CN115470003A - Cloud desktop resource processing method, device, equipment and storage medium - Google Patents
Cloud desktop resource processing method, device, equipment and storage medium Download PDFInfo
- Publication number
- CN115470003A CN115470003A CN202211125894.8A CN202211125894A CN115470003A CN 115470003 A CN115470003 A CN 115470003A CN 202211125894 A CN202211125894 A CN 202211125894A CN 115470003 A CN115470003 A CN 115470003A
- Authority
- CN
- China
- Prior art keywords
- memory
- cloud desktop
- terminal
- preset
- data
- 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.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 20
- 230000015654 memory Effects 0.000 claims abstract description 304
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000004458 analytical method Methods 0.000 claims abstract description 30
- 238000012545 processing Methods 0.000 claims description 38
- 238000004590 computer program Methods 0.000 claims description 16
- 239000000306 component Substances 0.000 description 19
- 238000013468 resource allocation Methods 0.000 description 17
- 238000010586 diagram Methods 0.000 description 13
- 230000006870 function Effects 0.000 description 10
- 238000004891 communication Methods 0.000 description 6
- 238000007726 management method Methods 0.000 description 6
- 230000003287 optical effect Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000013461 design Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000012384 transportation and delivery Methods 0.000 description 3
- 230000001174 ascending effect Effects 0.000 description 2
- 239000008358 core component Substances 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000013307 optical fiber Substances 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000007334 memory performance Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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
- G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
- G06F9/5016—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals the resource being the memory
Landscapes
- Engineering & Computer Science (AREA)
- Software Systems (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Stored Programmes (AREA)
Abstract
The application relates to the technical field of resource management, and provides a cloud desktop resource processing method, a device, equipment and a storage medium, wherein the method is used for acquiring memory use data of a cloud desktop use terminal within preset analysis time; determining whether to adjust the memory allocation type of the cloud desktop use terminal or not according to preset memory adjustment rules and memory use data; and if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, generating an adjusting instruction so as to allocate memory resources for the cloud desktop use terminal according to the adjusting instruction.
Description
Technical Field
The invention relates to the technical field of resource management, in particular to a cloud desktop resource processing method, a cloud desktop resource processing device, cloud desktop resource processing equipment and a storage medium.
Background
Cloud desktops are typical applications in the cloud computing era, including personal-level applications and organizational-level applications. The cloud desktop can release data space and management service to an operator in a desktop mode, is suitable for being used as a network operation system of micro-handheld mobile applications such as a tablet and a mobile phone, and can also upgrade a traditional Personal Computer (PC) into network operation. The cloud desktop based on the data space issues cloud resources to each operation terminal mainly through virtualization application, and still belongs to a data platform cloud operation system.
At present, when a cloud desktop provides a cloud desktop service for an operation terminal, a dynamic resource allocation scheme based on the use condition of a cloud desktop memory of a user is lacked, and when each terminal device is accessed, memories with the same size are uniformly allocated according to preset values.
However, the prior art has the problem of low resource utilization rate.
Disclosure of Invention
The application provides a cloud desktop resource processing method, device, equipment and storage medium, so as to solve the technical problem of low resource utilization rate in the prior art.
In a first aspect, the present application provides a cloud desktop resource processing method, including:
acquiring memory use data of the cloud desktop use terminal within preset analysis time;
determining whether to adjust the memory allocation type of the cloud desktop use terminal or not according to a preset memory adjustment rule and the memory use data;
and if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, generating an adjustment instruction so as to configure memory resources for the cloud desktop use terminal according to the adjustment instruction.
The cloud desktop using terminal can acquire the memory using data of the cloud desktop using terminal at regular time, analyze the memory using data according to the preset memory adjusting rule, accurately judge whether the memory allocated to the cloud desktop using terminal is reasonable or not, generate an adjusting instruction after the memory allocation is determined to be unreasonable, configure the reasonable memory for the cloud desktop using terminal, realize dynamic memory resource allocation, optimize the experience of a cloud desktop user, and improve the resource utilization rate.
Optionally, the determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data includes:
determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal;
determining the hit rate of the adjustment rule of the cloud desktop using terminal in the preset analysis time according to the memory using data and the preset memory adjustment rule;
and if the hit rate of the adjustment rule is greater than the preset frequency, determining to adjust the memory allocation type of the cloud desktop use terminal.
According to the method and the device, the preset memory adjustment rule corresponding to the cloud desktop using terminal is determined according to the current memory allocation type of the cloud desktop using terminal, so that whether the memory using condition of the cloud desktop using terminal is reasonable or not is judged according to the preset memory adjustment rule, specifically, according to the memory using data and the preset memory data of the preset memory adjustment rule, the adjustment rule hit rate of the memory using condition can be calculated, whether the memory allocation type needs to be adjusted or not is accurately judged, the memory utilization rate is further improved, and the using efficiency of the whole resources of the cloud desktop is improved.
Optionally, the memory allocation types include a high-allocation desktop pool allocation type, a medium-allocation desktop pool allocation type, and a low-allocation resource pool allocation type;
correspondingly, the determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal includes:
if the current memory allocation type of the cloud desktop using terminal is a medium-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a first preset threshold value or the size of the memory using data is larger than or equal to a fifth preset threshold value and smaller than or equal to a sixth threshold value;
if the current memory allocation type of the cloud desktop using terminal is a low-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a second threshold value and smaller than or equal to a third threshold value;
and if the current memory allocation type of the cloud desktop using terminal is a high-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a fourth preset threshold value.
Here, the embodiment of the application determines different memory adjustment rules according to different current resource allocation conditions, so that accurate regulation and control of storage resources are realized, the memory utilization rate is further improved, and the resource utilization rate is improved.
Optionally, after determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data, the method further includes:
and displaying the memory adjustment data of the cloud desktop use terminal.
Optionally, the memory adjustment data includes a cloud desktop account number of the cloud desktop using terminal, a frequency of collected indexes in a hit rule, a frequency of total collected indexes, and a hit ratio.
The embodiment of the application can also realize the visualization of the memory resource adjustment, display the memory adjustment data to a user or an operator, facilitate the user to know the resource use condition, and further improve the user experience.
Optionally, after the obtaining of the memory usage data of the cloud desktop usage terminal within the preset analysis time, the method further includes:
and storing the memory use data according to a preset storage format, wherein the preset storage format comprises a collection timestamp of each memory use data, a cloud desktop account and the memory use amount of the cloud desktop.
The memory use data can be stored in the embodiment of the application, so that the stored data can be directly called in the subsequent memory use condition analysis, the memory analysis and the vacancy holding can be conveniently carried out according to the stored data, and the user experience and the resource utilization rate are further improved.
Optionally, after the determining to adjust the memory allocation type of the cloud desktop user terminal, generating an adjustment instruction to configure memory resources for the cloud desktop user terminal according to the adjustment instruction, the method further includes:
and responding to the cloud desktop using request of the cloud desktop using terminal, and configuring memory resources for the cloud desktop using terminal according to the adjusting instruction.
Here, when the cloud desktop use terminal applies for or logs in the cloud desktop for use, the embodiment of the application can configure the memory resource for the cloud desktop use terminal according to the adjustment instruction generated by the dynamic memory analysis, so that the user experience and the resource utilization rate are further improved.
In a second aspect, an embodiment of the present application provides a cloud desktop resource processing apparatus, including:
the acquisition module is used for acquiring the memory use data of the cloud desktop use terminal within the preset analysis time;
the type determining module is used for determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjusting rule and the memory using data;
and the generating module is used for generating an adjusting instruction if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, so as to configure memory resources for the cloud desktop use terminal according to the adjusting instruction.
Optionally, the type determining module includes:
the first determining module is used for determining a preset memory adjusting rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal;
the second determining module is used for determining the hit rate of the adjustment rule of the cloud desktop using terminal in the preset analysis time according to the memory using data and the preset memory adjustment rule;
and the third determining module is used for determining to adjust the memory allocation type of the cloud desktop using terminal if the hit rate of the adjustment rule is greater than the preset frequency.
Optionally, the memory allocation type includes a high-allocation desktop pool allocation type, a medium-allocation desktop pool allocation type, and a low-allocation resource pool allocation type;
correspondingly, the first determining module is specifically configured to:
if the current memory allocation type of the cloud desktop using terminal is a medium-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a first preset threshold value or the size of the memory using data is larger than or equal to a fifth preset threshold value and smaller than or equal to a sixth threshold value;
if the current memory allocation type of the cloud desktop using terminal is a low-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a second threshold value and smaller than or equal to a third threshold value;
and if the current memory allocation type of the cloud desktop using terminal is a high-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a fourth preset threshold value.
Optionally, after the type determining module determines whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data, the method further includes:
and the display module is used for displaying the memory adjustment data of the cloud desktop use terminal.
Optionally, the memory adjustment data includes a cloud desktop account number of the cloud desktop using terminal, a collection index frequency in the hit rule, a total collection index frequency, and a hit ratio.
Optionally, after the obtaining module obtains the memory usage data of the cloud desktop usage terminal within the preset analysis time, the apparatus further includes:
the storage module is used for storing the memory use data according to a preset storage format, wherein the preset storage format comprises a collection timestamp of each memory use data, a cloud desktop account number and a cloud desktop memory use amount.
Optionally, if the generation module determines to adjust the memory allocation type of the cloud desktop user terminal, then an adjustment instruction is generated, so as to configure memory resources for the cloud desktop user terminal according to the adjustment instruction, where the apparatus further includes:
and the configuration processing module is used for responding to a cloud desktop using request of the cloud desktop using terminal and configuring memory resources for the cloud desktop using terminal according to the adjusting instruction.
In a third aspect, the present application provides a cloud desktop resource processing device, including: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored by the memory to cause the at least one processor to perform the cloud desktop resource processing method as described above in the first aspect and various possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, in which computer-executable instructions are stored, and when a processor executes the computer-executable instructions, the cloud desktop resource processing method according to the first aspect and various possible designs of the first aspect is implemented.
In a fifth aspect, the present invention provides a computer program product comprising a computer program that, when executed by a processor, implements the cloud desktop resource processing method according to the first aspect and various possible designs of the first aspect.
According to the cloud desktop resource processing method, the cloud desktop resource processing device, the cloud desktop resource processing equipment and the storage medium, the method can be used for collecting the memory use data of the cloud desktop use terminal at regular time, analyzing the memory use data according to the preset memory adjustment rule, accurately judging whether the memory allocated to the cloud desktop use terminal at present is reasonable or not, generating the adjustment instruction after the fact that the memory allocation is unreasonable is determined, allocating the reasonable memory for the cloud desktop use terminal, achieving dynamic memory resource allocation, optimizing experience of cloud desktop users and improving resource utilization rate.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a cloud desktop system architecture provided in the prior art;
fig. 2 is a schematic diagram of an architecture of a cloud desktop resource processing system according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of a cloud desktop resource processing method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another cloud desktop resource processing method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a cloud desktop resource processing apparatus according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a cloud desktop resource processing device according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the disclosure, as detailed in the appended claims.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of this application and the above-described figures 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 application described herein are capable of operation in sequences other than those illustrated or described herein. Moreover, 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 apparatus 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.
Citrix VDI is a set of desktop virtualization solutions that can transform Windows desktop and applications into an on-demand service for delivery to any user anywhere using any device. Single Windows, web, and SaaS applications or entire virtual desktops can be securely delivered to PCs, tablets, laptops, or clients. Exemplarily, fig. 1 is a schematic diagram of a cloud desktop system architecture provided by the prior art, and as shown in fig. 1, the cloud desktop system architecture includes a user cloud desktop resource pool 10, a cloud desktop architecture layer 11, an active directory domain 12, a virtualization layer 13, and an infrastructure layer 14.
Wherein, the cloud desktop architecture layer can comprise a PVS component, a CDC component, an SQL component, a Lic component and an STFT component.
The PVS component is a core component of a Citrix VDI scheme, is a scheduling center of a whole set of cloud desktop system, and delivers a single standard desktop mirror image to a user terminal as required through network service by adopting a streaming technology. And carrying out centralized configuration, delivery and management on the cloud desktop mirror image.
The CDC component is an online between a control user and a cloud desktop, and a cloud desktop administrator can manage applications and desktops in a data center in a centralized mode and control cloud desktop delivery to end users through a network.
The SQL component is a Citrix VDI back-end database, and stores platform configuration information, user cloud desktop login history records and other information.
The Lic component provides a component for the CDC and PVS to run authorization.
The STFT component is a front-end page of the cloud desktop, namely a user accesses an inlet of the cloud desktop.
The Active Directory (AD) domain can be used to manage multiple computers at the same time, thereby implementing centralized management.
The infrastructure layer may include servers, storage, and network services, among others.
The Citrix VDI cloud desktop solution enables people to access their own work environment at any time and any place using any device through a virtual desktop based on cloud computing technology. However, the native solution lacks a dynamic resource allocation scheme based on the usage of the cloud desktop memory of the user. The cloud desktop cannot be subjected to accurate dynamic expansion and contraction due to memory performance bottleneck in the later-stage platform maintenance and management process. On one hand, due to the lack of an effective memory monitoring mechanism, a low-frequency (i.e. occasionally or only running light-weight application and test) user list of the cloud desktop in a period (e.g. one week or one month) cannot be analyzed, dynamic capacity reduction of the memory of the cloud desktop cannot be realized, and the waste of memory resources of a platform is caused from the management perspective; on the other hand, for a user with a cloud desktop at a high frequency (i.e., often running high-memory-occupation application and test), dynamic expansion of a memory of the cloud desktop cannot be achieved, the user cannot feel convenience and configuration experience brought by the Citrix VDI technology, and a cloud desktop platform administrator cannot accurately evaluate memory resources required by the user.
In order to solve the above problems, embodiments of the present application provide a cloud desktop resource processing method, apparatus, device, and storage medium, where the method may collect memory usage data of a cloud desktop user terminal at regular time, analyze the memory usage data according to a preset memory adjustment rule, configure a reasonable memory for the cloud desktop user terminal, and implement dynamic adjustment of cloud desktop resources.
In the technical scheme of the application, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the related user data and other information all accord with the regulations of related laws and regulations and do not violate the good customs of the public order.
Optionally, fig. 2 is a schematic diagram of an architecture of a cloud desktop resource processing system according to an embodiment of the present disclosure. On the basis of fig. 1, fig. 2 divides the user cloud desktop resource pool 10 in fig. 1 into a high-configuration desktop pool 100, a medium-configuration desktop pool 101, and a low-configuration desktop pool 102, and fig. 2 further includes a dynamic resource allocation platform 21, where the dynamic resource allocation platform 21 specifically includes a performance analyzer 210, a performance collector 211, an allocation executor 212, and a data storage 213.
Alternatively, the dynamic resource deployment platform 21 may be implemented by a server or a processor.
Wherein, highly join in marriage desktop pond: compared with a middle-matched desktop, the cloud desktop memory is greatly improved, and is suitable for a scene needing to consume high memory resources in long-term operation; a middle desktop pool is matched: the cloud desktop configured conventionally defaults a desktop pool for a user, and is suitable for daily office work and occasions needing to consume certain memory resources during occasional operation; low-allocation table top pool: the system meets the daily basic office use and has no high-consumption memory test scene.
Optionally, the performance collector: the method is used for collecting the memory use condition of each user after logging in the cloud desktop.
Optionally, the data store: and the back-end database is used for storing the performance acquisition indexes. Wherein, the storage field has: collecting a timestamp, a cloud desktop account number and a cloud desktop memory usage amount (GB).
Optionally, the performance analyzer: the dynamic resource allocation platform is used as a core component of the dynamic resource allocation platform and plays the following roles:
and (4) analysis function: and performing secondary analysis and processing on the original data which is acquired and stored in the data storage, and converting the historical record of the memory usage amount of the cloud desktop of the user into acquisition index frequency according to the trigger rule.
And (4) displaying functions: according to the trigger rule, the data analysis result is visualized as a report, and the report comprises the following fields: the cloud desktop account number, the frequency of the collected indexes in the hit rule, the frequency of the total collected indexes and the hit ratio (reverse order).
Issuing a deployment instruction: and pushing the cloud desktop account number of the user and the allocation instruction (ascending allocation/descending allocation) which accord with the allocation rule to an allocation executor.
Optionally, deploying the actuator: and after receiving the cloud desktop account number and the instruction type (upgrade/downgrade configuration) of the user pushed by the performance analyzer, adjusting the cloud desktop configuration of the corresponding user, and taking the corresponding desktop pool configuration into effect when the user logs in the cloud desktop next time.
It is to be understood that the schematic structure in the embodiment of the present application does not constitute a specific limitation to the architecture of the cloud desktop resource processing system. In other possible embodiments of the present application, the foregoing architecture may include more or less components than those shown in the drawings, or combine some components, or split some components, or arrange different components, which may be determined according to practical application scenarios, and is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
It should be understood that the above dynamic resource allocation platform may be implemented by a processor reading instructions in a memory and executing the instructions, or may be implemented by a chip circuit.
In addition, the network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person skilled in the art that along with the evolution of the network architecture and the appearance of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
The technical scheme of the application is described in detail by combining specific embodiments as follows:
optionally, fig. 3 is a schematic flow chart of a cloud desktop resource processing method provided in the embodiment of the present application. The execution subject in the embodiment of the present application may be the dynamic resource allocation platform 21 in fig. 2, the dynamic resource allocation platform 21 may be a server or a processor, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 3, the method comprises the steps of:
s301: and acquiring memory use data of the cloud desktop use terminal within preset analysis time.
The cloud desktop using terminal is terminal equipment for using/logging in the cloud desktop.
Optionally, the method in the embodiment of the present application is not only applicable to memory configuration, but also applicable to configuration of other storage spaces and resources.
Optionally, the memory usage data includes a cloud desktop account number of the cloud desktop usage terminal and memory sizes at different time points.
The preset analysis time may be determined according to actual conditions, and this is not specifically limited in the embodiments of the present application.
Optionally, the memory usage data may be periodically collected by presetting a collection time, for example, the memory usage data is collected every 30 minutes after the cloud desktop usage terminal logs in.
Optionally, after obtaining the memory usage data of the cloud desktop usage terminal within the preset analysis time, the method further includes: and storing the memory use data according to a preset storage format, wherein the preset storage format comprises a collection timestamp of each memory use data, a cloud desktop account number and the cloud desktop memory use amount.
Here, the memory use data can be stored in the embodiment of the application, so that the stored data can be directly called in the subsequent memory use condition analysis, the memory analysis and regulation and control can be conveniently performed according to the stored data, and the user experience and the resource utilization rate are further improved.
S302: and determining whether to adjust the memory allocation type of the cloud desktop use terminal or not according to a preset memory adjustment rule and the memory use data.
Optionally, after determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data, the method further includes: and displaying the memory adjustment data of the cloud desktop use terminal.
Alternatively, the display can be performed on an interface of a server or a cloud desktop use terminal.
Optionally, the memory adjustment data may be displayed in a table manner.
Optionally, the memory adjustment data includes a cloud desktop account number of the cloud desktop usage terminal, a collection index frequency in the hit rule, a total collection index frequency, and a hit ratio. The hit ratio is a ratio of the collection index frequency (collection index frequency in the hit rule) of the preset memory adjustment rule hit by the memory use data to the total collection index frequency.
The embodiment of the application can also realize the visualization of memory resource adjustment, display the memory adjustment data to a user or an operator, facilitate the user to know the resource use condition, and further improve the user experience.
S303: and if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, generating an adjusting instruction so as to allocate memory resources for the cloud desktop use terminal according to the adjusting instruction.
Optionally, if it is determined to adjust the memory allocation type of the cloud desktop user terminal, generating an adjustment instruction to configure memory resources for the cloud desktop user terminal according to the adjustment instruction, and then: and responding to a cloud desktop using request of the cloud desktop using terminal, and configuring memory resources for the cloud desktop using terminal according to the adjusting instruction.
Here, when the cloud desktop using terminal applies for or logs in the cloud desktop for use, the memory resource can be configured for the cloud desktop using terminal according to the adjustment instruction generated by the dynamic memory analysis in the embodiment of the application, so that the user experience and the resource utilization rate are further improved.
According to the cloud desktop using terminal needing to use the cloud desktop, the memory using data of the cloud desktop using terminal can be collected regularly, the memory using data can be analyzed according to the preset memory adjusting rule, whether the memory allocated to the cloud desktop using terminal at present is reasonable can be accurately judged, the adjusting instruction is generated after the fact that the memory allocation is unreasonable is determined, the reasonable memory is configured for the cloud desktop using terminal, dynamic memory resource allocation is achieved, experience of a cloud desktop user is optimized, and the resource utilization rate is improved.
In a possible implementation manner, the resource may be dynamically and accurately allocated according to the current memory allocation types of different memories in the embodiment of the present application, and accordingly, fig. 4 is a schematic flow diagram of another cloud desktop resource processing method provided in the embodiment of the present application, and as shown in fig. 4, the method includes the following steps:
s401: and acquiring memory use data of the cloud desktop use terminal within preset analysis time.
S402: and determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal.
Optionally, the memory allocation type includes a high-allocation desktop pool allocation type, a medium-allocation desktop pool allocation type and a low-allocation resource pool allocation type;
correspondingly, determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal, including:
if the current memory allocation type of the cloud desktop using terminal is a middle-allocation desktop pool allocation type, determining a preset memory adjustment rule that the size of the memory using data is smaller than or equal to a first preset threshold value or the size of the memory using data is larger than or equal to a fifth preset threshold value and smaller than or equal to a sixth threshold value;
if the current memory allocation type of the cloud desktop using terminal is a low-allocation desktop pool allocation type, determining that a preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a second threshold value and smaller than or equal to a third threshold value;
and if the current memory allocation type of the cloud desktop using terminal is the high-allocation desktop pool allocation type, determining a preset memory adjustment rule that the size of the memory using data is smaller than or equal to a fourth preset threshold value.
In the embodiment of the application, different memory adjustment rules are determined according to different current resource allocation conditions, so that accurate regulation and control of storage resources are realized, the memory utilization rate is further improved, and the resource utilization rate is improved.
The first preset threshold, the second preset threshold, the third preset threshold, the fourth preset threshold, the fifth preset threshold, and the sixth preset threshold may be determined according to actual conditions, and the embodiments of the present application do not specifically limit this.
S403: and determining the hit rate of the adjustment rule of the cloud desktop use terminal in the preset analysis time according to the memory use data and the preset memory adjustment rule.
S404: and if the hit rate of the adjustment rule is greater than the preset frequency, determining to adjust the memory allocation type of the cloud desktop using terminal.
The preset frequency here may be determined according to actual conditions, and this is not specifically limited in the embodiments of the present application.
Exemplarily, in table 1, a first preset threshold, a second preset threshold, a third preset threshold, a fourth preset threshold, a fifth preset threshold, and a sixth preset threshold are 3, 3.5, 4, 6.5, 7, and 8, respectively, and the preset frequency is 80%, according to a schematic table of a preset memory adjustment rule provided in the embodiment of the present application. The upgrade rule in the table corresponds to a preset memory adjustment rule.
TABLE 1 Preset memory Regulation rule schematic Table
In one possible implementation, the performance analyzer in FIG. 2 periodically initiates the analysis planning process. And carrying out secondary analysis and processing on the historical record of the memory usage of the cloud desktop within the past 7 days, and converting into acquisition index frequency. And the performance analyzer visually displays the user list report conforming to the rules according to the dynamic resource allocation rules compiled by the cloud desktop administrator in advance. The performance analyzer pushes the cloud desktop user account and the allocation instruction (ascending allocation/descending allocation) which accord with the allocation rule to the allocation executor, and the allocation executor completes the allocation adjustment work of the cloud desktop pool of the user. The whole cloud desktop performance analysis and dynamic allocation work can be automatically completed within 30 minutes, and corresponding desktop pool allocation is performed when the user logs in the cloud desktop next time.
S405: and if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, generating an adjusting instruction so as to allocate memory resources for the cloud desktop use terminal according to the adjusting instruction.
According to the method and the device, the preset memory adjustment rule corresponding to the cloud desktop using terminal is determined according to the current memory allocation type of the cloud desktop using terminal, so that whether the memory using condition of the cloud desktop using terminal is reasonable or not is judged according to the preset memory adjustment rule, specifically, according to the memory using data and the preset memory data of the preset memory adjustment rule, the adjustment rule hit rate of the memory using condition can be calculated, whether the memory allocation type needs to be adjusted or not is accurately judged, the memory utilization rate is further improved, and the using efficiency of the whole resources of the cloud desktop is improved.
Fig. 5 is a schematic structural diagram of a cloud desktop resource processing apparatus provided in an embodiment of the present application, and as shown in fig. 5, the apparatus in the embodiment of the present application includes: an acquisition module 501, a type determination module 502, and a generation module 503. The cloud desktop resource processing device may be the dynamic resource allocation platform 21, the processor, and the server, or a chip or an integrated circuit that implements the functions of the dynamic resource allocation platform 21, the processor, and the server. It should be noted that the division of the obtaining module 501, the type determining module 502, and the generating module 503 is only one division of logical functions, and the two may be integrated or independent physically.
The acquisition module is used for acquiring the memory use data of the cloud desktop use terminal within the preset analysis time;
the type determining module is used for determining whether the memory allocation type of the cloud desktop using terminal is adjusted or not according to preset memory adjusting rules and memory using data;
and the generating module is used for generating an adjusting instruction if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, so as to configure memory resources for the cloud desktop use terminal according to the adjusting instruction.
Optionally, the type determining module includes:
the first determining module is used for determining a preset memory adjusting rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal;
the second determining module is used for determining the hit rate of the adjustment rule of the cloud desktop use terminal in the preset analysis time according to the memory use data and the preset memory adjustment rule;
and the third determining module is used for determining the memory allocation type of the cloud desktop use terminal if the hit rate of the adjustment rule is greater than the preset frequency.
Optionally, the memory allocation type includes a high-allocation desktop pool allocation type, a medium-allocation desktop pool allocation type and a low-allocation resource pool allocation type;
correspondingly, the first determining module is specifically configured to:
if the current memory allocation type of the cloud desktop using terminal is a middle-allocation desktop pool allocation type, determining a preset memory adjustment rule that the size of the memory using data is smaller than or equal to a first preset threshold value or the size of the memory using data is larger than or equal to a fifth preset threshold value and smaller than or equal to a sixth threshold value;
if the current memory allocation type of the cloud desktop using terminal is a low-allocation desktop pool allocation type, determining a preset memory adjustment rule that the size of the memory using data is less than or equal to a second threshold value and less than or equal to a third threshold value;
if the current memory allocation type of the cloud desktop using terminal is a high-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a fourth preset threshold value.
Optionally, after the determining, by the type determining module, whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data, the method further includes:
and the display module is used for displaying the memory adjustment data of the cloud desktop use terminal.
Optionally, the memory adjustment data includes a cloud desktop account number of the cloud desktop usage terminal, a collection index frequency in the hit rule, a total collection index frequency, and a hit ratio.
Optionally, after the obtaining module obtains the memory usage data of the cloud desktop using terminal within the preset analysis time, the apparatus further includes:
the storage module is used for storing the memory use data according to a preset storage format, wherein the preset storage format comprises a collection timestamp of each memory use data, a cloud desktop account number and the memory use amount of the cloud desktop.
Optionally, if the generation module determines to adjust the memory allocation type of the cloud desktop user terminal, then generate an adjustment instruction, so as to configure memory resources for the cloud desktop user terminal according to the adjustment instruction, where the apparatus further includes:
and the configuration processing module is used for responding to the cloud desktop use request of the cloud desktop use terminal and configuring the memory resources for the cloud desktop use terminal according to the adjustment instruction.
Referring to fig. 6, which shows a schematic structural diagram of a cloud desktop resource processing device 600 suitable for implementing the embodiment of the present disclosure, the cloud desktop resource processing device 600 may be a terminal device or a server. Among them, the terminal Device may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a Digital broadcast receiver, a Personal Digital Assistant (PDA), a tablet computer (PAD), a Portable Multimedia Player (PMP), a car navigation terminal (e.g., a car navigation terminal), etc., and a fixed terminal such as a Digital TV, a desktop computer, etc. The cloud desktop resource processing device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the use range of the embodiment of the present disclosure.
As shown in fig. 6, the cloud desktop resource processing apparatus 600 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 601, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage device 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the cloud desktop resource processing apparatus 600 are also stored. The processing device 601, the ROM602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Generally, the following devices may be connected to the I/O interface 605: input devices 606 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 607 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 608 including, for example, magnetic tape, hard disk, etc.; and a communication device 609. The communication means 609 may allow the cloud desktop resource processing apparatus 600 to perform wireless or wired communication with other apparatuses to exchange data. While fig. 6 illustrates a cloud desktop resource processing apparatus 600 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer-readable medium, the computer program comprising program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication means 609, or may be installed from the storage means 608, or may be installed from the ROM 602. The computer program, when executed by the processing device 601, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be included in the cloud desktop resource processing apparatus; or the device can exist independently and is not assembled into the cloud desktop resource processing device.
The computer readable medium carries one or more programs, and when the one or more programs are executed by the cloud desktop resource processing apparatus, the cloud desktop resource processing apparatus is caused to execute the method shown in the above embodiment.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of Network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of a unit does not in some cases constitute a limitation of the unit itself, for example, the first retrieving unit may also be described as a "unit for retrieving at least two internet protocol addresses".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The cloud desktop resource processing device in the embodiment of the present application may be configured to execute the technical solutions in the method embodiments of the present application, and the implementation principle and the technical effect are similar, which are not described herein again.
An embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is used to implement any one of the cloud desktop resource processing methods described above.
The embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is configured to implement the cloud desktop resource processing method of any one of the foregoing methods.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is only a logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (11)
1. A cloud desktop resource processing method is characterized by comprising the following steps:
acquiring memory use data of the cloud desktop use terminal within preset analysis time;
determining whether the memory allocation type of the cloud desktop use terminal is adjusted or not according to a preset memory adjustment rule and the memory use data;
and if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, generating an adjustment instruction so as to configure memory resources for the cloud desktop use terminal according to the adjustment instruction.
2. The method according to claim 1, wherein the determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjustment rule and the memory usage data comprises:
determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal;
determining the hit rate of the adjustment rule of the cloud desktop use terminal in the preset analysis time according to the memory use data and the preset memory adjustment rule;
and if the hit rate of the adjustment rule is greater than the preset frequency, determining to adjust the memory allocation type of the cloud desktop use terminal.
3. The method of claim 2, wherein the memory allocation types include a high-allocation desktop pool allocation type, a medium-allocation desktop pool allocation type, and a low-allocation resource pool allocation type;
correspondingly, the determining a preset memory adjustment rule corresponding to the cloud desktop using terminal according to the current memory allocation type of the cloud desktop using terminal includes:
if the current memory allocation type of the cloud desktop using terminal is a medium-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a first preset threshold value or the size of the memory using data is larger than or equal to a fifth preset threshold value and smaller than or equal to a sixth threshold value;
if the current memory allocation type of the cloud desktop using terminal is a low-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a second threshold value and smaller than or equal to a third threshold value;
and if the current memory allocation type of the cloud desktop using terminal is a high-allocation desktop pool allocation type, determining that the preset memory adjustment rule is that the size of the memory using data is smaller than or equal to a fourth preset threshold value.
4. The method according to any one of claims 1 to 3, wherein after determining whether to adjust the memory allocation type of the cloud desktop usage terminal according to a preset memory adjustment rule and the memory usage data, the method further comprises:
and displaying the memory adjustment data of the cloud desktop use terminal.
5. The method of claim 4, wherein the memory adjustment data comprises a cloud desktop account number of the cloud desktop usage terminal, a hit rule internal collection index frequency, a total collection index frequency, and a hit ratio.
6. The method according to any one of claims 1 to 3, wherein after the obtaining of the memory usage data of the cloud desktop usage terminal within the preset analysis time, the method further comprises:
and storing the memory use data according to a preset storage format, wherein the preset storage format comprises a collection timestamp of each memory use data, a cloud desktop account and the memory use amount of the cloud desktop.
7. The method according to any one of claims 1 to 3, wherein after the step of generating an adjustment instruction if it is determined to adjust the memory allocation type of the cloud desktop user terminal, so as to configure memory resources for the cloud desktop user terminal according to the adjustment instruction, the method further includes:
responding to a cloud desktop using request of the cloud desktop using terminal, and configuring memory resources for the cloud desktop using terminal according to the adjusting instruction.
8. A cloud desktop resource processing apparatus, comprising:
the acquisition module is used for acquiring the memory use data of the cloud desktop use terminal within the preset analysis time;
the type determining module is used for determining whether to adjust the memory allocation type of the cloud desktop using terminal according to a preset memory adjusting rule and the memory using data;
and the generating module is used for generating an adjusting instruction if the memory allocation type of the cloud desktop use terminal is determined to be adjusted, so as to configure memory resources for the cloud desktop use terminal according to the adjusting instruction.
9. A cloud desktop resource processing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the cloud desktop resource processing method of any one of claims 1-7.
10. A computer-readable storage medium having stored therein computer-executable instructions for implementing the cloud desktop resource processing method of any one of claims 1 to 7 when executed by a processor.
11. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any of claims 1 to 7 when executed by a processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211125894.8A CN115470003A (en) | 2022-09-16 | 2022-09-16 | Cloud desktop resource processing method, device, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211125894.8A CN115470003A (en) | 2022-09-16 | 2022-09-16 | Cloud desktop resource processing method, device, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115470003A true CN115470003A (en) | 2022-12-13 |
Family
ID=84333001
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211125894.8A Pending CN115470003A (en) | 2022-09-16 | 2022-09-16 | Cloud desktop resource processing method, device, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115470003A (en) |
-
2022
- 2022-09-16 CN CN202211125894.8A patent/CN115470003A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110008045B (en) | Method, device and equipment for aggregating microservices and storage medium | |
| CN111475298B (en) | Task processing method, device, equipment and storage medium | |
| US20220004480A1 (en) | Log data collection method, log data collection device, storage medium, and log data collection system | |
| JP2020173778A (en) | Method, apparatus, electronic facility, computer readable medium, and computer program for allocating resource | |
| CN111459944B (en) | MR data storage method, device, server and storage medium | |
| CN111985831A (en) | Scheduling method and device of cloud computing resources, computer equipment and storage medium | |
| CN113282611B (en) | Method, device, computer equipment and storage medium for synchronizing stream data | |
| CN110928732A (en) | Server cluster performance sampling analysis method and device and electronic equipment | |
| CN110753089A (en) | Method, device, medium and electronic equipment for managing client | |
| CN111580974B (en) | GPU instance allocation method, device, electronic equipment and computer readable medium | |
| CN113505302A (en) | Method, device and system for supporting dynamic acquisition of buried point data and electronic equipment | |
| CN112561301A (en) | Work order distribution method, device, equipment and computer readable medium | |
| CN111581930A (en) | Online form data processing method and device, electronic equipment and readable medium | |
| CN111274104B (en) | Data processing method, device, electronic equipment and computer readable storage medium | |
| CN116594568A (en) | Data storage method and device, electronic equipment and storage medium | |
| CN115470003A (en) | Cloud desktop resource processing method, device, equipment and storage medium | |
| CN111694672B (en) | Resource allocation method, task submission method, device, electronic equipment and medium | |
| CN110955709B (en) | Data processing method and device and electronic equipment | |
| US9772877B2 (en) | Managing I/O operations in a shared file system | |
| CN110119364B (en) | Method and system for input/output batch submission | |
| CN112100159A (en) | Data processing method and device, electronic equipment and computer readable medium | |
| CN112035256A (en) | Resource allocation method, device, electronic equipment and medium | |
| CN111831527A (en) | Method, apparatus, electronic device, and medium for scanning database performance problems | |
| CN112214469A (en) | Drive test data processing method, device, server and storage medium | |
| CN113672200A (en) | Microservice processing method and device, storage medium and electronic equipment |
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 |