CN117640541B - Cloud server resource allocation method, device, equipment and medium - Google Patents
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Abstract
The application relates to the field of cloud computing, in particular to a cloud server resource allocation method, a device, equipment and a medium, wherein the method comprises the following steps: acquiring task request information and current resource allocation time of a user, and judging whether the current resource allocation time is in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of the task; if yes, acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of the processing task; determining an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value; acquiring the processing efficiency of tasks and the first task number of the tasks to be processed; allocation information of the allocation amount of the additional resources is determined based on the processing efficiency and the first task amount, and allocation is performed based on the allocation information. The application has the effect of improving the accuracy of resource allocation.
Description
Technical Field
The present application relates to the field of cloud computing technologies, and in particular, to a method, an apparatus, a device, and a medium for distributing cloud server resources.
Background
With the progress and development of science and technology, cloud computing services are increasingly widely applied in the life of people, and the application range of cloud servers is also remarkably improved in the process. The cloud server is a simple, high-safety, high-reliability and high-processing-capability computing service, and a user can realize data sharing in a virtual environment, and meanwhile, the application of the cloud computing server is wider.
In the related technology, the time sequence of the resource request and the resource demand are acquired, and the resources corresponding to the resource demand are allocated to the users to be allocated in sequence according to the time sequence so as to be used by the users; however, the resource demand is elastically changed, that is, the direct allocation of the resource corresponding to the resource demand to the user to be allocated may cause the mismatch between the resource demand obtained by the user to be allocated and the actual resource demand, which means that the accuracy of the resource allocation in the related art is poor.
Disclosure of Invention
In order to improve the accuracy of resource allocation, the application provides a cloud server resource allocation method, a device, equipment and a medium.
In a first aspect, the present application provides a cloud server resource allocation method, which adopts the following technical scheme:
a cloud server resource allocation method comprises the following steps:
acquiring task request information and current resource allocation time of a user, and judging whether the current resource allocation time is positioned in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of a task, wherein the task is a task being processed;
If yes, acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of processing the task;
determining an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value;
acquiring the processing efficiency of the task and the first task number of the task to be processed;
And determining allocation information of the extra resource allocation amount based on the processing efficiency and the first task number, and allocating based on the allocation information.
The present application may be further configured in a preferred example, wherein the processing attribute value includes: a time attribute value and a resource demand attribute value, the determining an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value, comprising:
Determining a first additional resource allocation amount corresponding to a time attribute value based on a corresponding relation between the preset time attribute value and the additional resource allocation amount and the time attribute value;
Determining a second additional resource allocation amount corresponding to the resource demand attribute value based on a corresponding relation between a preset resource demand attribute value and the additional resource allocation amount and the resource demand attribute value;
determining a plurality of target historical task identifications based on the task identifications and a plurality of preset historical task identifications;
And acquiring historical additional resource allocation amounts respectively corresponding to all the target historical task identifications, and determining additional resource allocation amounts corresponding to the tasks based on all the historical additional resource allocation amounts, the first additional resource allocation amount and the second additional resource allocation amount.
The present application may be further configured in a preferred example, wherein the determining an additional resource allocation amount corresponding to the task based on all of the historical additional resource allocation amounts, the first additional resource allocation amount, and the second additional resource allocation amount includes:
determining a first average additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts;
acquiring a first weight value corresponding to the time attribute value and a second weight value corresponding to the resource demand attribute value;
Determining a second average additional resource allocation amount based on the first weight value, the first additional resource allocation amount, the second weight value, and the second additional resource allocation amount;
an additional resource allocation amount corresponding to the task is determined based on the first average additional resource allocation amount and the second average additional resource allocation amount.
The present application may be further configured in a preferred example, wherein the determining allocation information of the additional resource allocation amount based on the processing efficiency and the first task number includes:
judging whether the processing efficiency is smaller than a preset processing efficiency threshold value or not, and judging whether the first task number is larger than a preset task number threshold value or not;
If the processing efficiency is smaller than the preset processing efficiency threshold, or if the first task number is larger than the preset task number threshold, determining that the allocation information can be allocated;
otherwise, determining that the allocation information is unallocated.
The present application may be further configured in a preferred example, wherein the preset processing efficiency threshold includes: the determining whether the processing efficiency is smaller than a preset processing efficiency threshold or not includes:
Determining a processing efficiency sum based on the processing efficiencies corresponding to all the tasks respectively;
acquiring a second task number of the tasks, and determining average processing efficiency based on the second task number and the sum of the processing efficiencies;
judging whether the average processing efficiency is smaller than the first preset processing efficiency threshold value or not;
Or alternatively, the first and second heat exchangers may be,
Judging whether the processing efficiency corresponding to each task is smaller than the second preset processing efficiency threshold corresponding to each task.
The present application may be further configured in a preferred example, wherein the allocating based on the allocation information includes:
if the allocation information is the allocation capable, acquiring a task processing progress corresponding to the task;
for each task, determining an initial allocation priority corresponding to the task processing progress based on a corresponding relation between a preset task processing progress and allocation priority and the task processing progress;
Determining a correction coefficient corresponding to the resource demand based on a preset corresponding relation between the resource demand and the correction coefficient and the resource demand, wherein the correction coefficient is used for correcting the initial allocation priority;
and determining a target allocation priority based on the initial allocation priority and the correction coefficient, and allocating the additional resource allocation amount according to the target allocation priority.
The present application may be further configured in a preferred example to determine the preset resource demand peak period, including:
acquiring a scene identifier, and determining a plurality of initial time periods corresponding to the scene identifier based on the scene identifier, wherein the initial time periods represent user activity time periods;
For each initial period, acquiring a plurality of historical task quantity and quantity information corresponding to the initial period;
Determining an average historical task number based on the number information and the historical task number, and judging whether the average historical task number is larger than a preset task number threshold;
Determining a target period based on the average historical task number and a preset average task number, wherein the average historical task number of the target period is greater than the preset average task number;
and determining the target period as the preset resource demand peak period.
In a second aspect, the present application provides a cloud server resource allocation apparatus, which adopts the following technical scheme:
a cloud server resource allocation apparatus, comprising:
The first acquisition module is used for acquiring task request information and current resource allocation time of a user and judging whether the current resource allocation time is in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of a task, wherein the task is a task being processed; if yes, triggering a second acquisition module;
The second acquisition module is used for acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of processing the task;
An additional resource allocation amount determining module, configured to determine an additional resource allocation amount corresponding to the task based on the task identifier and the processing attribute value;
The third acquisition module is used for acquiring the processing efficiency of the tasks and the first task number of the tasks to be processed;
And the allocation module is used for determining the allocation information of the additional resource allocation amount based on the processing efficiency and the first task amount and performing allocation based on the allocation information.
In a third aspect, the present application provides an electronic device, which adopts the following technical scheme:
At least one processor;
A memory;
At least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, the at least one application program configured to: the cloud server resource allocation method according to any one of the first aspects is performed.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
A computer-readable storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform the cloud server resource allocation method according to any of the first aspects.
In summary, the application has the following beneficial technical effects:
Acquiring task identification, resource demand and current resource allocation time of a user; judging whether the current resource allocation time is in a preset resource demand peak period, and determining the additional resource demand to better cope with the elastic change of the resource demand of the user task request when the current resource allocation time is in the resource demand peak period, wherein the accurate additional resource allocation amount can avoid that the additional resource allocation amount occupies too much resources of other task requests and the additional resource allocation amount is too little to improve the processing efficiency of the user task request; if yes, obtaining a processing attribute value; along with the increase of the complexity of task requests, the resource demand increases, so that more accurate additional resource allocation quantity is determined by taking the task identification and the processing attribute value as references; the processing efficiency and the first task number are obtained again, the processing efficiency changes along with the change of the additional resource allocation amount, namely, the processing efficiency can be improved when the additional resource allocation amount is increased, and the pressure of the subsequent task processing can be relieved by allocating the additional resource allocation amount when the first task number is increased; compared with the prior art, the method and the device for allocating the resources only according to the user resource demand and the request time sequence, the method and the device for allocating the resources based on the user task demand have the advantages that the additional resource allocation quantity is determined, the allocation of the additional resource allocation quantity is carried out by referring to the processing progress of each user task request and the number of users to be allocated on the basis of meeting the resource demand of each user task request, the elastic change of the user resource demand is met, meanwhile, the accurate allocation of the resources is realized, and the technical problem of poor resource allocation accuracy in the prior art is solved.
Drawings
Fig. 1 is a schematic view of a cloud server resource allocation scenario provided in an embodiment of the present application.
Fig. 2 is a flow chart of a cloud server resource allocation method according to an embodiment of the present application.
Fig. 3 is a schematic structural diagram of a cloud server resource allocation device according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Description of the embodiments
The present application will be described in further detail with reference to fig. 1 to 4.
The present embodiment is merely illustrative of the present application and is not intended to limit the present application, and those skilled in the art, after having read the present specification, may make modifications to the present embodiment without creative contribution as necessary, but are protected by patent laws within the scope of the present application.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In this context, unless otherwise specified, the term "/" generally indicates that the associated object is an "or" relationship.
As shown in fig. 1, a schematic view of a cloud server resource allocation scenario provided in an embodiment of the present application is shown, an electronic device receives task request information of a user side device in a wireless transmission or wired transmission manner, and obtains processing efficiency and task number of a task while determining an additional resource allocation amount according to the task request information, determines allocation information of the additional resource allocation amount, and generates an allocation instruction according to the allocation information, and sends the allocation instruction to the user side device so as to allocate the additional resource allocation amount.
Embodiments of the application are described in further detail below with reference to the drawings.
The embodiment of the application provides a cloud server resource allocation method, which is executed by electronic equipment, wherein the electronic equipment can be a server, and the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server for providing cloud computing service. The servers may be directly or indirectly connected through wired or wireless communication, and as shown in fig. 2, the method includes steps S101, S102, S103, S104, and S105, where:
Step S101: acquiring task request information and current resource allocation time of a user, and judging whether the current resource allocation time is in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of a task, the task being a task being processed.
Specifically, the task request information of the user is sent to the electronic device by the user side device, so that the task corresponding to the task request information obtained by the electronic device is a task in a processing state. The current resource allocation time is determined based on the corresponding time when the request information is received. The process of determining the peak period of the preset resource demand, which is determined by the technician according to a plurality of historical data, can be referred to in the following embodiments. Judging the current resource allocation time and the preset resource demand peak time period, if the current resource allocation time is within the preset resource demand peak time period, indicating that the current cloud server resources are tense, reasonably allocating the cloud server resources to improve the utilization rate of the cloud server resources, avoiding the situation of cloud server congestion, and executing step S102; otherwise, the current cloud server resources are abundant, and the cloud server resources can be directly allocated.
Step S102: if yes, acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of the processing task.
Specifically, the processing attribute value may be user-defined and sent to the electronic device; the corresponding relation between the resource demand and the processing attribute value can be set by a technician according to working experience and input into the electronic equipment, and the processing attribute value is increased along with the increase of the resource demand in the corresponding relation.
Step S103: based on the task identification and the processing attribute value, an additional resource allocation amount corresponding to the task is determined.
In particular, the specific process of determining the allocation amount of additional resources based on the task identification and the processing attribute value may refer to the following embodiments. It will be appreciated that as the processing attribute value increases, the amount of additional resource allocation increases to better cope with the allocation of cloud server resources.
Step S104: and acquiring the processing efficiency of the tasks and the first task number of the tasks to be processed.
Specifically, the processing efficiency within the first preset duration may be obtained, and the task corresponding to the processing efficiency is a task in a processing state; the first task number is predicted, namely, the first task number in a second preset time period is predicted, and the first preset time period and the second preset time period are set by a technician according to working experience and are input into the electronic equipment in advance. The specific process for predicting the first task number comprises the following steps: acquiring a plurality of history periods corresponding to a second preset duration, acquiring the number of tasks corresponding to each history period, determining the average number of tasks corresponding to the history period based on all the history periods and the respective corresponding number of tasks, and determining the average number of tasks as the first number of tasks of the tasks to be processed. Wherein, the period of time corresponding to the second preset duration is later than the period of time corresponding to the first preset duration.
Step S105, determining the allocation information of the additional resource allocation amount based on the processing efficiency and the first task number, and allocating based on the allocation information.
Specifically, the allocation information of the allocation amount of the additional resources determined based on the processing efficiency and the first task amount may refer to the following embodiments, and it may be understood that the processing efficiency and the first task amount are taken as references, and the allocation amount of the additional resources is allocated, so that the normal processing efficiency of each user task is ensured, and meanwhile, the problem that the task to be processed cannot be processed in time due to the excessive task amount is avoided. The allocation information includes: can be dispensed or cannot be dispensed. The specific procedure of the allocation according to the allocation information can be referred to the following embodiments.
In the embodiment of the application, the task identification, the resource demand and the current resource allocation time of the user are acquired; judging whether the current resource allocation time is in a preset resource demand peak period, and determining the additional resource demand to better cope with the elastic change of the resource demand of the user task request when the current resource allocation time is in the resource demand peak period, wherein the accurate additional resource allocation amount can avoid that the additional resource allocation amount occupies too much resources of other task requests and the additional resource allocation amount is too little to improve the processing efficiency of the user task request; if yes, obtaining a processing attribute value; along with the increase of the complexity of task requests, the resource demand increases, so that more accurate additional resource allocation quantity is determined by taking the task identification and the processing attribute value as references; the processing efficiency and the first task number are obtained again, the processing efficiency changes along with the change of the additional resource allocation amount, namely, the processing efficiency can be improved when the additional resource allocation amount is increased, and the pressure of the subsequent task processing can be relieved by allocating the additional resource allocation amount when the first task number is increased; compared with the prior art, the method and the device for allocating the resources only according to the user resource demand and the request time sequence, the method and the device for allocating the resources based on the user task demand have the advantages that the additional resource allocation quantity is determined, the allocation of the additional resource allocation quantity is carried out by referring to the processing progress of each user task request and the number of users to be allocated on the basis of meeting the resource demand of each user task request, the elastic change of the user resource demand is met, meanwhile, the accurate allocation of the resources is realized, and the technical problem of poor resource allocation accuracy in the prior art is solved.
In one possible implementation manner of the embodiment of the present application, the processing the attribute value includes: a time attribute value and a resource demand attribute value, step S103 determines an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value, including:
determining a first additional resource allocation amount corresponding to the time attribute value based on a corresponding relation between the preset time attribute value and the additional resource allocation amount and the time attribute value;
Determining a second additional resource allocation amount corresponding to the resource demand attribute value based on a corresponding relation between the preset resource demand attribute value and the additional resource allocation amount and the resource demand attribute value;
determining a plurality of target historical task identifications based on the task identifications and a plurality of preset historical task identifications;
and acquiring the historical additional resource allocation amounts respectively corresponding to all the target historical task identifications, and determining the additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts, the first additional resource allocation amount and the second additional resource allocation amount.
Specifically, the corresponding relation between the preset time attribute value and the additional resource allocation amount is set by a technician according to working experience; the time attribute value characterizes the time spent for processing the task, and is determined by the electronic device according to a plurality of historical data, and the embodiment of the application does not limit the determination process of the time attribute value, and it can be understood that when the time attribute value is larger, the more cloud server resources are required for completing the task, and the complexity of the task is higher, so that the corresponding additional resource allocation amount is larger, that is, the first additional resource allocation amount is larger. The corresponding relation between the preset resource demand attribute value and the additional resource allocation amount is set by a technician according to working experience. The resource demand attribute value characterizes that the larger the resource demand amount for completing the task is, the more difficult the task is to complete, and thus the corresponding second additional resource allocation amount is increased. Multiple historical task identifications can be obtained from the historical database, and then the task identifications and the historical task identifications are matched by using a text matching algorithm so as to determine the historical task identifications which are the same as the task identifications, namely the target historical task identifications. The historical additional resource allocation amount corresponding to the target historical task identifier can be obtained from the historical database. The specific process of determining the additional resource allocation amount according to all the historical additional resource allocation amounts, the first additional resource allocation amount and the second additional resource allocation amount may refer to the following embodiments, and the embodiments of the present application are not repeated herein.
In the embodiment of the application, when the task processing needs to take a longer time, the processing difficulty of the task is higher, and more additional resources are allocated for improving the task processing efficiency, so that the allocation amount of the first additional resources is determined according to the time attribute value, and the accuracy of the allocation amount of the first additional resources is effectively improved; when the resource demand of the task is increased, the resource demand attribute value is increased, and more additional resource allocation amount is allocated to improve the task processing efficiency, so that the accuracy of determining the second additional resource allocation amount is effectively improved according to the resource demand attribute value; acquiring a task identifier and a historical task identifier, and determining a target historical task identifier so as to take the target historical task identifier as a reference; the allocation amount of the additional resources of the same task identifier is similar, so that the determination of the allocation amount of the additional resources from multiple dimensions is realized according to the historical allocation amount of the additional resources, the first allocation amount of the additional resources and the second allocation amount of the additional resources, and the purpose of improving the accuracy of the allocation amount of the additional resources is achieved.
In a possible implementation manner of the embodiment of the present application, step S103 determines, based on all the historical additional resource allocation amounts, the first additional resource allocation amount, and the second additional resource allocation amount, an additional resource allocation amount corresponding to the task, including:
Determining a first average additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts;
Acquiring a first weight value corresponding to the time attribute value and a second weight value corresponding to the resource demand attribute value;
Determining a second average additional resource allocation amount based on the first weight value, the first additional resource allocation amount, the second weight value, and the second additional resource allocation amount;
An additional resource allocation amount corresponding to the task is determined based on the first average additional resource allocation amount and the second average additional resource allocation amount.
Specifically, the number of target historical task identifications is obtained, the sum of the historical additional resource allocation amounts is calculated according to all the historical additional resource allocation amounts, and the first average additional resource allocation amount is calculated according to the number and the sum of the historical additional resource allocation amounts. The first weight value and the second weight value are set by a technician according to working experience and are input into the electronic equipment in advance. In the embodiment of the application, the first weight value represents the influence degree of task processing time on determining the allocation amount of the additional resources, and the second weight value represents the influence degree of the resource demand amount required for processing the task on determining the allocation amount of the additional resources. The second average additional resource allocation amount can be calculated according to a calculation formula: second average additional resource allocation amount =Wherein/>Characterizing a first weight value,/>Characterizing a first additional resource allocation amount,/>The second weight value is characterized by a second weight value,A second additional resource allocation amount is characterized. The average additional resource allocation amount may be determined based on the average additional resource allocation amount, or the sum of the additional resource allocation amounts, and in one achievable manner, the average additional resource allocation amounts of the first average additional resource allocation amount and the second average additional resource allocation amount may be calculated, and it may be understood that the average additional resource allocation amount may more accurately reflect the overall trend of change; in another implementation, a sum of the allocations of the first average additional resource allocation amount and the second average additional resource allocation amount is calculated, and the sum of the allocations is determined as the additional resource allocation amount.
In the embodiment of the application, a first average additional resource allocation amount is determined according to the historical additional resource allocation amount; the method comprises the steps of obtaining a first weight value corresponding to a time attribute value and a second weight value corresponding to a resource demand attribute value, determining a second average additional resource allocation amount according to the first weight value, the first additional resource allocation amount, the second weight value and the second additional resource allocation amount, performing targeted calculation to effectively improve the accuracy of the second average additional resource allocation amount, and determining the additional resource allocation amount corresponding to a task according to the accurate first average additional resource allocation amount and the accurate second average additional resource allocation amount so as to effectively improve the accuracy of the determination of the additional resource allocation amount.
In one possible implementation manner of the embodiment of the present application, step S105 determines allocation information of an additional resource allocation amount based on the processing efficiency and the first task number, including:
judging whether the processing efficiency is smaller than the preset processing efficiency or not, and judging whether the first task number is larger than a preset task number threshold value or not;
If the processing efficiency is smaller than a preset processing efficiency threshold, or the first task number is larger than the preset task number threshold, determining that the allocation information can be allocated;
Otherwise, determining the allocation information as unallocated.
Specifically, the preset processing efficiency and the preset task number threshold are set by a technician according to work, and it can be understood that when the processing efficiency is smaller than the preset processing efficiency threshold, it is indicated that the resource demand cannot guarantee the normal efficiency of the task when the task is executed, so that the additional resource allocation amount needs to be allocated to improve the processing efficiency of the task; when the number is greater than the threshold value of the number of the preset tasks, indicating that a large number of tasks are executed in the second preset time period, enough cloud server resources should be reserved to ensure normal execution of the tasks in the second preset time period, so that allocation information is determined to be capable of being allocated; otherwise, determining the allocation information as an allocation amount of the extra resources which cannot be allocated.
In the embodiment of the application, when the processing efficiency is slower, the processing efficiency can be effectively improved by distributing the additional resource distribution amount to the corresponding resources; when the number of tasks to be processed is too large, the processing efficiency of the task in the processing state can be improved by distributing the additional resource distribution amount, so that the occurrence rate of the problem of resource congestion of the subsequent task to be processed is reduced, and therefore whether the processing efficiency is smaller than a preset processing efficiency threshold value or not and whether the first task number is larger than the preset task number threshold value or not are required to be judged; when the processing efficiency is smaller than a preset processing efficiency threshold, or the first task number is larger than the preset task number threshold, determining that the allocation information can be allocated; otherwise, the allocation information is determined to be unable to be allocated, so that the accuracy of allocation information determination is effectively improved.
In one possible implementation manner of the embodiment of the present application, the preset processing efficiency threshold includes: step S105 of determining whether the processing efficiency is less than the preset processing efficiency threshold, includes:
Determining a processing efficiency sum based on the processing efficiencies corresponding to all the tasks respectively;
acquiring a second task number of the tasks, and determining average processing efficiency based on the sum of the second task number and the processing efficiency;
judging whether the average processing efficiency is smaller than a first preset processing efficiency threshold value or not;
Or alternatively, the first and second heat exchangers may be,
And judging whether the processing efficiency corresponding to each task is smaller than a second preset processing efficiency threshold corresponding to each task.
Specifically, the processing efficiency sum can be calculated by a summation calculation formula. The electronic equipment automatically records tasks to obtain a second task number; the average processing efficiency can be calculated by an average value calculation formula. And judging the average processing efficiency and a first preset processing efficiency threshold. And judging the processing efficiency and the corresponding second preset processing efficiency threshold value respectively for each task, wherein the corresponding second preset processing efficiency threshold value of each task can be the same or different. In the embodiment of the application, the task corresponding to the second number of tasks is a task in a processing state.
Correspondingly, if the average processing efficiency is smaller than a first preset processing efficiency threshold, or if any task processing is smaller than a second preset processing threshold, or the first task number is larger than a preset task number threshold, determining that the allocation information can be allocated; otherwise, determining the allocation information as unallocated.
In the embodiment of the application, the total processing efficiency is determined, the second task number of the tasks is obtained, and the average processing efficiency is determined according to the second task number and the total processing efficiency, so that the average processing efficiency can more accurately reflect the overall task processing efficiency, and the average processing efficiency and a first preset processing efficiency threshold value are judged; judging the corresponding processing efficiency and the corresponding second preset processing efficiency threshold value for each task; the multi-dimensional consideration is realized from the processing efficiency of the whole task and the processing efficiency of the single task, so that the processing efficiency and the accurate judgment of the preset processing efficiency threshold value are effectively improved.
In one possible implementation manner of the embodiment of the present application, step S105 performs allocation based on allocation information, including:
If the allocation information is allocable, acquiring a task processing progress corresponding to the task;
for each task, determining an initial allocation priority corresponding to the task processing progress based on a corresponding relation between the preset task processing progress and allocation priority and the task processing progress;
Determining a correction coefficient corresponding to the resource demand based on a preset corresponding relation between the resource demand and the correction coefficient and the resource demand, wherein the correction coefficient is used for correcting the initial allocation priority;
And determining a target allocation priority based on the initial allocation priority and the correction coefficient, and allocating the additional resource allocation amount according to the target allocation priority.
Specifically, the current data processing amount of the task is obtained, the occupation ratio of the current data processing amount and the total data processing amount is calculated, and the occupation ratio is used as the task processing progress. The corresponding relation between the preset task processing progress and the allocation priority is set by a technician according to working experience, and it can be understood that in the embodiment of the application, the faster the task processing progress is, the closer the task is to the completion time of the task, the higher the corresponding initial allocation priority is, and the allocation of the additional resource allocation amount to the task is beneficial to the acceleration of the task processing progress; in the embodiment of the present application, if the value of the initial allocation priority represents 89, the corresponding initial allocation priority may be determined to be the first priority according to the corresponding relationship between the value and the initial allocation priority. The corresponding relation between the preset resource demand and the correction coefficient is set by a technician according to working experience, and the corresponding correction coefficient can be determined. It will be appreciated that when the resource demand of a task is large, the corresponding additional resource allocation amount is large, and even if the corresponding additional resource allocation amount is allocated to the task, it still takes a long time, so that the additional resource allocation amount may be preferentially allocated to the task with a small resource demand. The target allocation priority can be obtained through calculation through a calculation formula, the target value=initial value is a correction coefficient, the initial value represents a value corresponding to the initial allocation priority, and then the target allocation priority is determined according to the corresponding relation between the value and the allocation priority and the target value. And sequencing according to the priorities corresponding to all the tasks, and distributing the additional resource allocation amount corresponding to all the tasks to the corresponding tasks so as to effectively improve the task processing efficiency and release more occupied cloud server resources.
In the embodiment of the application, the task processing progress of the task is acquired, when the task processing progress is faster, the task is preferentially allocated, the processing progress of the task is favorably accelerated, and the corresponding allocation priority is higher, so that the initial allocation priority is determined according to the task processing progress, and the accuracy of determining the initial allocation priority is effectively improved; when the resource demand of the task is large, it may still take a long time to complete the task to perform the priority allocation, so the correction coefficient should be determined to correct the initial allocation priority; and determining a correction coefficient from the dimension of the resource demand to obtain a target allocation priority on the basis of determining the accurate initial allocation priority, and allocating the additional resource allocation amount according to the target allocation priority so as to effectively improve the allocation accuracy.
One possible implementation manner of the embodiment of the present application, determining a preset resource demand peak period includes:
acquiring a scene identifier, determining a plurality of initial time periods corresponding to the scene identifier based on the scene identifier, wherein the initial time periods represent user activity time periods;
for each initial period, acquiring a plurality of historical task quantity and quantity information corresponding to the initial period;
Determining the average historical task number based on the number information and the historical task number, and judging whether the average historical task number is larger than a preset task number threshold;
determining a target period based on the average historical task number and a preset average task number, wherein the average historical task number of the target period is larger than the preset average task number;
And determining the target period as a preset resource demand peak period.
Specifically, the user identifier may be obtained, and the scene identifier is determined according to the user identifier, for example, when the user identifier is 3 class three of academy a, the scene identifier is determined to be school; when the user identifier is the company B operation and maintenance part Lifour, determining that the scene identifier is an enterprise; and determining a plurality of initial time periods according to the corresponding relation between the scene identification and the initial time periods, wherein the corresponding relation is set by a technician according to working experience. When the scene mark is a school, the initial period corresponds to the school time period of the students; when the scene is identified as an enterprise, then the initial period is a person on duty period. The number of the historical tasks corresponding to the initial period can be obtained from the historical information base; the quantity information characterizes the quantity of the initial period; the average historical task number can be calculated according to an average calculation formula. The preset average task number is set by a technician according to working experience, whether the average historical task number is larger than the preset average task number is judged, and an initial period corresponding to the average historical task number larger than the preset average task number is determined as a target period, namely a preset resource demand peak period.
In the embodiment of the application, under different scenes, the demand time periods for the cloud server resources are different, so that the initial time period corresponding to the scene needs to be determined; and then the number of the initial time periods and the number of the historical tasks in the initial time periods are obtained, the average historical task number is determined according to the number and the historical task number, and the change trend of the task number corresponding to the initial time periods can be more accurately reflected by the average historical task number, so that the target time periods are determined according to the average historical task number and the preset average task number, and the target time periods are determined to be the preset resource demand peak time periods, so that the accuracy of the preset resource demand peak time periods is effectively improved.
The foregoing embodiments describe a cloud server resource allocation method from the perspective of a method flow, and the following embodiments describe a cloud server resource allocation device from the perspective of a virtual module or a virtual unit, which are specifically described in the following embodiments.
An embodiment of the present application provides a cloud server resource allocation device, as shown in fig. 3, where the cloud server resource allocation device specifically may include:
The first obtaining module 201 is configured to obtain task request information and a current resource allocation time of a user, and determine whether the current resource allocation time is within a preset resource demand peak period, where the task request information includes: task identification and resource demand of a task, wherein the task is a task being processed; if yes, triggering the second acquisition module 202;
a second obtaining module 202, configured to obtain a processing attribute value of the task, where the processing attribute value is used to describe complexity of the processing task;
An extra resource allocation amount determining module 203, configured to determine an extra resource allocation amount corresponding to the task based on the task identifier and the processing attribute value;
A third obtaining module 204, configured to obtain a processing efficiency of a task and a first task number of a task to be processed;
An allocation module 205, configured to determine allocation information of the allocation amount of the additional resources based on the processing efficiency and the first task number, and allocate based on the allocation information.
In one possible implementation manner of the embodiment of the present application, the processing the attribute value includes: the time attribute value and the resource demand attribute value, and the extra resource allocation amount determining module 203 is specifically configured to, when determining an extra resource allocation amount corresponding to a task based on the task identifier and the processing attribute value:
determining a first additional resource allocation amount corresponding to the time attribute value based on a corresponding relation between the preset time attribute value and the additional resource allocation amount and the time attribute value;
Determining a second additional resource allocation amount corresponding to the resource demand attribute value based on a corresponding relation between the preset resource demand attribute value and the additional resource allocation amount and the resource demand attribute value;
determining a plurality of target historical task identifications based on the task identifications and a plurality of preset historical task identifications;
and acquiring the historical additional resource allocation amounts respectively corresponding to all the target historical task identifications, and determining the additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts, the first additional resource allocation amount and the second additional resource allocation amount.
In one possible implementation manner of the embodiment of the present application, the extra resource allocation amount determining module 203 is specifically configured to, when determining the extra resource allocation amount corresponding to the task based on all the historical extra resource allocation amounts, the first extra resource allocation amount and the second extra resource allocation amount:
Determining a first average additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts;
Acquiring a first weight value corresponding to the time attribute value and a second weight value corresponding to the resource demand attribute value;
Determining a second average additional resource allocation amount based on the first weight value, the first additional resource allocation amount, the second weight value, and the second additional resource allocation amount;
An additional resource allocation amount corresponding to the task is determined based on the first average additional resource allocation amount and the second average additional resource allocation amount.
In one possible implementation manner of the embodiment of the present application, when the allocation module 205 performs allocation information for determining the allocation amount of the additional resource based on the processing efficiency and the first task number, the allocation module is specifically configured to:
Judging whether the processing efficiency is smaller than a preset processing efficiency threshold value or not, and judging whether the first task number is larger than the preset task number threshold value or not;
If the processing efficiency is smaller than a preset processing efficiency threshold, or if the first task number is larger than the preset task number threshold, determining that the allocation information can be allocated;
Otherwise, determining the allocation information as unallocated.
In one possible implementation manner of the embodiment of the present application, the preset processing efficiency threshold includes: the allocation module 205 is specifically configured to, when executing the determination of whether the processing efficiency is less than the preset processing efficiency threshold, perform:
Determining a processing efficiency sum based on the processing efficiencies corresponding to all the tasks respectively;
acquiring a second task number of the tasks, and determining average processing efficiency based on the sum of the second task number and the processing efficiency;
judging whether the average processing efficiency is smaller than a first preset processing efficiency threshold value or not;
Or alternatively, the first and second heat exchangers may be,
And judging whether the processing efficiency corresponding to each task is smaller than a second preset processing efficiency threshold corresponding to each task.
In one possible implementation manner of the embodiment of the present application, the allocation module 205 is specifically configured to, when performing allocation based on allocation information:
If the allocation information is allocable, acquiring a task processing progress corresponding to the task;
for each task, determining an initial allocation priority corresponding to the task processing progress based on a corresponding relation between the preset task processing progress and allocation priority and the task processing progress;
Determining a correction coefficient corresponding to the resource demand based on a preset corresponding relation between the resource demand and the correction coefficient and the resource demand, wherein the correction coefficient is used for correcting the initial allocation priority;
And determining a target allocation priority based on the initial allocation priority and the correction coefficient, and allocating the additional resource allocation amount according to the target allocation priority.
In one possible implementation manner of the embodiment of the present application, the cloud server resource allocation device further includes:
the resource demand peak period determining module is used for:
acquiring a scene identifier, determining a plurality of initial time periods corresponding to the scene identifier based on the scene identifier, wherein the initial time periods represent user activity time periods;
for each initial period, acquiring a plurality of historical task quantity and quantity information corresponding to the initial period;
Determining the average historical task number based on the number information and the historical task number, and judging whether the average historical task number is larger than a preset task number threshold;
determining a target period based on the average historical task number and a preset average task number, wherein the average historical task number of the target period is larger than the preset average task number;
And determining the target period as a preset resource demand peak period.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, a specific working process of the cloud server resource allocation apparatus described above may refer to a corresponding process in the foregoing method embodiment, which is not described herein again.
In an embodiment of the present application, as shown in fig. 4, an electronic device shown in fig. 4 includes: a processor 301 and a memory 303. Wherein the processor 301 is coupled to the memory 303, such as via a bus 302. Optionally, the electronic device may also include a transceiver 304. It should be noted that, in practical applications, the transceiver 304 is not limited to one, and the structure of the electronic device is not limited to the embodiment of the present application.
The Processor 301 may be a CPU (Central Processing Unit ), general purpose Processor, DSP (DIGITAL SIGNAL Processor, data signal Processor), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field Programmable GATE ARRAY ) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. Processor 301 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 302 may include a path to transfer information between the components. Bus 302 may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. Bus 302 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 4, but not only one bus or type of bus.
The Memory 303 may be, but is not limited to, a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, an EEPROM (ELECTRICALLY ERASABLE PROGRAMMABLE READ ONLY MEMORY ), a CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 303 is used for storing application program codes for executing the inventive arrangements and is controlled to be executed by the processor 301. The processor 301 is configured to execute the application code stored in the memory 303 to implement what is shown in the foregoing method embodiments.
Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. But may also be a server or the like. The electronic device shown in fig. 4 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the application.
Embodiments of the present application provide a computer-readable storage medium having a computer program stored thereon, which when run on a computer, causes the computer to perform the corresponding method embodiments described above.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
The foregoing is only a partial embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and adaptations can be made without departing from the principles of the present application, and such modifications and adaptations are intended to be comprehended within the scope of the present application.
Claims (10)
1. The cloud server resource allocation method is characterized by comprising the following steps of:
acquiring task request information and current resource allocation time of a user, and judging whether the current resource allocation time is positioned in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of a task, wherein the task is a task being processed;
If yes, acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of processing the task;
determining an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value;
acquiring the processing efficiency of the task and the first task number of the task to be processed;
And determining allocation information of the extra resource allocation amount based on the processing efficiency and the first task number, and allocating based on the allocation information.
2. The cloud server resource allocation method of claim 1, wherein the processing attribute value comprises: a time attribute value and a resource demand attribute value, the determining an additional resource allocation amount corresponding to the task based on the task identification and the processing attribute value, comprising:
Determining a first additional resource allocation amount corresponding to a time attribute value based on a corresponding relation between the preset time attribute value and the additional resource allocation amount and the time attribute value;
Determining a second additional resource allocation amount corresponding to the resource demand attribute value based on a corresponding relation between a preset resource demand attribute value and the additional resource allocation amount and the resource demand attribute value;
determining a plurality of target historical task identifications based on the task identifications and a plurality of preset historical task identifications;
And acquiring historical additional resource allocation amounts respectively corresponding to all the target historical task identifications, and determining additional resource allocation amounts corresponding to the tasks based on all the historical additional resource allocation amounts, the first additional resource allocation amount and the second additional resource allocation amount.
3. The cloud server resource allocation method of claim 2, wherein said determining an additional resource allocation amount corresponding to the task based on all of the historical additional resource allocation amounts, the first additional resource allocation amount, and the second additional resource allocation amount comprises:
determining a first average additional resource allocation amount corresponding to the task based on all the historical additional resource allocation amounts;
acquiring a first weight value corresponding to the time attribute value and a second weight value corresponding to the resource demand attribute value;
Determining a second average additional resource allocation amount based on the first weight value, the first additional resource allocation amount, the second weight value, and the second additional resource allocation amount;
an additional resource allocation amount corresponding to the task is determined based on the first average additional resource allocation amount and the second average additional resource allocation amount.
4. The cloud server resource allocation method according to claim 1, wherein the determining allocation information of the additional resource allocation amount based on the processing efficiency and the first task number includes:
judging whether the processing efficiency is smaller than a preset processing efficiency threshold value or not, and judging whether the first task number is larger than a preset task number threshold value or not;
If the processing efficiency is smaller than the preset processing efficiency threshold, or if the first task number is larger than the preset task number threshold, determining that the allocation information can be allocated;
otherwise, determining that the allocation information is unallocated.
5. The cloud server resource allocation method according to claim 4, wherein the preset processing efficiency threshold includes: the determining whether the processing efficiency is smaller than a preset processing efficiency threshold or not includes:
Determining a processing efficiency sum based on the processing efficiencies corresponding to all the tasks respectively;
acquiring a second task number of the tasks, and determining average processing efficiency based on the second task number and the sum of the processing efficiencies;
judging whether the average processing efficiency is smaller than the first preset processing efficiency threshold value or not;
Or alternatively, the first and second heat exchangers may be,
Judging whether the processing efficiency corresponding to each task is smaller than the second preset processing efficiency threshold corresponding to each task.
6. The cloud server resource allocation method according to claim 4, wherein said allocating based on the allocation information comprises:
if the allocation information is the allocation capable, acquiring a task processing progress corresponding to the task;
for each task, determining an initial allocation priority corresponding to the task processing progress based on a corresponding relation between a preset task processing progress and allocation priority and the task processing progress;
Determining a correction coefficient corresponding to the resource demand based on a preset corresponding relation between the resource demand and the correction coefficient and the resource demand, wherein the correction coefficient is used for correcting the initial allocation priority;
and determining a target allocation priority based on the initial allocation priority and the correction coefficient, and allocating the additional resource allocation amount according to the target allocation priority.
7. The cloud server resource allocation method of claim 1, wherein determining the preset resource demand peak period comprises:
acquiring a scene identifier, and determining a plurality of initial time periods corresponding to the scene identifier based on the scene identifier, wherein the initial time periods represent user activity time periods;
For each initial period, acquiring a plurality of historical task quantity and quantity information corresponding to the initial period;
Determining an average historical task number based on the number information and the historical task number, and judging whether the average historical task number is larger than a preset task number threshold;
Determining a target period based on the average historical task number and a preset average task number, wherein the average historical task number of the target period is greater than the preset average task number;
and determining the target period as the preset resource demand peak period.
8. A cloud server resource allocation apparatus, comprising:
The first acquisition module is used for acquiring task request information and current resource allocation time of a user and judging whether the current resource allocation time is in a preset resource demand peak period or not, wherein the task request information comprises: task identification and resource demand of a task, wherein the task is a task being processed; if yes, triggering a second acquisition module;
The second acquisition module is used for acquiring a processing attribute value of the task, wherein the processing attribute value is used for describing the complexity of processing the task;
An additional resource allocation amount determining module, configured to determine an additional resource allocation amount corresponding to the task based on the task identifier and the processing attribute value;
The third acquisition module is used for acquiring the processing efficiency of the tasks and the first task number of the tasks to be processed;
And the allocation module is used for determining the allocation information of the additional resource allocation amount based on the processing efficiency and the first task amount and performing allocation based on the allocation information.
9. An electronic device, comprising:
At least one processor;
A memory;
at least one application program, wherein the at least one application program is stored in the memory and configured to be executed by the at least one processor, the at least one application program configured to: the cloud server resource allocation method according to any one of claims 1 to 7 is performed.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed in a computer, causes the computer to perform the cloud server resource allocation method of any of claims 1 to 7.
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