32 results about "Average memory" patented technology

A system and method for limiting power consumption of a computer memory system. The system and method includes selecting a memory access rate. The selected memory access rate corresponds to a desired average memory power consumption rate. A first time interval is started as a current time interval. A memory system is accessed. If the memory access rate has not been exceeded, then the access is applied to the memory system. Alternatively, if the memory access rate has been exceeded, then the access is delayed until the current time interval has expired and a subsequent time interval is started as the current time interval and the access is applied to the memory system

A computer application program executing on a computer, such as a portable computer, is selected for termination by first identifying computer application programs executing on the computer. A priority value is assigned to each of the identified computer applications. The priority value is based on multiple characteristics of the identified computer application programs. The computer application program with the smallest priority value is automatically terminated. If the computer application program with the smallest priority value is in a modal state in which it waits for a response from a user, then a default response is provided to the application prior to terminating the computer application program. The characteristics associated with the computer application programs may include average launch times, average memory usages, a class or type of application, frequencies of usage, and an amount of data stored on the computer by the computer application program.

The invention relates to a cloud computing virtualization environment-oriented resource monitoring and adjustment system which comprises a resource information monitoring module and a resource adjustment module, wherein the resource information monitoring module is used for obtaining static configuration information of a virtual machine from Xenstore, obtaining dynamic resource information through XenHypervisor and obtaining detail resource usage information from each DomU; the resource adjustment module comprises a memory resource adjustment module and a VCPU (virtual central processing unit) resource adjustment module; the memory resource adjustment module is used for providing a memory adjustment interface and adjusting memory resources through an on-demand adjustment strategy or a hosting adjustment strategy based on the average memory utilization rate; the VCPU resource adjustment module is used for providing a VCPU adjustment interface and adjusting VCPU resources by calling a hot plug module of the XenHypervisor. The system can be used for not only monitoring the resource utilization state of each virtual machine in real time but also adjusting the resources of various virtual machines.

A method, system and computer program product for optimizing memory usage of an application running on a virtual machine. A virtual machine memory block is pre-allocated and the average memory usage of the virtual machine is periodically computed using statistics collected from the virtual machine through an API. If the memory usage average becomes higher than a maximum threshold, then a recovery mode is entered by releasing the virtual machine memory block and forcing the running application to reduce its processing activity; optionally, a garbage collector cycle can be forced. If the computed memory usage average becomes lower than a minimum threshold value, which is lower than the maximum threshold value, then a normal mode is entered by re-allocating the virtual machine memory block and forcing the running application to resumes its normal processing activity. Optionally, when the virtual machine is idle, a deep garbage collection is forced.

The invention relates to a memory optimization method and device, a readable storage medium and a mobile terminal. The method includes the steps of: acquiring program running information correspondingto each application, which is in a use state within each unit timing period, after a system powering-on signal is received; calculating a weight value, which corresponds to each application, according to acquired running frequency, average running duration and an average memory occupancy value; and when it is judged that remaining memory of a system is lower than preset memory, clearing corresponding applications according to the plurality of weight values and a preset sorting rule to ensure that the remaining memory of the system is not lower than the preset memory. The memory optimization method provided by the invention can realize automatic cleaning on running memory of the mobile terminal equipment, and improves user use experience.

The invention discloses a method for allocating memory to a virtual machine and the virtual machine, and the virtual machine comprises N non-uniform memory architecture NUMA nodes. The method comprises the following steps: firstly, obtaining the size of an average memory of each NUMA node in N NUMA nodes to be allocated to a virtual machine; then, obtaining the number of first remaining memories to be allocated to n1 NUMA nodes in the N NUMA nodes; and finally, when the size of the second remaining memory is 0, allocating memories with corresponding sizes to the N NMUA nodes based on the sizeof the average memory and the number of the first remaining memories. The technical effects that the memory is flexibly allocated to the virtual machine, the memory is flexibly allocated to the NUMA node of the virtual machine, and the diversified memory calling requirements of the process are met are achieved.

Techniques are disclosed relating to filtering cache accesses. In some embodiments, a control unit is configured to, in response to a request to process a set of data, determine a size of a portion of the set of data to be handled using a cache. In some embodiments, the control unit is configured to determine filtering parameters indicative of a set of addresses corresponding to the determined size. In some embodiments, the control unit is configured to process one or more access requests for the set of data based on the determined filter parameters, including: using the cache to process one or more access requests having addresses in the set of addresses and bypassing the cache to access a backing memory directly, for access requests having addresses that are not in the set of addresses. The disclosed techniques may reduce average memory bandwidth or peak memory bandwidth.

The invention relates to a cloud computing virtualization environment-oriented resource monitoring and adjustment system which comprises a resource information monitoring module and a resource adjustment module, wherein the resource information monitoring module is used for obtaining static configuration information of a virtual machine from Xenstore, obtaining dynamic resource information through XenHypervisor and obtaining detail resource usage information from each DomU; the resource adjustment module comprises a memory resource adjustment module and a VCPU (virtual central processing unit) resource adjustment module; the memory resource adjustment module is used for providing a memory adjustment interface and adjusting memory resources through an on-demand adjustment strategy or a hosting adjustment strategy based on the average memory utilization rate; the VCPU resource adjustment module is used for providing a VCPU adjustment interface and adjusting VCPU resources by calling a hot plug module of the XenHypervisor. The system can be used for not only monitoring the resource utilization state of each virtual machine in real time but also adjusting the resources of various virtual machines.

Embodiments of the present invention provide a cloud platform, a virtual machine scheduling method and device based on the cloud platform, and belong to the technical field of the Internet. Among them, the virtual machine scheduling method based on the cloud platform includes: step 1, determining that a host machine node needs to be allocated for the virtual machine to be scheduled; step 2, obtaining the average CPU of multiple host machine nodes on the cloud platform in the latest preset time period Utilization rate and average memory usage rate; Step 3, at least according to the average CPU usage rate and average memory usage rate of each host machine node in the most recent first preset time period, count the current available resource metrics of each host machine node weight value; step 4, assigning the host node with the largest weight value measured by the currently available resources to the virtual machine to be scheduled.

The present invention relates to a sintered body sputtering target. The structure observed on the polished surface of the target is composed of a metal phase containing Co or Fe and metal particles dispersed with non-magnetic material particles with an average particle diameter of 1.8 μm or less. When the maximum value of the distance between any two points located on the outer circumference of the non-magnetic material particle is set as the maximum diameter, and the minimum value of the distance between the two straight lines when the particle is clamped by two parallel straight lines is set as the minimum diameter, the The difference between the maximum diameter and the minimum diameter is 0.7 μm or less of the non-magnetic material particles relative to the non-magnetic material particles in the structure observed on the polished surface of the target is 60% or more, and, in the When the maximum value of the distance between two arbitrary points is defined as the maximum diameter, and the minimum value of the distance between the two straight lines when metal particles are sandwiched between two parallel lines is defined as the minimum diameter, the sum of the maximum diameter and the minimum diameter is 30 μm or more There is an average of one or more metal particles within a field of view of 1 mm2, and this sintered sputtering target can suppress abnormal discharge caused by a non-magnetic material that causes particle generation during sputtering.

The embodiment of the present invention discloses a message downgrading method and a related device, which are used for downgrading according to the actual situation of the user equipment to improve the downgrading efficiency. The method in the embodiment of the present invention includes: regularly obtaining the current memory usage information of the client and the current CPU usage information of the client within the preset duration; respectively obtaining the preset duration according to the current memory usage information and the current CPU usage information The average memory usage information and the average CPU usage information in the server; the average memory usage information and the average CPU usage information are sent to the server; the first threshold value sent by the server is received, and the first threshold value is obtained by the server based on the average memory usage information and the average CPU usage information ; When receiving a message, store the time of receiving the message in the time queue, and the time queue is used to store the time of receiving the message; judge whether the number of times stored in the time queue exceeds the first threshold; if exceeded, then according to the message Type downgrades messages.

The present invention relates to a memory optimization method and apparatus, and a readable storage medium and a mobile terminal. The method comprises the following steps: after a series of system power-on signals are received, acquiring program running information corresponding to each application program in a used state within each unit timing period; calculating and obtaining a weight value corresponding to each application program according to an acquired number of running times, average running duration and average memory occupancy value; and when it is determined that the remaining memory of a system is lower than a preset memory, removing corresponding application programs according to the plurality of weight values and a preset sorting rule, so as to ensure that the remaining memory of the system is not lower than the preset memory. By means of the memory optimization method provided by the present invention, automatic clearing of a running memory of a mobile terminal device can be realized, and the use experience of the user is improved.

The invention discloses a method for realizing instruction buffer lock, which comprises the following steps: 1) analyzing program summary, pre-running a section of program, and recording a memory block access sequence, citation times, re-access time interval and hit times; and 2) selecting memory blocks to be locked in a buffer, setting an access counter and a least recently used (LRU) counter for each memory block, accessing memory block access times recorded in the counters, assigning weights N1 and N2 to the access counter and LRU counter in each memory block, counting according to N1*access times+N2*(LRU counter limit-LRU value), and if the counted values are greater than a threshold M, locking the memory blocks in the buffer, wherein N1+N2=1. In the invention, the memory blocks locked in the instruction buffer can be replaced only when the lock is removed, the buffer hit rate is improved greatly, the accesses to low-level storage are reduced, and average memory access delay is reduced.

The invention discloses a multistage load estimating method facing task scheduling of a cloud computing platform. The method provided by the invention fully considers dynamic variation of own loads of task nodes, difference in performance of different task nodes and difference in different task load needs, selects operation queue average progress number, average CPU (central processing unit) utilization ration, average memory utilization ratio and average network bandwidth utilization ratio as load parameters for estimating, and gives different priorities to load parameters, so that a multistage load estimating method is provided for task scheduling of a large-scale server cluster. An adaptive task scheduling method of the cloud computing platform further is disclosed by the invention. Compared with the prior art, the method provided by the invention has the advantages of high precision and low consumption, and fully meets demands on task scheduling policies of the cloud computing platform.

The invention relates to an energy-saving dynamic adjustment method of a virtual web application cluster in a cloud computing environment. The energy-saving dynamic adjustment method includes: a building the virtual web application cluster; b monitoring the use condition of a virtual web application server; c judging the conditions of average central processing unit (CPU) use ratio and average memory use ratio in a section of [min%, max%]; d continuing to monitor the virtual web application server if both the CPU use ratio and average memory use ratio are in the section of [min%, max%]; e using a dynamical expanding method to build a new server if one of the CPU use ratio and average memory use ratio is larger than max% and the other of the CPU use ratio and average memory use ratio is larger than min%; f using a dynamic reduction method to remove a server on a host machine with the lowest resource use equivalent if one of the CPU use ratio and average memory use ratio is smaller than min% and the other of the CPU use ratio and average memory use ratio is smaller than max%; and g using a dynamic specification variation expanding method to build a new server if one of the CPU use ratio and average memory use ratio is smaller than min% and the other one of the CPU use ratio and average memory use ratio is larger than max%. The energy-saving dynamic adjustment method adjusts the web application cluster through the dynamical expanding method, the dynamic reduction method or the dynamic specification variation expanding method, ensures concentrated and lowest quantity operation of the host machine in a cloud platform, and achieves effective energy saving effect.

The present application discloses a memory optimization method based on cloud computing. The cloud server determines multiple Java virtual machines JVMs running multiple first application programs, and obtains the memory size and processing resource occupancy rate of multiple JVMs; the cloud server respectively The memory size and processing resource occupancy of multiple JVMs are weighted and averaged to obtain the average memory and processing resource occupancy, and based on the average processing resource occupancy, the first upper limit of processing resource occupancy is set, based on the average memory and processing resources The upper limit of the occupancy rate is to establish a first template JVM; obtain a plurality of first running times of a plurality of first applications in a plurality of JVMs; obtain a plurality of second running times of a plurality of first applications in a plurality of JVMs; According to the ratio of the first running time to the second running time, optimize the upper limit of memory and processing resource occupancy of the first template JVM, generate multiple optimized template JVMs, and the multiple optimized template JVMs correspond to multiple first application programs .