which can eventually lead to processor damage if the heat dissipation is inefficient or out of time, and the processor temperature increases over time. In addition, the temperature of the processor is increased continuously along with the use time, and the heat dissipation device may lose efficiency due to ageing of the device, dust or fan failure in the long-term use process, so that insufficient heat dissipation is caused.
The model debugging unit is used for calibrating the temperature change prediction model. Acquiring temperature information of each computer processor in real time through a temperature sensor, performing difference value operation on the predicted temperature corresponding to the time, judging whether an operation result is in an error interval, and if the operation result is in the error interval, indicating that the predicted result is accurate and not processing; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the corresponding formula in the temperature change prediction model is adjusted.
Because the performance, the operation time length and various parameters of each computer are different in the big data center, in order to more accurately predict the temperature change condition of each computer, the temperature change prediction model stores the temperature calculation formulas of all the computers, each formula corresponds to one computer, when the operation result is deviated, only the influence parameters of the temperature prediction formulas corresponding to the computers are required to be adjusted, and the temperature prediction formulas corresponding to other computers are not changed.
When the predicted temperature at the same moment is greater than the actual measured temperature, the temperature change prediction model is used for overestimating the temperature change value, and the corresponding influence parameters in the formula are required to be reduced; when the predicted temperature at the same moment is smaller than the actual measured temperature, the temperature change prediction model is used for underestimating the temperature change value, and the corresponding influence parameters in the formula are required to be enlarged.
The operation management module comprises a heat dissipation management unit and a task scheduling unit.
The heat dissipation management unit is used for dynamically adjusting the operation parameters of the heat dissipation equipment; fitting the relation data of the temperature and the operation parameters of the heat radiation equipment in the history record to obtain a relation formula, and dynamically calculating the operation parameters of the heat radiation equipment according to the temperature change trend given by the temperature change prediction model.
The operation parameters of the heat dissipating device generally refer to the rotation speed of the heat dissipating fan, and the high and low rotation speeds of the fan are beneficial and bad:
the high-rotation-speed fan can improve the heat dissipation effect, protect the stable operation of the processor, prolong the service life and maintain the working performance of the processor under the high-load condition. However, at the same time, the fan rotating at a high speed generates a large noise and consumes more electric energy, increasing the electric charge expense, and the heat dissipating fan itself may be damaged due to an excessively high fan rotation speed, resulting in a shortened fan life.
The advantage of low fan rotation speed is that noise and power consumption can be reduced, and electric charge expenditure is reduced. However, at the same time, the low rotation speed of the fan can cause untimely heat dissipation of the processor, thereby causing temperature rise and damaging the processor. In the actual running process, the user-defined adjustment is often carried out according to the actual requirements.
The task scheduling unit is used for distributing the split service again; when the temperature change amplitude given by the temperature change prediction model exceeds a normal interval, the computing power utilization rate of each computer is analyzed through the running state information, the computers are ordered according to the computing power utilization rate, the service with the predicted temperature overrun is split into a plurality of subtasks, and part of subtasks are distributed to computers with low computing power utilization rate for execution.
The heat radiation device is a heat radiation fan, the operation parameter is the fan rotating speed, the fan rotating speed and the temperature are in a direct proportion relation, the higher the temperature is, the faster the fan rotating speed is, and the more obvious the cooling effect is.
Generally, the higher the fan speed, the better the heat dissipation effect. This is because the main function of the fan is to increase the air flow by generating an air flow, thereby enhancing the heat dissipation effect. When the fan speed is higher, a greater airflow may be generated, drawing heat away from the processor surface in a shorter time, thereby reducing the processor temperature more quickly.
Referring to fig. 2, the present invention provides a method for processing big data information based on a computer, the method comprising the following steps:
s1, collecting service information of all computers in a large data center area;
S2, predicting the temperature change of each computer processor according to the service information;
s3, under the condition that the predicted temperature exceeds the limit, splitting the service in advance to perform relevant processing;
s4, executing a service by a computer, and simultaneously adjusting the temperature change prediction model in real time;
s5, adjusting the operation parameters of the heat dissipation device according to the prediction result.
In S1, the service information refers to service data to be executed by the computer, where the service data includes service data amount information and service difficulty information, and service data acquisition is performed by test software installed on each computer.
In S2, the temperature change of the processor is related to the traffic data volume and the traffic difficulty. The larger the traffic data volume, the longer the execution time and the longer the temperature maintenance time. The greater the business difficulty, the higher the spatial and temporal complexity of the algorithm used, the greater the computer processor power and the higher the processor temperature.
When the processor executes the service, the heat dissipation device synchronously operates, and the temperature of the processor is maintained in a normal interval. When the service is executed, the next service with higher service difficulty level starts to be executed, the space complexity and the time complexity increase to increase the computer load along with algorithm change, the temperature of the processor starts to rise, the running state of the heat radiation equipment is synchronously adjusted, and when the heat radiation speed is not in line with the heating speed, the temperature of the processor exceeds a normal interval to cause temperature overrun.
In order to prevent the occurrence of the condition that the temperature of the processor exceeds the limit, before the processor starts to execute the service, service information is required to be brought into a formula, the temperature change in the service execution time is calculated, whether the predicted temperature exceeds the limit is judged, and the temperature prediction formula is as follows:
W t ＝w a +log a [P+d(D-P)]×h×t
in which W is t The temperature of the processor is expressed when the prediction time is t, and the unit is the temperature; w (w) a The current ambient temperature is represented in degrees celsius; a is a temperature influence coefficient, and the value is more than 1; p represents the power of the processor when the processor does not execute the service, and the unit is watt; d is the service difficulty level, and the value interval is (0-1]The method comprises the steps of carrying out a first treatment on the surface of the D is the maximum power of the processor, and the unit is watt; h represents the thermal resistance between the processor and the heat sink in degrees celsius/watt; t represents the duration of the service executed by the processor in seconds.
When the temperature exceeds the limit, the processor usually starts to limit the performance to slow down the temperature rise, but as the temperature at this time reaches the degree that damage can be brought to hardware, and service execution cannot be automatically stopped, the performance is limited to realize the low efficiency of reducing the temperature, the processor cannot be immediately cooled, the temperature still continues to maintain the overrun state within a period of time, and irreversible damage can still be brought to the processor.
In S3, please refer to fig. 3, which is a service splitting flow chart of the present invention, the specific splitting steps are as follows:
s301, searching the calculation force utilization rate of all computers of the big data center, marking all computers which are equal to or smaller than the calculation force threshold value, and sequencing the marked computers according to the calculation force utilization rate from small to large.
S302, under the current temperature, the computer for calculating the predicted temperature overrun predicts the maximum service difficulty N which can be executed in the temperature variation range not exceeding the normal interval, the service is split into two parts according to the calculated service difficulty information, one part is the calculated maximum service difficulty N, the part is processed by the local machine, the other part is the original service difficulty minus the maximum service difficulty N to obtain the allocated service difficulty M, and the next step is carried out to continue to judge.
S303, when judging that the computer with the minimum calculation power utilization rate executes the distribution service difficulty M in the current environment, predicting whether the temperature change amplitude exceeds a normal interval, and if not, transmitting the distribution service difficulty M to the computer for execution; if the predicted temperature variation amplitude of the computer does not exceed the maximum service difficulty R which can be executed in a normal interval, continuously dividing the allocated service difficulty M into two parts, wherein one part is the calculated maximum service difficulty R, the part is processed by the computer, the other part is the allocated service difficulty M minus the maximum service difficulty R to obtain the allocated service difficulty Q, and the judgment and allocation are sequentially carried out from small to large according to the utilization ratio of the calculated force, and the like until the service is completely allocated.
A service often includes a plurality of records, and each record is executed in the same way; splitting the service difficulty refers to analyzing the execution step of each record in the service, finding out the step of splitting calculation, combining the steps of splitting each record into a subtask, and returning a result after the subtask is executed by other computers; the splitting is performed for all records in the service, after the splitting is completed, the service difficulty is reduced, the space complexity and the time complexity of an algorithm used by a computer for executing the service again are reduced, and the consumption and the calculation power are reduced.
Firstly, defining a business target, listing all business processes, including all steps in the processes and required data; secondly, evaluating each business process, determining the difficulty of each process, identifying which business processes can be split and which business processes cannot be split according to the evaluation result, and making a detailed splitting plan for the split business processes according to the business difficulty; and finally, splitting the service according to a splitting plan, splitting the service into a plurality of subtasks with unequal service difficulties, executing the subtasks by other computers, and returning result data after the execution is finished.
In S4, the temperature change prediction model is adjusted to obtain more accurate predicted temperature information, a temperature sensor is used for collecting the temperature information of each computer processor in real time and carrying out difference value operation on the predicted temperature corresponding to the time, whether an operation result is in an error interval or not is judged, if the operation result is in the error interval, the prediction result is accurate, and the operation is not processed; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the temperature influence coefficient in the temperature change prediction model is adjusted.
In S5, the operation parameter of the heat dissipating device refers to the rotation speed of the heat dissipating fan, and the heat dissipating efficiency is adjusted by increasing the rotation speed of the fan; according to the prediction result, when the predicted temperature is smaller than the current temperature, the rotating speed of the fan is not adjusted, and after the service execution is finished, the rotating speed of the fan returns to a set value; when the predicted temperature is greater than the current temperature, the fan rotating speed is increased in advance to realize temperature control, so that the situation that the temperature of the processor breaks through a normal interval due to the fact that the service difficulty is suddenly increased and the fan rotating speed is not adjusted in time is avoided, and the fan rotating speed is calculated according to the following formula:
wherein f is the rotation speed of the cooling fan after adjustment, and the unit is rotation/min; e is the current rotation speed of the cooling fan, and the unit is rotation/min; w (w) yc To predict processor temperature, in degrees celsius; w (w) dq The unit is the current processor temperature in degrees celsius; t (T) yc The unit is minutes for predicting the corresponding time of the temperature; t (T) dq The current time is given in minutes; s is a temperature change speed influence coefficient, and g is a cooling fan rotation speed influence coefficient.
Embodiment one:
assuming that two computers A and B respectively execute different services, the current environment temperature is 25 ℃, the power of the processor is 30W when the processor does not execute the services, the maximum power of the processor is 500W, the thermal resistance between the processor and the radiator is 0.1 ℃/W, the temperature influence coefficient is 1.2, the difficulty level of the computer A in executing the services is 0.1, the difficulty level of the computer B in executing the services is 0.9, and the temperature change of the processor in the service execution time is respectively predicted before the execution:
predicting the first minute:
and A, computer: w (W) t ＝25+log 1.2 [30+0.1(500-30)]×0.1×60≈58.65℃
And B, computer: w (W) t ＝25+log 1.2 [30+0.9(500-30)]×0.1×60≈68.37℃
Predicting the second minute:
and A, computer: w (W) t ＝25+log 1.2 [30+0.1(500-30)]×0.1×120≈62.45℃
And B, computer: w (W) t ＝25+log 1.2 [30+0.9(500-30)]×0.1×120≈72.17℃
Predicting the third minute:
and A, computer: w (W) t ＝25+log 1.2 [30+0.1(500-30)]×0.1×180≈64.68℃
And B, computer: w (W) t ＝25+log 1.2 [30+0.9(500-30)]×0.1×180≈74.40℃
Predicting the fourth minute:
and A, computer: w (W) t ＝25+log 1.2 [30+0.1(500-30)]×0.1×240≈66.26℃
And B, computer: w (W) t ＝25+log 1.2 [30+0.9(500-30)]×0.1×240≈75.98℃
Predicting the fifth minute:
and A, computer: w (W) t ＝25+log 1.2 [30+0.1(500-30)]×0.1×300≈67.48℃
And B, computer: w (W) t ＝25+log 1.2 [30+0.9(500-30)]×0.1×300≈77.20℃
Assuming a normal temperature interval of 15-75 degrees, then:
the predicted temperature of the computer A is not exceeded, the predicted temperature of the computer B is exceeded, and the business on the computer B is split;
Assuming that the initial rotation speed of the cooling fan is 1500 revolutions per minute, the initial time is 0, the initial temperature of the processor is 25 ℃, the temperature change speed influence coefficient is 0.01, the cooling fan rotation speed influence coefficient is 6000, and the predicted temperature of the computer A is substituted into a formula to calculate the cooling fan rotation speed in each time period:
after one minute, the temperature of the computer processor reaches 58.65 ℃, and the rotating speed of the cooling fan is adjusted in advance to be:
assuming that the current processor temperature is 58.65 ℃, the current cooling fan rotating speed is 3519 revolutions per minute, predicting that the computer processor temperature reaches 62.45 ℃ after one minute, and adjusting the cooling fan rotating speed to be:
assuming a current processor temperature of 62.45 ℃, a current radiator fan rotation speed of 3747 rpm, predicting that the computer processor temperature reaches 64.68 ℃ after one minute, and adjusting the radiator fan rotation speed in advance to be:
assuming that the current processor temperature is 64.68 ℃, the current cooling fan rotating speed is 3880 revolutions per minute, predicting that the computer processor temperature reaches 66.26 ℃ after one minute, and adjusting the cooling fan rotating speed to be:
assuming that the current processor temperature is 66.26 ℃, the current cooling fan rotating speed is 3974 rpm, predicting that the computer processor temperature reaches 67.48 ℃ after one minute, and adjusting the cooling fan rotating speed to be:
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A big data information processing system based on a computer, which is characterized in that: the system comprises a data acquisition module, a data processing module, an operation management module and a data storage module;
the data acquisition module is used for acquiring service information, processor temperature information and running state information; the data processing module establishes a temperature change prediction model according to the service information, predicts the temperature change information of the computer processor in the service execution time, and adjusts the temperature change prediction model in real time according to the temperature information of the processor; the operation management module is used for adjusting the operation parameters of the heat radiation equipment to adapt to computers in different operation states, analyzing the calculation power utilization rate of each computer through the operation state information, sequencing according to the calculation power utilization rate, dividing the service with the predicted temperature overrun into a plurality of subtasks, and distributing part of subtasks to the computers with low calculation power utilization rate for execution; the data storage module is used for carrying out backup storage on all the information.
2. A computer big data based information processing system according to claim 1, wherein: the data acquisition module comprises a service data acquisition unit, a temperature data acquisition unit and an equipment data acquisition unit;
The service data acquisition unit is used for acquiring service data to be executed by all computers in the large data center area, and acquiring the service data through test software installed on each computer; the business data comprises business data volume information and business difficulty information, the quantity of the business data volume determines the length of processing time, and the quantity of business difficulty determines the amount of business consumption of a computer for executing the business;
the temperature data acquisition unit is used for acquiring temperature information of all computer processors in the large data center area, and acquiring the temperature information through temperature sensors arranged on the computer processors;
the equipment data acquisition unit is used for acquiring the running state information of all computers in the large data center area, wherein the running state information comprises the calculation power utilization rate.
3. A computer big data based information processing system according to claim 1, wherein: the data processing module comprises a temperature prediction unit and a model debugging unit;
the temperature prediction unit is used for predicting the temperature of each computer processor in the large data center area; fitting the relation data of the temperature and the service data in the history record to obtain a relation formula, and establishing a temperature change prediction model according to the service data volume information and the service difficulty information of each computer; the model can show the change trend of the temperature of all computer processors in a large data center area along with the time in the service execution time;
The model debugging unit is used for calibrating the temperature change prediction model; acquiring temperature information of each computer processor in real time through a temperature sensor, performing difference value operation on the predicted temperature corresponding to the time, judging whether an operation result is in an error interval, and if the operation result is in the error interval, indicating that the predicted result is accurate and not processing; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the corresponding formula in the temperature change prediction model is adjusted.
4. A computer big data based information processing system according to claim 1, wherein: the operation management module comprises a heat dissipation management unit and a task scheduling unit;
the heat dissipation management unit is used for dynamically adjusting the operation parameters of the heat dissipation equipment; fitting the relation data of the temperature and the operation parameters of the heat radiation equipment in the history record to obtain a relation formula, and dynamically calculating the operation parameters of the heat radiation equipment according to the temperature change trend given by the temperature change prediction model;
the task scheduling unit is used for distributing the split service again; when the temperature change amplitude given by the temperature change prediction model exceeds a normal interval, the computing power utilization rate of each computer is analyzed through the running state information, the computers are ordered according to the computing power utilization rate, the service with the predicted temperature overrun is split into a plurality of subtasks, and part of subtasks are distributed to computers with low computing power utilization rate for execution.
5. The big data information processing method based on the computer is characterized by comprising the following steps:
s1, collecting service information of all computers in a large data center area;
s2, predicting the temperature change of each computer processor according to the service information;
s3, under the condition that the predicted temperature exceeds the limit, splitting the service in advance to perform relevant processing;
s4, executing a service by a computer, and simultaneously adjusting the temperature change prediction model in real time;
s5, adjusting the operation parameters of the heat dissipation device according to the prediction result.
6. The computer big data based information processing method according to claim 5, wherein: in S1, the service information refers to service data to be executed by the computer, where the service data includes service data amount information and service difficulty information, and service data acquisition is performed by test software installed on each computer.
7. The computer big data based information processing method according to claim 5, wherein: in S2, the temperature change of the processor is related to the traffic data volume and the traffic difficulty; the larger the traffic data volume, the longer the execution time, and the longer the temperature maintenance time; the greater the business difficulty, the higher the spatial complexity and the time complexity of the algorithm used, the greater the power of the computer processor, and the higher the processor temperature;
When the processor executes the service, the heat dissipation device synchronously operates, and the temperature of the processor is maintained in a normal interval; when the service execution is finished, starting to execute the service with higher next service difficulty level, increasing the space complexity and the time complexity to cause the increase of the computer load along with the algorithm change, starting to increase the temperature of the processor, synchronously adjusting the running state of the heat radiation equipment, and when the heat radiation speed is not in line with the heating speed, causing the temperature overrun when the temperature of the processor exceeds a normal interval;
in order to prevent the occurrence of the condition that the temperature of the processor exceeds the limit, before the processor starts to execute the service, service information is required to be brought into a formula, the temperature change in the service execution time is calculated, whether the predicted temperature exceeds the limit is judged, and the temperature prediction formula is as follows:
W t ＝w a +log a [P+d(D-P)]×h×t
in which W is t Representing processor temperature, w, at a predicted time t a The method comprises the steps of representing the current environment temperature, wherein a is a temperature influence coefficient, P represents power when a processor does not execute service, D is a service difficulty level, D is the maximum power of the processor, h represents thermal resistance between the processor and a radiator, and t represents service execution time of the processor.
8. The method for processing big data information based on computer according to claim 5, wherein in S3, the splitting service steps are as follows:
S301, searching the calculation force utilization rate of all computers of the big data center, marking all computers which are equal to or smaller than a calculation force threshold value, and sequencing the marked computers according to the calculation force utilization rate from small to large;
s302, under the current temperature, a computer for calculating the predicted temperature overrun predicts the maximum service difficulty N which can be executed in a temperature variation range not exceeding a normal interval, divides the service into two parts according to the calculated service difficulty information, wherein one part is the calculated maximum service difficulty N, the part is processed by the local machine, the other part is the original service difficulty minus the maximum service difficulty N to obtain the allocated service difficulty M, and the next step is carried out to continue to judge;
s303, when judging that the computer with the minimum calculation power utilization rate executes the distribution service difficulty M in the current environment, predicting whether the temperature change amplitude exceeds a normal interval, and if not, transmitting the distribution service difficulty M to the computer for execution; if the predicted temperature variation amplitude of the computer does not exceed the maximum service difficulty R which can be executed in a normal interval, continuously dividing the allocated service difficulty M into two parts, wherein one part is the calculated maximum service difficulty R, the part is processed by the computer, the other part is the allocated service difficulty M minus the maximum service difficulty R to obtain the allocated service difficulty Q, and the judgment and allocation are sequentially carried out from small to large according to the utilization ratio of the calculated force, and the like until the service is completely allocated.
9. The computer big data based information processing method according to claim 5, wherein: in S4, the temperature change prediction model is adjusted to obtain more accurate predicted temperature information, a temperature sensor is used for collecting the temperature information of each computer processor in real time and carrying out difference value operation on the predicted temperature corresponding to the time, whether an operation result is in an error interval or not is judged, if the operation result is in the error interval, the prediction result is accurate, and the operation is not processed; if the temperature is not in the error interval, the prediction result is inaccurate, the operation result which is not in the error interval is substituted into the temperature prediction formula, and the temperature influence coefficient in the temperature change prediction model is adjusted.
10. The computer big data based information processing method according to claim 5, wherein: in S5, the operation parameter of the heat dissipating device refers to the rotation speed of the heat dissipating fan, and the heat dissipating efficiency is adjusted by increasing the rotation speed of the fan; according to the prediction result, when the predicted temperature is smaller than the current temperature, the rotating speed of the fan is not adjusted, and after the service execution is finished, the rotating speed of the fan returns to a set value; when the predicted temperature is greater than the current temperature, the fan rotating speed is increased in advance to realize temperature control, so that the situation that the temperature of the processor breaks through a normal interval due to the fact that the service difficulty is suddenly increased and the fan rotating speed is not adjusted in time is avoided, and the fan rotating speed is calculated according to the following formula:
Wherein f is the rotation speed of the radiator fan after adjustment, E is the current rotation speed of the radiator fan, and w yc To predict processor temperature, w dq T is the current processor temperature yc To predict the temperature corresponding time, T dq S is the temperature change speed influence coefficient, and g is the cooling fan rotation speed influence coefficient.
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