CN115408160A - Data processing system for process monitoring - Google Patents
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Abstract
The invention provides a data processing system for process monitoring, which comprises: a first database, a second database, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of: acquiring target application software and a process feature vector corresponding to the target application software; acquiring an intermediate process feature vector; acquiring a priority list of the process corresponding to the central processing unit according to the process characteristic vector and the intermediate process characteristic vector; acquiring a target central processing unit corresponding to the process according to the priority; acquiring a target node corresponding to the process according to an original node list corresponding to the target central processing unit, and enabling the target node corresponding to the process to execute the process; therefore, on one hand, the process can be bound to different nodes to run; on the other hand, the main process and the sub-process can be independently bound on different nodes, so that resource waste is avoided, the flexibility is high, and the overall operation efficiency of the application software can be improved.
Description
Technical Field
The invention relates to the field of process processing, in particular to a data processing system for process monitoring.
Background
The existing data processing method for process monitoring is mostly completed through the following steps: testing and acquiring the memory amount, the node information and the node amount required by the application software process; matching with the memory amount and the node information required by the application software process according to the existing node information and the node amount in the system; if the matching is successful, calling the node; the method comprises the steps of binding a process of application software on a node of a corresponding system, wherein the software process binding node mainly enables the process to run on a characteristic node or a CPU core, but when the process accesses a memory across nodes, the memory performance is low.
Therefore, some solutions do not bind processes to nodes, but there is also a problem: when the OS (Operating System, abbreviated as "OS") schedules a process, the same process is scheduled to different CPU cores to run, and data in the memory cannot be migrated in real time or the migration cost is high, which causes the process to access the memory across nodes, and the running efficiency is low.
At present, a software process binding node has two modes, (1) binding is carried out in software, namely when the software runs, an interface provided by an OS is used for binding the process to a characteristic node or a CPU core, but the software process binding node is not flexible enough and cannot adapt to different CPUs or machine architectures, and most of software is not bound in the software process binding node; (2) The external tool is used for binding, but the process and the sub-process can only be bound to some same nodes or CPU cores, and if the process and the sub-process are bound to a plurality of nodes, the technical problem that the process is scheduled to run by different nodes still exists.
Disclosure of Invention
Aiming at the technical problems, the technical scheme adopted by the invention is as follows:
a data processing system for process monitoring, the system comprising: a first database, a second database, a processor, and a memory storing a computer program, wherein the first database comprises: the original application software list and the process set corresponding to each original application software in the original application software list, and the second database comprises: when the computer program is executed by the processor, the following steps are realized:
s100, obtaining target application software and a first process set A = { A } corresponding to the target application software from an original application software list 1 ，……，A i ，……，A m }，A i ＝{A i1 ，……，A ij ，……，A in }，A ij Is the ith corresponding to the target application softwareJ =1 \ 8230, and (8230), n and n are the number of the first process characteristic vectors.
S300, acquiring a first CPU list C = { C from an original CPU set 1 ，……，C r ，……，C s }，C r ＝{C r1 ，……，C rg ，……，C rz }，C rg And identifying the corresponding g-th first middle process characteristic vector for the r-th first CPU, wherein r =1 \8230, g =1 \8230, s is the number of the first CPU identifications, and g =1 \8230, z are the number of the first middle process characteristic vectors.
S500, traverse A i And according to A ij And C rg Obtaining A i Corresponding first CPU priority List D i ＝{D i1 ，……，D ir ，……，D is }，D ir Is A i And C r First CPU priority in between.
S700, according to D ir Obtaining A i A corresponding first target CPU.
S900, obtaining A according to the first target CPU and a first original node list corresponding to the first target CPU i Corresponding first target node, such that according to A i The corresponding first target node executes A i A corresponding first process.
The invention has at least the following beneficial effects:
the invention provides a data processing system for process monitoring, which comprises: a first database, a second database, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of: acquiring target application software and a process characteristic vector corresponding to the target application software; acquiring an intermediate process feature vector; acquiring a priority list of a central processing unit corresponding to a process according to the process characteristic vector and the intermediate process characteristic vector; acquiring a target central processing unit corresponding to the process according to the priority; acquiring a target node corresponding to the process according to an original node list corresponding to the target central processing unit, and enabling the target node corresponding to the process to execute the process; therefore, the process can be bound on different nodes to run on one hand, and the main process and the sub-process can be independently bound on different nodes on the other hand, so that resource waste is avoided, the flexibility is high, and the overall running efficiency of the application software can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a process monitoring data processing system according to an embodiment of the present invention, where the process monitoring data processing system executes a computer program to process a parent process.
Fig. 2 is a flowchart of a data processing system for process monitoring executing a computer program to process a sub-process according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment provides a data processing system for process monitoring, which comprises: a first database, a second database, a processor, and a memory storing a computer program, wherein the first database comprises: the original application software list, the process set corresponding to each original application software in the original application software list, the second database includes: the original CPU set and the original node list corresponding to each CPU in the original CPU set, when the computer program is executed by the processor, implement the following steps, as shown in fig. 1 and 2:
s100, obtaining target application software and a first process set A = { A } corresponding to the target application software from an original application software list 1 ，……，A i ，……，A m }，A i ＝{A i1 ，……，A ij ，……，A in }，A ij And the j-th first process feature vector of the ith first process corresponding to the target application software is i =1 \ 8230, m is the number of the first processes, j =1 \ 8230, m 8230, n and n are the number of the first process feature vectors.
Specifically, the step S100 further includes the steps of:
s101 acquiring a preset first process feature type list L = { L = { (L) } 1 ，……，L a ，……，L b }，L a The method is characterized in that a = 1\8230forthe a-th preset process characteristic type, and b are preset process characteristic type numbers.
Specifically, L is stored in the second database.
S103, obtaining an original process set A ' = { A ' corresponding to target application software ' 1 ，……，A' i ，……，A' m }，A' i ＝{A' i1 ，……，A' ic ，……，A' id }，A' ic C =1 \ 8230, d, d is the original process feature vector of the ith first process corresponding to the target application softwareThe number of process feature vectors.
S105, is A' ic Corresponding Process feature type and L a When they are consistent, then A ij ＝A' ic 。
By the above, whether the process is the specific process or not is determined according to the characteristics of the process, and the specific process is favorably bound with the corresponding CPU, so that the execution efficiency of the specific process is improved, the waste of CPU resources is avoided, and the overall operation efficiency of the application software is improved.
Specifically, the following steps are also included after the step S100:
s200, according to A i Obtaining A i Corresponding second set of processes B i ＝{B i1 ，……，B ix ，……，B ip }，B ix ＝{B 1 ix ，……，B y ix ，……，B q ix }，B y ix Is A i And the y second process characteristic vector of the corresponding x second process is x =1 \8230p, 8230p, p is the number of the second processes, and y =1 \8230p, 8230q, q is the number of the second process characteristic vectors.
Specifically, the method further includes the following steps in S200:
s201, obtaining a preset second process feature type list L 0 ＝{L 0 1 ，……，L 0 e ，……，L 0 f }，L 0 e And presetting a second process characteristic type for the e-th process, wherein e =1 \ 8230 \ 8230, and f, f are the number of preset second process characteristic types.
In particular, L 0 Is stored in a second database.
S203, obtaining A i Corresponding original second set of processes B' i ＝{B' i1 ，……，B' ix ，……，B' ip }，B' ix ＝{B' 1 ix ，……，B' h ix ，……，B' k ix }，B' h ix Is A i H =1 \ 8230; k, k is the original second process feature vector of the corresponding xth second processThe number of process feature vectors.
S205, when B' h ix Corresponding second process feature type and L 0 If they are identical, then B y ix ＝B' h ix 。
Specifically, the association relationship between the first process and the second process is a parent-child relationship, preferably, the first process is a parent process corresponding to the second process, and the second process is a child process corresponding to the first process.
In this embodiment, the process with the characteristic type is further refined, and the child processes with the characteristic type corresponding to the parent process are distinguished, so that the parent process and the child processes with the characteristic type can be processed at the same time in a targeted manner, and compared with the existing process of processing the parent process and the child process, the running time of the application software can be reduced, and the overall running efficiency of the application software is improved.
S300, acquiring a first CPU list C = { C from the original CPU set 1 ，……，C r ，……，C s }，C r ＝{C r1 ，……，C rg ，……，C rz }，C rg And identifying the corresponding g-th first middle process characteristic vector for the r-th first CPU, wherein r =1 \ 8230, g =1 \ 8230, s is the number of the first CPU identifications, and g =1 \ 8230, z, z is the number of the first middle process characteristic vectors.
Specifically, the first CPU identifier is an identifier corresponding to a first CPU, where the first CPU is a CPU having an I/O interface.
S500, traverse A i And according to A ij And C rg Obtaining A i Corresponding first CPU priority list D i ＝{D i1 ，……，D ir ，……，D is }，D ir Is A i And C r First CPU priority in between.
Specifically, the step S500 further includes the steps of:
s501, obtaining A ij Corresponding first similarity list F ij ＝{F 1 ij ，……，F r ij ，……，F s ij }，F r ij ＝(F r1 ij ，……，F rg ij ，……，F rz ij )，F rg ij Is A ij And C rg A first similarity therebetween.
S503, traverse F r ij From F r ij To obtain the maximum first similarity as the first intermediate similarity F' r ij 。
S505, according to F' r ij Obtaining D ir Wherein D is ir The following conditions are met:
in this embodiment, the similarity is obtained by comparing the feature vector of the parent process with the feature vector of the first intermediate process corresponding to the first CPU identifier, and the similarity is further averaged, so that the matching degree between the parent process and the CPU can be more accurately obtained, the optimal CPU corresponding to the parent process can be selected, the optimal node corresponding to the parent process can be selected conveniently according to the optimal CPU, and the resource utilization is further maximized.
Specifically, the method further comprises the following steps after the step of S500:
s600, determining a second CPU priority list corresponding to the second process according to the second process feature vector and the first intermediate process feature vector, wherein the method for determining the second CPU priority list corresponding to the second process according to the second process feature vector and the first intermediate process feature vector is consistent with the step S500, and is not repeated here, and further, the step S500 and the step S600 can be executed simultaneously.
In this embodiment, the parent process and the child process can be processed independently at the same time, without mutual interference and mutual influence, thereby saving the running time of the application software and improving the running efficiency of the application software.
S700, according to D ir Obtaining A i The corresponding target CPU.
Specifically, the step S700 further includes the steps of:
s701, when D ir ≥D 0 At the time of from D i The first CPU corresponding to the maximum CPU priority is obtained as A i A corresponding first target CPU, wherein D 0 Is a preset first priority threshold.
S703, when D ir ＜D 0 And then, acquiring a second CPU list from the original CPU set.
In particular, D 0 The value range of (a) is 0.8-1.
Preferably, D 0 The value is 0.9, so that the phenomena that the CPU selection is wrong due to too low threshold setting, or a proper CPU cannot be selected due to too high threshold setting, so that the resource waste is caused, and the running efficiency of software is reduced due to the fact that the CPU cannot be reasonably used can be avoided.
S705, randomly selecting any second CPU from the second CPU list as A i A corresponding first target CPU.
Specifically, the second CPU is a CPU that does not have an I/O interface.
Specifically, the step S700 is followed by the following steps:
and S800, determining a target CPU corresponding to the second process according to the second CPU priority list, wherein the method for determining the target CPU corresponding to the second process according to the second CPU priority list is consistent with the step S700, and is not repeated here, and further, the step S700 and the step S800 can be executed simultaneously.
S900, obtaining A according to the first target CPU and the original node list corresponding to the first target CPU i Corresponding first target node, such that according to A i The corresponding first target node executes A i A corresponding first process.
Specifically, the step S900 further includes the steps of:
s901, according to the first target CPU, acquiring a first original node list H = { H } corresponding to the first target CPU 1 ，……，H t ，……，H u }，H t ＝(H t1 ，……，H tv ，……，H tw ) Wherein H tv T =1 \ 8230u, v =1 \ 8230u, u is the number of first original nodes, v =1 \ 8230w, w is the number of first original node feature vectors, and the tth first original node feature vector corresponding to the first target CPU is the number of first original node feature vectors.
S903, traverse A i According to H t And A i Obtaining A i Corresponding first node priority List H i ＝{H i1 ，……，H it ，……，H iv }，H it Is A i And H t First node priority in between.
Specifically, the step S903 further includes the steps of:
s9031, obtaining A ij Corresponding third similarity list K ij ＝{K 1 ij ，……，K t ij ，……，K u ij }，K t ij ＝(K t1 ij ，……，K tv ij ，……，k tw ij )，K tv ij Is A ij And H tv A third similarity therebetween.
S9033, traverse K t ij From K to K t ij Obtaining the maximum third similarity as the third intermediate similarity K' t ij 。
S9035 according to K' t ij Obtaining H it Wherein H is it The following conditions are met:
in this embodiment, the similarity is obtained by comparing the feature vector of the parent process with the feature vector of the first original node corresponding to the first original node, and the similarity is further averaged, so that the matching degree between the parent process and the first original node can be more accurately obtained, and the selection of the optimal first original node corresponding to the parent process is facilitated.
S905, from H i To obtain the largest H it The corresponding node is taken as A i A corresponding first initial target node.
Specifically, the step S905 further includes the following steps:
and S9051, when the first initial target node is in the first working state, taking the first initial target node as the first target node.
S9052, when the first initial target node is in the second working state, enabling the first initial target node to be in the first working state i And storing the data in a first queue corresponding to the first initial target node, and taking the first initial target node as the first target node when waiting for the first initial target node to be in the first working state.
S9053, when the first initial target node is in the second working state, acquiring the maximum H in the first working state it Corresponding node as A i A corresponding first target node.
Further, the S9052 step and the S9053 step cannot be executed simultaneously.
Specifically, the first working state is a state in which a node does not execute a process.
Specifically, the second working state is a state in which the node is executing a process.
In this embodiment, the first target node corresponding to the parent process is selected, so that the phenomenon that the process processing is interrupted due to the fact that the parent process cannot acquire the first target node can be avoided, running confusion and resource waste caused by random node switching of the parent process are prevented, all nodes are reasonably utilized, and the running efficiency of the application software is improved.
Specifically, the method further comprises the following steps after the step S900:
and S1000, determining a second target node corresponding to the second process according to the second target CPU and the original node list corresponding to the second target CPU, so that the second target node corresponding to the second process executes the second process, wherein the second target node corresponding to the second process is determined according to the second target CPU and the original node list corresponding to the second target CPU, so that the method for executing the second process by the second target node corresponding to the second process is consistent with the step S900, which is not described herein again, and further, the step S900 and the step S1000 can be executed simultaneously.
Specifically, when a first target node corresponding to a parent process is the same as a second target node corresponding to a child process corresponding to the parent process, the node preferentially executes the parent process.
Further, it can be understood that: and when the first target node corresponding to the parent process and the second target node corresponding to the child process corresponding to the parent process are the same node, the node preferentially executes the parent process.
The invention provides a data processing system for process monitoring, which comprises: a first database, a second database, a processor and a memory storing a computer program which, when executed by the processor, performs the steps of: acquiring target application software and a process characteristic vector corresponding to the target application software; acquiring a feature vector of an intermediate process; acquiring a priority list of the process corresponding to the central processing unit according to the process characteristic vector and the intermediate process characteristic vector; acquiring a target central processing unit corresponding to the process according to the priority; acquiring a target node corresponding to the process according to an original node list corresponding to the target central processing unit, and enabling the target node corresponding to the process to execute the process; therefore, on one hand, the process can be bound on different nodes to run, on the other hand, the main process and the sub-process can be independently bound on different nodes, resource waste is avoided, flexibility is high, and the overall running efficiency of the application software can be improved.
Although some specific embodiments of the present invention have been described in detail by way of illustration, it should be understood by those skilled in the art that the above illustration is only for the purpose of illustration and is not intended to limit the scope of the invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. A data processing system for process monitoring, the system comprising: a first database, a second database, a processor, and a memory storing a computer program, wherein the first database comprises: the original application software list, the process set corresponding to each original application software in the original application software list, and the second database includes: the original CPU set and the original node list corresponding to each CPU in the original CPU set realize the following steps when the computer program is executed by a processor:
s100, obtaining target application software and a first process set A = { A } corresponding to the target application software from an original application software list 1 ，……，A i ，……，A m }，A i ＝{A i1 ，……，A ij ，……，A in }，A ij The method comprises the steps that j =1 \ 8230, m, m is the number of first processes, j =1 \ 8230, n, n is the number of first process feature vectors, and the j =1 \ 8230is the number of the first process feature vectors corresponding to target application software;
s300, acquiring a first CPU list C = { C from an original CPU set 1 ，……，C r ，……，C s }，C r ＝{C r1 ，……，C rg ，……，C rz }，C rg The method comprises the steps that a g-th first middle process characteristic vector corresponding to an r-th first CPU identifier is marked, wherein r =1 \ 8230 \8230, s is the number of the first CPU identifiers, and g =1 \ 8230 \8230 \ 8230, z and z are the number of the first middle process characteristic vectors;
s500, traverse A i And according to A ij And C rg Obtaining A i Corresponding first CPU priority list D i ＝{D i1 ，……，D ir ，……，D is }，D ir Is A i And C r A first CPU priority in between;
s700, according to D ir Obtaining A i A corresponding first target CPU;
s900, obtaining A according to the first target CPU and a first original node list corresponding to the first target CPU i Corresponding first target node, such that rootAccording to A i Corresponding first target node executes A i A corresponding first process.
2. The process monitoring data processing system according to claim 1, further comprising the step of, in the step S100:
s101, obtaining a preset process feature type list L = { L = { (L) } 1 ，……，L a ，……，L b }，L a The method is characterized in that the method is a first preset process characteristic type, wherein a =1 \ 8230 \8230, b and b are preset process characteristic types;
s103, obtaining an original process set A ' = { A ' corresponding to target application software ' 1 ，……，A' i ，……，A' m }，A' i ＝{A' i1 ，……，A' ic ，……，A' id }，A' ic C =1 \ 8230for the c original process feature vector of the ith first process corresponding to the target application software, wherein \ 8230d and d are the number of the original process feature vectors;
s105, is A' ic Corresponding Process feature type and L a When they are consistent, then A ij ＝A' ic 。
3. The process monitoring data processing system of claim 2, wherein L is stored in the second database.
4. The process monitoring data processing system according to claim 1, further comprising the following step after the step S100:
s200, according to A i Obtaining A i Corresponding second set of processes B i ＝{B i1 ，……，B ix ，……，B ip }，B ix ＝{B 1 ix ，……，B y ix ，……，B q ix }，B y ix Is A i And the y second process characteristic vector of the corresponding x second process is x =1 \8230p, 8230p, p is the number of the second processes, and y =1 \8230p, 8230q, q is the number of the second process characteristic vectors.
5. The process-monitored data processing system of claim 4, wherein the association between the first process and the second process is a parent-child relationship, wherein the first process is a parent process corresponding to the second process, and the second process is a child process corresponding to the first process.
6. The process-monitoring data processing system of claim 1, wherein the first CPU identifier is an identifier corresponding to a first CPU, and wherein the first CPU is a CPU having an I/O interface.
7. The process monitoring data processing system according to claim 1, further comprising the step of, in the step S500:
s501, obtaining A ij Corresponding first similarity list F ij ＝{F 1 ij ，……，F r ij ，……，F s ij }，F r ij ＝(F r1 ij ，……，F rg ij ，……，F rz ij )，F rg ij Is A ij And C rg A first similarity therebetween;
s503, traverse F r ij From F r ij To obtain the maximum first similarity as the first intermediate similarity F' r ij ；
S505, according to F' r ij Obtaining D ir Wherein D is ir The following conditions are met:
8. the process monitoring data processing system according to claim 1, further comprising the step of, in the step S700:
s701, when D ir ≥D 0 At the time of from D i The first CPU corresponding to the maximum CPU priority is obtained as A i A corresponding first target CPU, wherein D 0 Is a preset first priority threshold;
s703, when D ir ＜D 0 Then, a second CPU list is obtained from the original CPU set;
s705, randomly selecting any second CPU from the second CPU list as A i A corresponding first target CPU.
9. The process monitoring data processing system of claim 8, wherein the second CPU is a CPU without an I/O interface.
10. The process monitoring data processing system according to claim 1, further comprising the step of, in the step S900:
s901, according to the first target CPU, acquiring a first original node list H = { H } corresponding to the first target CPU 1 ，……，H t ，……，H u }，H t ＝(H t1 ，……，H tv ，……，H tw ) Wherein H is tv T =1 \ 8230for the vth first original node feature vector corresponding to the first target CPU, \8230, u is the number of first original nodes, v =1 \ 8230for the vth first original node feature vector corresponding to the first target CPU, and w is the number of first original node feature vectors;
s903, traverse A i According to H t And A i Obtaining A i Corresponding first node priority list H i ＝{H i1 ，……，H it ，……，H iv }，H it Is A i And H t A first node priority in between;
s905, according to H it Obtaining A i A corresponding first target node.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202211128291.3A CN115408160B (en) | 2022-09-16 | 2022-09-16 | Data processing system for process monitoring |
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