CN113076193B - Memory optimization method, device and computer readable storage medium - Google Patents
Memory optimization method, device and computer readable storage medium Download PDFInfo
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- 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
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- 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
- G06F9/5022—Mechanisms to release resources
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Abstract
The present disclosure relates to a method, apparatus, and computer-readable storage medium for memory optimization, the method comprising: monitoring memory overhead information of each module in an application program; determining the memory optimization priority of each module based on the memory overhead information; and based on the memory optimization priority, performing memory release. In the embodiment of the disclosure, the memory optimization priority of each module can be determined by monitoring the memory overhead information of each module in the running process of the application program, so that the memory release is performed based on the memory optimization priority, the problems of crashing, blocking, scalding and the like of the application program can be reduced, and the user experience is improved.
Description
Technical Field
The present disclosure relates to the field of memory optimization technologies, and in particular, to a memory optimization method, apparatus, and computer readable storage medium.
Background
In the application program (APP) use process, problems such as breakdown, clamping, hair scalding and the like of the APP can be caused by large memory overhead of the APP, and user experience is affected. In the existing technical scheme for solving the problem of large memory overhead of APP, most of the technical scheme can only determine the condition of code memory overhead in the development process, then solve the problem of large memory overhead, and only give a solution after finding the problem, so that the solution has high maintenance cost and can not immediately solve the problem of large memory overhead.
Therefore, there is a need to provide a scheme for optimizing the memory, so as to record the memory overhead in real time and optimize the memory in the running process of the APP, reduce the problems of breakdown, jamming, hot burning and the like of the APP, and improve the user experience.
Disclosure of Invention
To solve or at least partially solve the above technical problems, the present disclosure provides a method, an apparatus, and a computer-readable storage medium for memory optimization.
The first aspect of the present disclosure provides a method for optimizing a memory, the method comprising:
monitoring memory overhead information of each module in an application program;
determining the memory optimization priority of each module based on the memory overhead information;
and based on the memory optimization priority, performing memory release.
Further, before determining the memory optimization priority of each module based on the memory overhead information, the method further includes:
acquiring the maximum use memory of an application program;
judging whether to perform memory optimization or not based on the maximum used memory and the memory overhead information;
if the memory optimization is performed, the memory optimization priority is determined based on the memory overhead information.
Further, the maximum used memory is the memory capacity applied by the application program, or the residual memory capacity of the device;
Wherein an application is installed in the device; the remaining memory capacity of the device is calculated based on the total memory capacity of the device and the allocated memory capacity of the device.
Further, based on the maximum used memory and the memory overhead information of each module, determining whether to perform memory optimization includes:
determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module;
after the memory residual threshold value is determined, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after the memory residual threshold value is determined; calculating the residual memory capacity of the application program based on the total memory overhead;
and determining to perform memory optimization after the residual memory capacity of the application program is smaller than or equal to a memory residual threshold value.
Further, determining a memory remaining threshold for performing memory optimization based on the maximum used memory and memory overhead information of each module includes:
determining the residual memory capacity of the application program based on the maximum used memory and the memory overhead information of each module;
and determining a memory residual threshold value for performing memory optimization based on the residual memory capacity of the application program.
Further, the memory residual threshold is a preset multiple of the residual memory capacity of the application program; wherein the preset multiple is greater than 0 and less than 1.
Further, the memory optimization priority of each module includes:
the module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
Further, based on the memory optimization priority, performing the memory release includes:
and sequentially releasing the memory based on the sequence of the memory optimization priority from high to low until the residual memory capacity of the application program after the memory release is greater than the residual memory threshold value, and stopping the memory release.
Further, the method further comprises:
and recording module information corresponding to the memory release while the memory release is carried out.
Further, the method further comprises:
and uploading the module information corresponding to the memory release to the cloud.
A second aspect of the present disclosure provides an apparatus for memory optimization, the apparatus comprising:
the monitoring module is used for monitoring the memory overhead information of each module in the application program;
the determining module is used for determining the memory optimization priority of each module based on the memory overhead information;
And the release module is used for releasing the memory based on the memory optimization priority.
Further, the apparatus further comprises:
the acquisition module is used for acquiring the maximum use memory of the application program;
the judging module is used for judging whether to perform memory optimization or not based on the maximum used memory and the memory overhead information;
and the determining module is used for determining the memory optimization priority of each module based on the memory overhead information after the judging module determines that the memory is optimized.
Further, the judging module is specifically configured to:
determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module;
after the memory residual threshold value is determined, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after the memory residual threshold value is determined; calculating the residual memory capacity of the application program based on the total memory overhead;
and performing memory optimization after the residual memory capacity of the application program is smaller than or equal to a memory residual threshold value.
A third aspect of the present disclosure provides a computer apparatus comprising a processor for implementing the steps of the method according to any one of the above aspects when executing a computer program stored in a memory.
A fourth aspect of the present disclosure provides a readable storage medium having a computer program stored thereon, the readable storage medium comprising: the computer program when executed by a processor implements the steps of the method according to any of the above schemes.
One or more technical solutions provided by the embodiments of the present disclosure at least have the following effects or advantages:
by adopting the technical scheme provided by the disclosure, the memory overhead information of each module in the application program is monitored; determining the memory optimization priority of each module based on the memory overhead information of each module; and performing memory release based on the memory optimization priority of each module. Compared with the prior art, in the embodiment of the disclosure, the memory optimization priority of each module can be determined by monitoring the memory overhead information of each module in the running process of the application program, so that the memory release is performed based on the memory optimization priority, the problems of breakdown, jamming, scalding and the like of the APP can be reduced, and the user experience is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a flow chart of a method of memory optimization provided by the present disclosure;
FIG. 2 is a flow chart of another method of memory optimization provided by the present disclosure;
FIG. 3 is a block diagram of an apparatus for memory optimization provided by the present disclosure;
fig. 4 is a schematic diagram of a computer device provided in the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
The embodiment of the disclosure provides a memory optimization method, a memory optimization device and a computer readable storage medium, wherein the memory management plug-in is based on the memory management plug-in, and is a memory management plug-in which a user installs an APP (application) while installing the APP at a user terminal, the plug-in cannot appear in a mobile phone interface and be bound with the APP, the user is noninductive in the whole installation process, and the memory management plug-in and the APP are unloaded simultaneously when the user is unloaded, so that the running safety of the APP is ensured. The user terminal is any electronic device operable by a user, for example, may be a mobile handheld device such as a smart phone or a tablet computer, or may be a fixed electronic device such as a desktop computer or a smart television. It should be understood that the application scenario of the method of the present application may be a mobile phone, and may also be a vehicle. While the application scenario of the method of the present application is merely some examples or embodiments of the present application, it will be obvious to those of ordinary skill in the art that the present application may be applied to other similar scenarios without the exercise of inventive effort. In order to make the description clearer, the embodiments of the present disclosure take a handheld device as an example to describe a method, an apparatus, and a computer storage medium for optimizing a memory.
To facilitate understanding of the technical solutions of the present disclosure, first, the technical arts to which the present disclosure relates are explained:
cloud: the cloud is a software platform adopting an application program virtualization technology (Application Virtualization), and integrates multiple functions of software searching, downloading, using, managing, backing up and the like.
Thermal remediation (hotfix): the code needing to be updated is configured in the server in advance, the code is pushed to the client from the server to be updated, and the client can update the software without reinstallation.
FIG. 1 is a flow chart of a method of memory optimization provided.
With reference to fig. 1, at least one embodiment of the present disclosure provides a method for optimizing a memory, including the following four steps 101 to 103:
101: and monitoring the memory overhead information of each module in the application program. In this embodiment, the application program may include a plurality of functional modules, such as a vehicle control related module, a community module, and the like. In the application program using process, the memory size used by each module included in the application program is different, so that the memory overhead information of each module in the application program is monitored in this embodiment.
102: and determining the memory optimization priority of each module based on the memory overhead information of each module.
In this embodiment, the memory optimization priorities of the modules include: the module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
Specifically, in the running process of the APP, the unused time of each module is ordered, the memory overhead is ordered, each module is divided into a module with larger memory overhead and unused for a longer time, a module with longer time and unused for a longer time, a module with larger memory overhead, all modules except the currently used module and unused functions of the currently used module according to the used time of the module and the memory overhead, and the memory optimization priorities of the modules are sequentially decreased.
103: and performing memory release based on the memory optimization priority of each module.
In this embodiment, based on the memory optimization priority of each module, performing memory release includes: and sequentially releasing the memory based on the sequence of the memory optimization priority from high to low until the residual memory capacity of the application program after the memory release is greater than the residual memory threshold value, and stopping the memory release.
Specifically, for example, assume that the first module is a module with larger memory overhead and longer unused time, the second module is a module with longer unused time, the third module is a module with larger memory overhead, the fourth module is all modules except the currently used module, and the fifth module is a longer unused function of the currently used module, then the memory optimization priority is that the first module > the second module > the third module > the fourth module > the fifth module, and in the release process, if the remaining memory capacity of the APP is greater than 30 mx 20% =6m, memory release is stopped. For example, in the memory release process, the first module is released first, and whether the residual memory capacity of the APP is larger than 6M at the moment is judged, if so, the memory release is stopped; if the residual memory capacity of the APP is smaller than 6M, releasing the second module, judging whether the residual memory capacity of the APP is larger than 6M, if so, stopping memory release, and if so, releasing the third module; and so on.
According to the method and the device for optimizing the memory, the influence on the currently used module can be effectively reduced through the determination of the memory optimization priority, the memory of the APP is optimized under the condition that the currently used module is not influenced, the running flow field of the APP is improved, and the user experience is improved.
In some embodiments, prior to step 102, the method may further comprise 1011 and 1012:
1011: and obtaining the maximum use memory of the application program. The maximum used memory is the memory capacity applied by the application program, or the residual memory capacity of the equipment; wherein the application is installed in the device; the remaining memory capacity of the device is calculated based on the total memory capacity of the device and the allocated memory capacity of the device.
Specifically, in the embodiment of the disclosure, after a user opens an APP, a memory management plug-in may acquire basic information of a mobile phone, including model information, memory information, system version information, and the like, where the model and system version of the mobile phone are only used in a subsequent process of optimizing the APP, where the acquired memory information is a maximum used memory of the APP, and the maximum used memory is a memory capacity of the APP application, or is a remaining memory capacity of the APP, or is a remaining memory capacity of the device; wherein, APP is installed in the device; the remaining memory capacity of the device is a remaining memory capacity calculated based on the total memory capacity of the device and the memory capacity allocated by the device. For example, in practical application, according to practical needs or APP requests, memories with different sizes may be allocated to APP for use, for example, the mobile phone memory is 256M, and the current other APP use memories are 156M, then the remaining memories are 100M, and the APP use memory 50M is allocated, that is, the maximum use memory of APP acquired at this time. According to the acquired whole memory condition of the APP, the memory management plug-in monitors the memory overhead information of each module in the APP in real time.
1012: judging whether to perform memory optimization or not based on the maximum used memory and the memory overhead information; if the memory optimization is performed, the memory optimization priority is determined based on the memory overhead information.
In this embodiment, based on the maximum used memory and the memory overhead information, determining whether to perform memory optimization includes: determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module; after determining the memory residual threshold, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after determining the memory residual threshold; calculating the residual memory capacity of the application program based on the total memory overhead; and determining to perform memory optimization after the residual memory capacity of the application program is smaller than or equal to the memory residual threshold value.
Based on the maximum used memory and the memory overhead information of each module, determining a memory residual threshold for performing memory optimization includes: determining the residual memory capacity of the application program based on the maximum used memory and the memory overhead information of each module; and determining a memory residual threshold value for performing memory optimization based on the residual memory capacity of the application program.
The memory residual threshold value is a preset multiple of the residual memory capacity of the application program; wherein the preset multiple is more than 0 and less than 1.
Specifically, in combination with the use condition of the memory of the APP, a preset multiple of 20% of the memory of the APP for performing the memory optimization is set, for example, the maximum used memory allocated to the APP is 50M, assuming that the APP has 5 modules, the first module uses 5M of the memory, the second module uses 4M of the memory, the third module uses 6M of the memory, the fourth module uses 3M of the memory, the fifth module uses 2M of the memory, the total used memory of each module is 20M, then the remaining memory capacity of the APP is 30M, the remaining memory threshold of the APP is set to be 30 mx 20% =6m, at this time, the memory overhead is gradually increased, and when the total memory overhead of the APP is 24M, that is, when the remaining memory capacity is 6M, the total memory overhead is equal to the remaining memory threshold, the remaining memory is determined to perform the memory optimization. Fig. 2 is a flowchart of another method for memory optimization provided by an embodiment of the present disclosure.
Referring to fig. 2, in the present embodiment, the method for optimizing the memory includes steps 201, 2011, 2012, 202 and 203, wherein "determining the priority of memory optimization based on the memory overhead information" and step 203 are the same as steps 101, 102 and 103 in fig. 1, respectively, in step 201, 2012, and are not repeated.
In this embodiment, the difference from fig. 1 is that, after stopping the memory release in step 203, the method of this embodiment further includes:
during the running process of the application program, the method repeatedly performs:
201: monitoring memory overhead information of each module in an application program;
2011: acquiring the maximum use memory of the application program; the maximum used memory of the application program is the residual memory capacity of the application program after the memory is released;
2012: judging whether to perform memory optimization or not based on the maximum used memory and the memory overhead information; if the memory optimization is carried out, executing the memory optimization based on the memory overhead information, and determining the priority of the memory optimization;
202: determining the memory optimization priority of each module based on the memory overhead information of each module;
203: and performing memory release based on the memory optimization priority of each module.
Specifically, memories with different sizes are allocated to the APP for use, if the memory of the mobile phone is 256M and the memory of other APP for use is 156M currently, then the remaining memory is 100M, and the memory 50M for use is allocated to the APP, that is, the maximum memory for use of the APP acquired at this time. Assuming that the maximum memory used by the APP is 50M, each module of the APP totally uses 20M of memory capacity, and the remaining memory capacity is 30M; as APP continues to run, the remaining memory capacity gradually decreases, when the remaining memory capacity is 30m×20% =6m, memory optimization is performed, and if the memory capacity is released to be 14M, the total remaining memory capacity is 20M; APP continues to run, and when the remaining memory capacity is 20m×20% =4m, memory optimization is performed again; and so on. And when the memory optimization is performed each time, the priority ranking is performed on the memory use conditions of all the APP modules, and the release is performed according to the priority from high to low until the total residual memory capacity is more than the memory residual threshold value.
In this embodiment, the method for optimizing the memory may further include the following steps, which are not shown in fig. 2:
204: recording module information corresponding to the memory release while performing the memory release;
205: and uploading the module information corresponding to the memory release to the cloud.
Specifically, when the memory is released, the memory use condition of each module in the APP is recorded, the memory release optimization priority of each module, the basic information of the equipment comprises a model, a system version and the like, and the execution condition of a release mechanism comprises a memory release process, a memory release result and the like; text data is generated through the memory management plug-in and uploaded to the cloud, and the later-stage APP can locate the problem according to the text data report generated by the memory management plug-in, and is a manual locating problem at the moment, and the APP is only responsible for uploading the text data, so that a later-stage developer can conveniently utilize the text data to carry out APP optimization. Later-stage developer can store APP optimization scheme through the high in the clouds, and this optimization scheme can be dynamically updated to the memory management plug-in components for the more accurate tracking of memory management plug-in components, and make things convenient for developer to solve the problem.
Specifically, the memory management plug-in unit generates a corresponding text data report according to the operation data of the APP, and the text data report is uploaded to the cloud end every day at regular time; the cloud end can also manually call the text data report of the memory management plug-in, and the log is fished back to the cloud end by sending an instruction to the memory management plug-in.
According to the method and the device, the memory condition of the whole APP is monitored in real time through the memory management plug-in, the module with larger memory overhead of the APP can be forcibly released, so that the memory allocation of the whole APP is reasonable, the experience of the whole APP is not influenced by the memory of a certain module, the released memory is not perceived by a user, and only the used function but the module with the memory which is not released in time can be subjected to memory release optimization. If the user is using the functions of the related modules of the vehicle control, but the community module memory is not released because the user uses the community module before, then the previous community module memory is released in time to ensure the best experience of the current service function; and the memory is released, the module with larger memory overhead is recorded, APP data are synchronized to the cloud end every day at regular time, the problem points are counted, and the APP is convenient to optimize in the later period.
Fig. 3 provides a block diagram of an apparatus for memory optimization.
With reference to fig. 3, an embodiment of the present invention further provides a memory optimization apparatus 300, which specifically includes:
the monitoring module 301 is configured to monitor memory overhead information of each module in the application program. In this embodiment, the application program may include a plurality of functional modules, such as a vehicle control related module, a community module, and the like. In the application program using process, the memory size used by each module included in the application program is different, so that the memory overhead information of each module in the application program is monitored in this embodiment.
A determining module 302, configured to determine a memory optimization priority of each module based on the memory overhead information.
In this embodiment, the memory optimization priorities of the modules include: the module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
Specifically, in the running process of the APP, the unused time of each module is ordered, the memory overhead is ordered, each module is divided into a module with larger memory overhead and unused for a longer time, a module with longer time and unused for a longer time, a module with larger memory overhead, all modules except the currently used module and unused functions of the currently used module according to the used time of the module and the memory overhead, and the memory optimization priorities of the modules are sequentially decreased.
And the releasing module 303 is configured to perform memory release based on the memory optimization priority.
In this embodiment, based on the memory optimization priority of each module, performing memory release includes: and sequentially releasing the memory based on the sequence of the memory optimization priority from high to low until the residual memory capacity of the application program after the memory release is greater than the residual memory threshold value, and stopping the memory release.
Specifically, for example, assume that the first module is a module with larger memory overhead and longer unused time, the second module is a module with longer unused time, the third module is a module with larger memory overhead, the fourth module is all modules except the currently used module, and the fifth module is a longer unused function of the currently used module, then the memory optimization priority is that the first module > the second module > the third module > the fourth module > the fifth module, and in the release process, if the remaining memory capacity of the APP is greater than 30 mx 20% =6m, memory release is stopped. For example, in the memory release process, the first module is released first, and whether the residual memory capacity of the APP is larger than 6M at the moment is judged, if so, the memory release is stopped; if the residual memory capacity of the APP is smaller than 6M, releasing the second module, judging whether the residual memory capacity of the APP is larger than 6M, if so, stopping memory release, and if so, releasing the third module; and so on.
According to the method and the device for optimizing the memory, the influence on the currently used module can be effectively reduced through the determination of the memory optimization priority, the memory of the APP is optimized under the condition that the currently used module is not influenced, the running flow field of the APP is improved, and the user experience is improved.
In some embodiments, the apparatus may further comprise: the device comprises an acquisition module and a judgment module.
And the acquisition module is used for acquiring the maximum use memory of the application program.
The maximum used memory is the memory capacity applied by the application program, or the residual memory capacity of the equipment; wherein the application is installed in the device; the remaining memory capacity of the device is calculated based on the total memory capacity of the device and the allocated memory capacity of the device.
Specifically, in the embodiment of the disclosure, after a user opens an APP, a memory management plug-in may acquire basic information of a mobile phone, including model information, memory information, system version information, and the like, where the model and system version of the mobile phone are only used in a subsequent process of optimizing the APP, where the acquired memory information is a maximum used memory of the APP, and the maximum used memory is a memory capacity of the APP application, or is a remaining memory capacity of the APP, or is a remaining memory capacity of the device; wherein, APP is installed in the device; the remaining memory capacity of the device is a remaining memory capacity calculated based on the total memory capacity of the device and the memory capacity allocated by the device. For example, in practical application, according to practical needs or APP requests, memories with different sizes may be allocated to APP for use, for example, the mobile phone memory is 256M, and the current other APP use memories are 156M, then the remaining memories are 100M, and the APP use memory 50M is allocated, that is, the maximum use memory of APP acquired at this time. According to the acquired whole memory condition of the APP, the memory management plug-in monitors the memory overhead information of each module in the APP in real time.
And the judging module is used for judging whether the memory optimization is performed or not based on the maximum used memory and the memory overhead information.
In this embodiment, the judging module is specifically configured to: determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module; after determining the memory residual threshold, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after determining the memory residual threshold; calculating the residual memory capacity of the application program based on the total memory overhead; and determining to perform memory optimization after the residual memory capacity of the application program is smaller than or equal to the memory residual threshold value.
Based on the maximum used memory and the memory overhead information of each module, determining a memory residual threshold for performing memory optimization includes: determining the residual memory capacity of the application program based on the maximum used memory and the memory overhead information of each module; and determining a memory residual threshold value for performing memory optimization based on the residual memory capacity of the application program.
The memory residual threshold value is a preset multiple of the residual memory capacity of the application program; wherein the preset multiple is more than 0 and less than 1.
Specifically, in combination with the use condition of the memory of the APP, a preset multiple of 20% of the memory of the APP for performing the memory optimization is set, for example, the maximum used memory allocated to the APP is 50M, assuming that the APP has 5 modules, the first module uses 5M of the memory, the second module uses 4M of the memory, the third module uses 6M of the memory, the fourth module uses 3M of the memory, the fifth module uses 2M of the memory, the total used memory of each module is 20M, then the remaining memory capacity of the APP is 30M, the remaining memory threshold of the APP is set to be 30 mx 20% =6m, at this time, the memory overhead is gradually increased, and when the total memory overhead of the APP is 24M, that is, when the remaining memory capacity is 6M, the total memory overhead is equal to the remaining memory threshold, the remaining memory is determined to perform the memory optimization.
The determining module 302 is configured to determine, after the determining module determines that the memory optimization is performed, a memory optimization priority of each module based on the memory overhead information.
In some embodiments, after the release module 303 stops the memory release, the monitoring module 301 continues to monitor the memory overhead information of each module in the application during the running process of the application. The obtaining module obtains the maximum used memory of the application program, where the maximum used memory is the remaining memory capacity of the application program after the releasing module 303 stops releasing the memory. The judging module judges whether to perform memory optimization or not based on the maximum used memory and the memory overhead information. The determining module determines the memory optimization priority of each module based on the memory overhead information after the judging module determines to perform the memory optimization. The releasing module 303 performs memory release based on the memory optimization priority of each module. It should be noted that, after the application program stops running, the monitoring module 301 stops monitoring the memory overhead information of each module in the application program.
Specifically, memories with different sizes are allocated to the APP for use, if the memory of the mobile phone is 256M and the memory of other APP for use is 156M currently, then the remaining memory is 100M, and the memory 50M for use is allocated to the APP, that is, the maximum memory for use of the APP acquired at this time. Assuming that the maximum memory used by the APP is 50M, each module of the APP totally uses 20M of memory capacity, and the remaining memory capacity is 30M; as APP continues to run, the remaining memory capacity gradually decreases, when the remaining memory capacity is 30m×20% =6m, memory optimization is performed, and if the memory capacity is released to be 14M, the total remaining memory capacity is 20M; APP continues to run, and when the remaining memory capacity is 20m×20% =4m, memory optimization is performed again; and so on. And when the memory optimization is performed each time, the priority ranking is performed on the memory use conditions of all the APP modules, and the release is performed according to the priority from high to low until the total residual memory capacity is more than the memory residual threshold value.
In this embodiment, the apparatus may further include the following modules:
the recording module is configured to record the module information corresponding to the memory release while the memory release module 303 performs the memory release.
And the uploading module is used for uploading the module information corresponding to the memory release of the releasing module 303 to the cloud.
Specifically, when the memory is released, the memory use condition of each module in the APP is recorded, the memory release optimization priority of each module, the basic information of the equipment comprises a model, a system version and the like, and the execution condition of a release mechanism comprises a memory release process, a memory release result and the like; text data is generated through the memory management plug-in and uploaded to the cloud, and the later-stage APP can locate the problem according to the text data report generated by the memory management plug-in, and is a manual locating problem at the moment, and the APP is only responsible for uploading the text data, so that a later-stage developer can conveniently utilize the text data to carry out APP optimization. Later-stage developer can store APP optimization scheme through the high in the clouds, and this optimization scheme can be dynamically updated to the memory management plug-in components for the more accurate tracking of memory management plug-in components, and make things convenient for developer to solve the problem.
Specifically, the memory management plug-in unit generates a corresponding text data report according to the operation data of the APP, and the text data report is uploaded to the cloud end every day at regular time; the cloud end can also manually call the text data report of the memory management plug-in, and the log is fished back to the cloud end by sending an instruction to the memory management plug-in.
According to the method and the device, the memory condition of the whole APP is monitored in real time through the memory management plug-in, the module with larger memory overhead of the APP can be forcibly released, so that the memory allocation of the whole APP is reasonable, the experience of the whole APP is not influenced by the memory of a certain module, the released memory is not perceived by a user, and only the used function but the module with the memory which is not released in time can be subjected to memory release optimization. If the user is using the functions of the related modules of the vehicle control, but the community module memory is not released because the user uses the community module before, then the previous community module memory is released in time to ensure the best experience of the current service function; and the memory is released, the module with larger memory overhead is recorded, APP data are synchronized to the cloud end every day at regular time, the problem points are counted, and the APP is convenient to optimize in the later period.
Fig. 4 is a schematic diagram of a computer device according to a third embodiment of the present invention. The computer apparatus of this embodiment includes: a processor 401, a memory 402, and a computer program stored in the memory and executable on the processor, such as a program corresponding to the method of memory optimization in the embodiment. The steps in the method for implementing memory optimization in the above embodiment are implemented when the processor executes the computer program. Alternatively, the processor may implement the functions of each module/unit in the memory optimization device of the above embodiment when executing the computer program, for example, the functions of the monitoring module 301, the determining module 302, and the releasing module 303 shown in fig. 3.
For example, a computer program may be split into one or more modules/units, which are stored in a memory and executed by a processor to perform the present invention. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program in a computer device. For example, the computer program may be divided into functions of the monitoring module 301, the determining module 302 and the releasing module 303, the specific functions of each module being as follows:
the monitoring module 301 is configured to monitor memory overhead information of each module in the application program. In this embodiment, the application program may include a plurality of functional modules, such as a vehicle control related module, a community module, and the like. In the application program using process, the memory size used by each module included in the application program is different, so that the memory overhead information of each module in the application program is monitored in this embodiment.
A determining module 302, configured to determine a memory optimization priority of each module based on the memory overhead information. In this embodiment, the memory optimization priorities of the modules include: the module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
And the releasing module 303 is configured to perform memory release based on the memory optimization priority. In this embodiment, based on the memory optimization priority of each module, performing memory release includes: and sequentially releasing the memory based on the sequence of the memory optimization priority from high to low until the residual memory capacity of the application program after the memory release is greater than the residual memory threshold value, and stopping the memory release.
In some embodiments, the apparatus may further comprise: the device comprises an acquisition module and a judgment module.
And the acquisition module is used for acquiring the maximum use memory of the application program.
The maximum used memory is the memory capacity applied by the application program, or the residual memory capacity of the equipment; wherein the application is installed in the device; the remaining memory capacity of the device is calculated based on the total memory capacity of the device and the allocated memory capacity of the device.
And the judging module is used for judging whether the memory optimization is performed or not based on the maximum used memory and the memory overhead information.
In this embodiment, the judging module is specifically configured to: determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module; after determining the memory residual threshold, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after determining the memory residual threshold; calculating the residual memory capacity of the application program based on the total memory overhead; and determining to perform memory optimization after the residual memory capacity of the application program is smaller than or equal to the memory residual threshold value.
Based on the maximum used memory and the memory overhead information of each module, determining a memory residual threshold for performing memory optimization includes: determining the residual memory capacity of the application program based on the maximum used memory and the memory overhead information of each module; and determining a memory residual threshold value for performing memory optimization based on the residual memory capacity of the application program.
The memory residual threshold value is a preset multiple of the residual memory capacity of the application program; wherein the preset multiple is more than 0 and less than 1.
The determining module 302 is configured to determine, after the determining module determines that the memory optimization is performed, a memory optimization priority of each module based on the memory overhead information.
In some embodiments, after the release module 303 stops the memory release, the monitoring module 301 continues to monitor the memory overhead information of each module in the application during the running process of the application. The obtaining module obtains the maximum used memory of the application program, where the maximum used memory is the remaining memory capacity of the application program after the releasing module 303 stops releasing the memory. The judging module judges whether to perform memory optimization or not based on the maximum used memory and the memory overhead information. The determining module determines the memory optimization priority of each module based on the memory overhead information after the judging module determines to perform the memory optimization. The releasing module 303 performs memory release based on the memory optimization priority of each module. It should be noted that, after the application program stops running, the monitoring module 301 stops monitoring the memory overhead information of each module in the application program.
In this embodiment, the apparatus may further include the following modules:
the recording module is configured to record the module information corresponding to the memory release while the memory release module 303 performs the memory release.
And the uploading module is used for uploading the module information corresponding to the memory release of the releasing module 303 to the cloud.
The computer device may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. Computer devices may include, but are not limited to, processors, memory. It will be appreciated by those skilled in the art that the schematic diagram 4 is merely an example of a computer apparatus and is not limiting of the computer apparatus, and may include more or fewer components than shown, or may combine certain components, or different components, e.g., the computer apparatus may also include input and output devices, network access devices, buses, etc.
The processor 401 may be a central processing unit (Central Proceing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital ignalProceor, DP), application specific integrated circuits (Application pecific Integrated Circuit, AIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, and the various interfaces and lines connecting the various parts of the overall computer device.
The memory 402 may be used to store computer programs and/or modules that, by running or executing the computer programs and/or modules stored in the memory, implement the various functions of the computer device by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, a plug-in hard disk, a smart Card (MC), a secure digital (D) Card, a flash Card (flash Card), at least one disk storage device, a flash memory device, or other volatile solid state storage device.
The computer program may be divided into functions of the monitoring module 301, the determining module 302 and the releasing module 303, and specific functions of the modules are as follows:
the monitoring module 301 is configured to monitor memory overhead information of each module in the application program. In this embodiment, the application program may include a plurality of functional modules, such as a vehicle control related module, a community module, and the like. In the application program using process, the memory size used by each module included in the application program is different, so that the memory overhead information of each module in the application program is monitored in this embodiment.
A determining module 302, configured to determine a memory optimization priority of each module based on the memory overhead information. In this embodiment, the memory optimization priorities of the modules include: the module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
And the releasing module 303 is configured to perform memory release based on the memory optimization priority. In this embodiment, based on the memory optimization priority of each module, performing memory release includes: and sequentially releasing the memory based on the sequence of the memory optimization priority from high to low until the residual memory capacity of the application program after the memory release is greater than the residual memory threshold value, and stopping the memory release.
In some embodiments, the apparatus may further comprise: the device comprises an acquisition module and a judgment module.
And the acquisition module is used for acquiring the maximum use memory of the application program.
The maximum used memory is the memory capacity applied by the application program, or the residual memory capacity of the equipment; wherein the application is installed in the device; the remaining memory capacity of the device is calculated based on the total memory capacity of the device and the allocated memory capacity of the device.
And the judging module is used for judging whether the memory optimization is performed or not based on the maximum used memory and the memory overhead information.
In this embodiment, the judging module is specifically configured to: determining a memory residual threshold value for performing memory optimization based on the maximum used memory and memory overhead information of each module; after determining the memory residual threshold, monitoring the memory overhead information of each module in real time to obtain the total memory overhead of the application program after determining the memory residual threshold; calculating the residual memory capacity of the application program based on the total memory overhead; and determining to perform memory optimization after the residual memory capacity of the application program is smaller than or equal to the memory residual threshold value.
Based on the maximum used memory and the memory overhead information of each module, determining a memory residual threshold for performing memory optimization includes: determining the residual memory capacity of the application program based on the maximum used memory and the memory overhead information of each module; and determining a memory residual threshold value for performing memory optimization based on the residual memory capacity of the application program.
The memory residual threshold value is a preset multiple of the residual memory capacity of the application program; wherein the preset multiple is more than 0 and less than 1.
The determining module 302 is configured to determine, after the determining module determines that the memory optimization is performed, a memory optimization priority of each module based on the memory overhead information.
In some embodiments, after the release module 303 stops the memory release, the monitoring module 301 continues to monitor the memory overhead information of each module in the application during the running process of the application. The obtaining module obtains the maximum used memory of the application program, where the maximum used memory is the remaining memory capacity of the application program after the releasing module 303 stops releasing the memory. The judging module judges whether to perform memory optimization or not based on the maximum used memory and the memory overhead information. The determining module determines the memory optimization priority of each module based on the memory overhead information after the judging module determines to perform the memory optimization. The releasing module 303 performs memory release based on the memory optimization priority of each module. It should be noted that, after the application program stops running, the monitoring module 301 stops monitoring the memory overhead information of each module in the application program.
In this embodiment, the apparatus may further include the following modules:
the recording module is configured to record the module information corresponding to the memory release while the memory release module 303 performs the memory release.
And the uploading module is used for uploading the module information corresponding to the memory release of the releasing module 303 to the cloud.
The present invention may be implemented by implementing all or part of the procedures in the methods of the embodiments described above, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor may implement the steps of the embodiments of the methods described above. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Acce Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. A method of memory optimization, the method comprising:
monitoring and obtaining initial memory overhead information of each module in the application program;
acquiring the maximum use memory of the application program, wherein the maximum use memory is the memory capacity applied by the application program;
determining the initial residual memory capacity of the application program based on the maximum used memory and the initial memory overhead information of each module;
calculating the initial residual memory capacity with a preset multiple as a memory residual threshold, wherein the preset multiple is more than 0 and less than 1;
monitoring real-time memory overhead information of each module to obtain real-time memory overhead of the application program after determining the memory residual threshold;
calculating the real-time residual memory capacity of the application program based on the maximum used memory and the total overhead of the real-time memory;
after the real-time residual memory capacity is smaller than or equal to the memory residual threshold value, determining to perform memory optimization;
determining the memory optimization priority of each module based on the real-time memory overhead information;
and based on the memory optimization priority, performing memory release.
2. The method of claim 1, wherein the memory optimization priorities of the modules comprise:
The module with larger memory cost and longer time unused is > the module with larger memory cost is > all modules except the currently used module are > the longer time unused function of the currently used module.
3. The method of claim 1, wherein the performing a memory release based on the memory optimization priority comprises:
and sequentially performing memory release based on the sequence of the memory optimization priority from high to low until the real-time residual memory capacity of the application program after the memory release is larger than the memory residual threshold value, and stopping the memory release.
4. The method according to claim 1, wherein the method further comprises:
and recording module information corresponding to the memory release while the memory release is performed.
5. The method according to claim 4, wherein the method further comprises:
and uploading the module information corresponding to the memory release to the cloud.
6. An apparatus for memory optimization, the apparatus comprising:
the monitoring module is used for monitoring and obtaining initial memory overhead information of each module in the application program; monitoring real-time memory overhead information of each module to obtain real-time memory total overhead of the application program after determining a memory residual threshold;
The acquisition module is used for acquiring the maximum use memory of the application program, wherein the maximum use memory is the memory capacity applied by the application program;
the judging module is used for determining the initial residual memory capacity of the application program based on the maximum used memory and the initial memory overhead information of each module; calculating the initial residual memory capacity with a preset multiple as a memory residual threshold, wherein the preset multiple is more than 0 and less than 1;
calculating the real-time residual memory capacity of the application program based on the maximum used memory and the total overhead of the real-time memory; after the real-time residual memory capacity is smaller than or equal to the memory residual threshold value, determining to perform memory optimization;
the determining module is used for determining the memory optimization priority of each module based on the real-time memory overhead information;
and the release module is used for releasing the memory based on the memory optimization priority.
7. A computer apparatus, comprising: the computer arrangement comprising a processor for implementing the steps of the method according to any of claims 1-5 when executing a computer program stored in a memory.
8. A readable storage medium having a computer program stored thereon, comprising: the computer program implementing the steps of the method according to any of claims 1-5 when executed by a processor.
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