CN115408160B - Data processing system for process monitoring - Google Patents
Data processing system for process monitoring Download PDFInfo
- Publication number
- CN115408160B CN115408160B CN202211128291.3A CN202211128291A CN115408160B CN 115408160 B CN115408160 B CN 115408160B CN 202211128291 A CN202211128291 A CN 202211128291A CN 115408160 B CN115408160 B CN 115408160B
- Authority
- CN
- China
- Prior art keywords
- cpu
- target
- original
- list
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- 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
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
- G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
- G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
- G06F9/5038—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the execution order of a plurality of tasks, e.g. taking priority or time dependency constraints into consideration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/5021—Priority
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2209/00—Indexing scheme relating to G06F9/00
- G06F2209/50—Indexing scheme relating to G06F9/50
- G06F2209/508—Monitor
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Abstract
The invention provides a data processing system for process monitoring, which comprises: the first database, the second database, the processor and the memory storing the computer program, when the computer program is executed by the processor, the following steps are realized: acquiring target application software and process feature vectors corresponding to the target application software; acquiring an intermediate process feature vector; acquiring a priority list of a central processing unit corresponding to the process according to the process feature vector and the intermediate process feature 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, so that the target node corresponding to the process executes the process; it can be seen that, in one aspect of the present invention, a process may be bound to different nodes to run; on the other hand, the main process and the subprocess can be independently bound on different nodes, so that resource waste is avoided, the flexibility is high, and the overall running efficiency of the application software can be improved.
Description
Technical Field
The present invention relates to the field of process processing, and in particular, to a process monitoring data processing system.
Background
The existing data processing method for process monitoring is mostly completed through the following steps: testing and acquiring the memory quantity, node information and node quantity required by an application software process; matching with the memory quantity and the node information required by the application software process according to the existing node information and the node quantity in the system; calling the node if the matching is successful; and binding the process of the application software on the node of the corresponding system, wherein the software process binding node mainly enables the process to run on the characteristic node or the CPU core, but when the process cross-node access memory exists, the memory performance is lower.
Therefore, some technical solutions do not bind a process with a node, but there are also problems: when an OS (Operating System, abbreviated as "OS") schedules a process, the same process may be scheduled to different CPU cores for running, and data in the memory may not be migrated in real time or the migration cost is high, so that the process accesses the memory across nodes, and the running efficiency is low.
At present, the binding node of the software process has two modes, (1) binding is carried out inside the software, namely, when the software runs, an interface provided by an OS is used for binding the process to a characteristic node or CPU core, but the software is not flexible enough and cannot adapt to different CPU or machine architectures, and most of the software is not bound inside; (2) External tool binding is used, but only processes and sub-processes can be bound to certain same nodes or CPU cores, and if the processes and the sub-processes are bound to a plurality of nodes, the technical problem of the situation that the processes are scheduled to run on different nodes still exists.
Disclosure of Invention
Aiming at the technical problems, the invention adopts the following technical scheme:
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: 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:
s100, acquiring 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 j-th first process feature vector of the i-th first process corresponding to the target application software is i=1, … …, m, m is the number of the first processes, j=1, … …, n, n is the number of the first process feature 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 The corresponding g first intermediate process feature vector is identified for the r first CPU, r=1, … …, s, s are the number of first CPU identifications, and g=1, … …, z, z are the number of first intermediate process feature vectors.
S500, traversing 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 therebetween.
S700 according to D ir Obtaining A i And a corresponding first target CPU.
S900, acquiring A according to a first target CPU and a first original node list corresponding to the first target CPU i A corresponding first target node such that according to A i 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: the first database, the second database, the processor and the memory storing the computer program, when the computer program is executed by the processor, the following steps are realized: acquiring target application software and process feature vectors corresponding to the target application software; acquiring an intermediate process feature vector; acquiring a priority list of a central processing unit corresponding to the process according to the process feature vector and the intermediate process feature 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, so that the target node corresponding to the process executes the process; according to the invention, 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, so that the resource waste is avoided, the flexibility is higher, and the overall running efficiency of the application software can be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a process monitoring data processing system executing a computer program to process a parent process according to an embodiment of the present invention.
FIG. 2 is a flow chart of a process control data processing system executing a computer program to process a sub-process according to an embodiment of the present invention.
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 fall within the scope of the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise 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 or inherent to such process, method, article, or apparatus, 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, the system includes: 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: the original CPU set and the list of original nodes 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, acquiring 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 j-th first process feature vector of the i-th first process corresponding to the target application software is i=1, … …, m, m is the number of the first processes, j=1, … …, n, n is 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 1 ，……，L a ，……，L b }，L a For the a-th preset process feature type, a=1, … …, b, b is the number of preset process feature types.
Specifically, L is stored in the second database.
S103, acquiring an original process set A ' = { A ' corresponding to the target application software ' 1 ，……，A' i ，……，A' m }，A' i ＝{A' i1 ，……，A' ic ，……，A' id }，A' ic The c-th original process feature vector of the i-th first process corresponding to the target application software is c=1, … …, d and d are the number of the original process feature vectors.
S105, when A' ic Corresponding process characteristic type and L a When the two are consistent, then A ij ＝A' ic 。
Above, for the characteristics of the process, it is determined whether the process is a specific process, so that binding between the specific process and the corresponding CPU is facilitated, further, the execution efficiency of the specific process is improved, the CPU resource waste is avoided, and the overall running efficiency of the application software is improved.
Specifically, the following steps are further included after step S100:
s200, according to A i Obtaining A i Corresponding second process set 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 The y second process feature vector of the corresponding x second process, x=1, … …, p, p is the number of second processes, and y=1, … …, q, q is the number of second process feature vectors.
Specifically, the step S200 further includes the steps of:
s201, acquiring a preset second process feature type list L 0 ＝{L 0 1 ，……，L 0 e ，……，L 0 f }，L 0 e The second process feature type is preset for the e-th process feature type, and e=1, … …, f and f are the number of the preset second process feature types.
Specifically, L 0 Stored in a second database.
S203, obtain A i Corresponding original second Process set 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 The h original second process feature vector of the corresponding x second process, h=1, … …, k, k is the number of original second process feature vectors.
S205, when B' h ix Corresponding second process feature type and L 0 If it is consistent, then B y ix ＝B' h ix 。
Specifically, the association relationship between the first process and the second process is a parent-child relationship, and 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 feature type is further refined, and the sub-process with the feature type corresponding to the parent process is distinguished, so that the parent process and the sub-process with the feature 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 sub-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 an original CPU set 1 ，……，C r ，……，C s }，C r ＝{C r1 ，……，C rg ，……，C rz }，C rg The corresponding g first intermediate process feature vector is identified for the r first CPU, r=1, … …, s, s are the number of first CPU identifications, and g=1, … …, z, z are the number of first intermediate process feature vectors.
Specifically, the first CPU identifier is an identity identifier corresponding to the first CPU, wherein the first CPU is a CPU with an I/O interface.
S500, traversing 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 therebetween.
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 degree of similarity between.
S503, traversing F r ij From F r ij The maximum first similarity is obtained as a first intermediate similarity F' r ij 。
S505 according to F' r ij Obtaining D ir Wherein D is ir Meets the following conditions:
in this embodiment, the similarity is obtained by comparing the feature vector of the parent process with the first intermediate process feature vector corresponding to the first CPU identifier, and further, the similarity is averaged, so that the matching degree between the parent process and the CPU can be obtained more accurately, which is favorable for selecting the optimal CPU corresponding to the parent process, and is favorable for selecting the optimal node corresponding to the parent process according to the optimal CPU, so as to further maximize the resource utilization.
Specifically, the following steps are further included after step 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 further, the step S500 and the step S600 can be executed simultaneously.
In the embodiment, the parent process and the child process can be processed independently at the same time, do not interfere with each other and do not affect each other, so that the running time of the application software is saved, and the running efficiency of the application software is improved.
S700 according to D ir Obtaining A i And a corresponding target CPU.
Specifically, the step S700 further includes the steps of:
s701, when D ir ≥D 0 At the time 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 acquiring a second CPU list from the original CPU set.
Specifically, D 0 The range of the value of (2) is 0.8-1.
Preferably D 0 The value is 0.9, so that CPU selection errors caused by too low threshold value setting or selection caused by too high threshold value setting can be avoidedThe proper CPU is not taken, so that resource waste is caused, and the running efficiency of the software is reduced due to the fact that the CPU cannot be reasonably used.
S705, randomly selecting any second CPU from the second CPU list as A i And a corresponding first target CPU.
Specifically, the second CPU is a CPU having no I/O interface.
Specifically, the following steps are further performed after step S700:
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 further, the step S700 and the step S800 can be executed simultaneously.
S900, acquiring A according to the first target CPU and an original node list corresponding to the first target CPU i A corresponding first target node such that according to A i 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 is tv And for the v first original node feature vector of the t first original node corresponding to the first target CPU, t=1, … …, u, u is the number of the first original nodes, v=1, … …, w is the number of the first original node feature vectors.
S903, traversal A i According to H t And A is 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 is t A first node priority in between.
Specifically, the step S903 further includes the steps of:
s9031 acquisition of 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 is tv A third similarity therebetween.
S9033 traversing K t ij From K t ij The largest third similarity is obtained as a 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 further the average value is obtained on the similarity, 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 Obtain the maximum H it The corresponding node is taken as A i 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, adding A i And storing the first initial target node in a first queue corresponding to the first initial target node, and taking the first initial target node as a first target node when the first initial target node is in a 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 step S9052 and the step S9053 cannot be performed simultaneously.
Specifically, the first working state is a state that the 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 parent process cannot acquire the first target node and causes interruption of process processing can be avoided, the situation that the parent process randomly switches nodes to cause operation confusion and resource waste is prevented, all the nodes are reasonably utilized, and the operation efficiency of application software is improved.
Specifically, the following steps are further included after step S900:
s1000, determining a second target node corresponding to a second process according to a second target CPU and an 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 a method for executing the second process by the second target node corresponding to the second process is consistent with the step S900, and further, the step S900 and the step S1000 can be executed simultaneously.
Specifically, when a first target node corresponding to a parent process and a second target node corresponding to a child process corresponding to the parent process form the same, the node preferentially executes the parent process.
Further, it can be understood that: when a first target node corresponding to a parent process and a second target node corresponding to a 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: the first database, the second database, the processor and the memory storing the computer program, when the computer program is executed by the processor, the following steps are realized: acquiring target application software and process feature vectors corresponding to the target application software; acquiring an intermediate process feature vector; acquiring a priority list of a central processing unit corresponding to the process according to the process feature vector and the intermediate process feature 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, so that the target node corresponding to the process executes the process; according to the invention, 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, so that the resource waste is avoided, the flexibility is higher, and the overall running efficiency of the application software can be improved.
While certain specific embodiments of the invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. Those skilled in the art will also appreciate that many 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 second database comprises an original application software list and a process set corresponding to each original application software in the original application software list, wherein the second database comprises: 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, the following steps are realized:
s100, acquiring 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-th first process feature vectors of an i-th first process corresponding to target application software are provided, i=1, … …, m and m are the number of the first processes, j=1, … …, n and n are the number of the first process feature 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 Identifying a corresponding g first intermediate process feature vector for the r first CPU, wherein r=1, … …, s, s are the number of the first CPU identifications, g=1, … …, z is the number of the first intermediate process feature vectors;
s500, traversing 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 therebetween;
s700 according to D ir Obtaining A i A corresponding first target CPU;
s900, acquiring A according to the first target CPU and a first original node list corresponding to the first target CPU i A corresponding first target node such that according to A i Corresponding first target node executes A i A corresponding first process.
2. The process-monitored data processing system according to claim 1, further comprising, in step S100, the steps of:
s101, acquiring a preset process feature type list L= { L 1 ，……，L a ，……，L b }，L a A=1 for the a-th preset process feature type, … …, b, b is the preset process feature type;
s103, acquiring an original process set A ' = { A ' corresponding to the target application software ' 1 ，……，A' i ，……，A' m }，A' i ＝{A' i1 ，……，A' ic ，……，A' id }，A' ic C=1, … …, d, d is the number of original process feature vectors for the c original process feature vector of the i first process corresponding to the target application software;
s105, when A' ic Corresponding process characteristic type and L a When the two are consistent, then A ij ＝A' ic 。
3. The process-monitored data processing system of claim 2, wherein L is stored in a second database.
4. The process-monitored data processing system according to claim 1, further comprising, after step S100, the steps of:
s200, according to A i Obtaining A i Corresponding second process set 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 The y second process feature vector of the corresponding x second process, x=1, … …, p, p is the number of second processes, and y=1, … …, q, q is the number of second process feature vectors.
5. The process-monitored data processing system according to claim 4, wherein the association relationship 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-monitored data processing system according to claim 1, wherein said first CPU is identified as a corresponding identity of a first CPU, wherein said first CPU is a CPU having an I/O interface.
7. The process-monitored data processing system according to claim 1, further comprising, in step S500, 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 degree of similarity therebetween;
s503, traversing F r ij From F r ij The maximum first similarity is obtained as a first intermediate similarity F' r ij ；
S505 according to F' r ij Obtaining D ir Wherein D is ir Meets the following conditions:
8. the process-monitored data processing system according to claim 1, further comprising, in step S700, the steps of:
s701, when D ir ≥D 0 At the time 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 When the CPU is in the original CPU set, acquiring a second CPU list;
s705, randomly selecting any second CPU from the second CPU list as A i And a corresponding first target CPU.
9. The process-monitored data processing system according to claim 8, wherein the second CPU is a CPU having no I/O interface.
10. The process-monitored data processing system according to claim 1, further comprising, in step S900, 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 is tv The method comprises the steps that v first node feature vectors of a t first original node corresponding to a first target CPU are obtained, t=1, … …, u, u is the number of the first original nodes, v=1, … …, w is the number of the first original node feature vectors;
s903, traversal A i According to H t And A is 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 is t A first node priority therebetween;
s905 according to H it Obtaining A i A corresponding first target node.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211128291.3A CN115408160B (en) | 2022-09-16 | 2022-09-16 | Data processing system for process monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211128291.3A CN115408160B (en) | 2022-09-16 | 2022-09-16 | Data processing system for process monitoring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115408160A CN115408160A (en) | 2022-11-29 |
| CN115408160B true CN115408160B (en) | 2023-05-05 |
Family
ID=84165245
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211128291.3A Active CN115408160B (en) | 2022-09-16 | 2022-09-16 | Data processing system for process monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115408160B (en) |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106775975A (en) * | 2016-12-08 | 2017-05-31 | 青岛海信移动通信技术股份有限公司 | Process scheduling method and device |
| CN107145389A (en) * | 2017-03-09 | 2017-09-08 | 深圳市先河系统技术有限公司 | A kind of system process monitoring method and computing device |
| CN114580392A (en) * | 2022-04-29 | 2022-06-03 | 中科雨辰科技有限公司 | Data processing system for identifying entity |
| CN114595456A (en) * | 2022-03-17 | 2022-06-07 | 杭州云深科技有限公司 | Abnormal application software acquisition system based on active behavior of application software |
| CN114610581A (en) * | 2022-03-17 | 2022-06-10 | 杭州云深科技有限公司 | Data processing system for acquiring application software |
| CN114781379A (en) * | 2022-06-22 | 2022-07-22 | 中科雨辰科技有限公司 | Data processing system for acquiring key phrases |
| CN115048925A (en) * | 2022-08-15 | 2022-09-13 | 中科雨辰科技有限公司 | Data processing system for determining abnormal text |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017052555A1 (en) * | 2015-09-24 | 2017-03-30 | Hewlett Packard Enterprise Development Lp | Process and thread launch features |
-
2022
- 2022-09-16 CN CN202211128291.3A patent/CN115408160B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106775975A (en) * | 2016-12-08 | 2017-05-31 | 青岛海信移动通信技术股份有限公司 | Process scheduling method and device |
| CN107145389A (en) * | 2017-03-09 | 2017-09-08 | 深圳市先河系统技术有限公司 | A kind of system process monitoring method and computing device |
| CN114595456A (en) * | 2022-03-17 | 2022-06-07 | 杭州云深科技有限公司 | Abnormal application software acquisition system based on active behavior of application software |
| CN114610581A (en) * | 2022-03-17 | 2022-06-10 | 杭州云深科技有限公司 | Data processing system for acquiring application software |
| CN114580392A (en) * | 2022-04-29 | 2022-06-03 | 中科雨辰科技有限公司 | Data processing system for identifying entity |
| CN114781379A (en) * | 2022-06-22 | 2022-07-22 | 中科雨辰科技有限公司 | Data processing system for acquiring key phrases |
| CN115048925A (en) * | 2022-08-15 | 2022-09-13 | 中科雨辰科技有限公司 | Data processing system for determining abnormal text |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115408160A (en) | 2022-11-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110096345B (en) | Intelligent task scheduling method, device, equipment and storage medium | |
| CN109685092B (en) | Clustering method, equipment, storage medium and device based on big data | |
| US10013288B2 (en) | Data staging management system | |
| JP2016042284A (en) | Parallel computer system, management device, method for controlling parallel computer system, and management device control program | |
| CN112839048B (en) | DIDS task scheduling algorithm based on reinforcement learning under edge computing environment | |
| CN115756780A (en) | Quantum computing task scheduling method and device, computer equipment and storage medium | |
| CN110414569B (en) | Clustering implementation method and device | |
| CN115408160B (en) | Data processing system for process monitoring | |
| CN101256484A (en) | Method and equipment for determining to call at least one application program frequency | |
| CN108509440A (en) | A kind of data processing method and device | |
| US10599472B2 (en) | Information processing apparatus, stage-out processing method and recording medium recording job management program | |
| CN103699653A (en) | Method and device for clustering data | |
| CN111143063A (en) | Task resource reservation method and device | |
| CN116126937A (en) | Job scheduling method, job scheduling device, electronic equipment and storage medium | |
| CN115858648A (en) | Database generation method, data stream segmentation method, device, equipment and medium | |
| CN114203304A (en) | Information pushing method based on smart medical big data and smart medical cloud server | |
| CN113127289B (en) | Resource management method, computer equipment and storage medium based on YARN cluster | |
| US20180052728A1 (en) | Root cause candidate determination in multiple process systems | |
| CN114116150A (en) | Task scheduling method and device and related equipment | |
| CN113535346A (en) | Method, device and equipment for adjusting number of threads and computer storage medium | |
| WO2002003192A2 (en) | Device and method for allocating jobs in a network | |
| CN112486615B (en) | Method, device, equipment and storage medium for executing decision flow based on topological path | |
| CN116841717B (en) | Method and system for generating sequencing in real time according to task emergency degree | |
| CN112149696A (en) | Method and device for training graph embedding model | |
| CN111552561B (en) | Task processing method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |