159 results about "Cache capacity" patented technology

The invention discloses a matrix calculation adaptive optimization method and system based on an ARM architecture. The method comprises the following steps: preprocessing to-be-processed matrix data; performing local activeness calculation and hot spot region identification on the preprocessed matrix data, and determining long-tail distribution characteristics of the matrix; calculating the optimal block size range of the matrix based on the long tail distribution characteristics of the matrix and the multi-level cache capacity parameters in the processor information, and generating an asymmetric block scheme; based on an asymmetric partitioning scheme, establishing a mapping relation between matrix features and optimal partitioning parameters; calculating the calculation density and the memory access mode of each block based on the asymmetric block scheme and the mapping relation, and generating a task scheduling scheme; based on the task scheduling scheme, matrix calculation is executed on the processor, and a final calculation result is output. According to the method, self-adaptive blocking and heterogeneous core scheduling are realized by identifying the long tail distribution characteristics of the matrix, and the performance and energy efficiency of matrix calculation on ARM are improved.

The invention relates to the technical field of mining data processing, and particularly discloses a coal yard production operation real-time monitoring management system. Aiming at the problems of difficulty in dynamic matching of time series data caused by insufficient cache capacity of edge nodes and fault diagnosis delay caused by data flow breakpoints or redundancy, the system adopts a multi-stage cache architecture, and physical mapping and quick positioning of data are realized through dynamic resource allocation of a real-time processing layer and a batch buffer layer in combination with three-dimensional grid spatio-temporal indexing. A breakpoint compensation mechanism is utilized to trigger target area resampling and historical data prefetching, and data stream continuity is guaranteed; and dynamically screening the data based on the confidence coefficient weight, and inhibiting redundancy accumulation. The collaborative optimization engine establishes a parameter linkage rule of the collection frequency, the cache period and the fusion threshold value, and the resource priority is inclined during high-risk early warning. And through closed-loop feedback and edge-cloud collaborative learning, a data processing strategy is continuously optimized. The cache resource utilization rate and the diagnosis timeliness are improved, and the method is suitable for real-time safety monitoring of complex industrial scenes.

The invention relates to the technical field of cloud computing and distributed storage, and discloses a distributed metadata management method and storage system based on cloud computing, and the method comprises the following steps: S1, collecting the access frequency of a plurality of metadata objects in a set time period; s2, performing weighting processing on the access frequency, and constructing access heat distribution under multiple time scales; s3, obtaining the available cache capacity of a plurality of cache nodes, and constructing cache resource weight distribution; s4, assessing the access cost and migration cost between each metadata object and each cache node; and S5, generating a scheduling strategy between the metadata object and the cache node based on a preset optimization target, wherein the optimization target is access cost minimization. According to the method, a heat, resource and cost joint modeling mechanism is constructed, and optimal scheduling and feedback adaptive control are combined, so that accurate decision, dynamic adjustment and migration cost controllability of copy deployment are realized, and the performance and stability of distributed metadata management are effectively improved.

The invention relates to the technical field of artificial intelligence compilers, in particular to an AI compiler and compiling method based on a multi-level intermediate representation framework, and the compiler comprises a high-level semantic retention layer which converts models of different AI frameworks into Lalg-on-Tensor IR intermediate representations; the hardware perception optimization layer comprises a tensor packaging and propagation module which is used for performing block packaging, layout propagation and folding of redundant packaging / unpackaging operation on the input tensor; the dynamic partitioning module is used for automatically selecting the partitioning size based on the cache capacity and the core number of the target hardware; the microkernel fusion module is used for fusing matrix multiplication, bias addition and an activation function into a single composite operator; and the microkernel collaboration layer is in butt joint with the hardware acceleration library through the XSMM dialect to generate a target hardware code. The hardware perception optimization layer can perform optimization according to different hardware characteristics, so that codes generated by the compiler can better adapt to target hardware, and the hardware utilization rate is improved.

The invention discloses a network-on-chip routing method and device, routing nodes, equipment, a medium and a product, and relates to the technical field of network-on-chip, and the method comprises the steps: firstly, screening the routing nodes through employing the input direction of a message, and limiting the selection range of the next-hop routing node; the relative position of the target routing node relative to the current routing node is determined, the routing nodes are further screened according to the relative position, and candidate routing nodes are determined; and determining a next-hop routing node according to the cache capacity and the input direction of the candidate routing node, and transmitting the message to be transmitted to the next-hop routing node to complete routing. The problem that the routing of the network-on-chip is easy to cause network message blockage or link failure of the network-on-chip can be solved. According to the method, an input direction is adopted to limit a selection range of a next-hop routing node, a relative position is adopted to determine candidate routing nodes, the next-hop routing node is determined in the candidate routing nodes based on cache capacity, and self-adaptive deadlock-free routing is realized.

The invention provides a cache cleaning method and device based on large model reasoning. The method comprises the following steps: monitoring the cache capacity of a hash table cache pool; when the current cache capacity of the hash table cache pool is greater than or equal to a preset capacity threshold value, obtaining access information corresponding to one or more cache entries, and screening a first to-be-cleaned cache entry through a preset cache cleaning algorithm according to the access information; and / or obtaining importance information corresponding to one or more cache entries, and screening a second to-be-cleaned cache entry meeting an importance cleaning rule according to the importance information; and according to the first to-be-cleaned cache entry and / or the second to-be-cleaned cache entry, cleaning the cache in the hash table cache pool, and updating the cache capacity of the hash table cache pool. According to the method and the device, the cleaned cache items are identified and determined according to the access information and / or the context importance information of the cache items, so that the cache redundancy and the accumulation of invalid data are reduced, and the cache management efficiency is improved.

The invention provides a control method and control equipment for photovoltaic module production based on a swarm intelligence algorithm, and relates to the technical field of intelligent manufacturing of photovoltaic modules. The method comprises the following steps: acquiring a discrete event simulation model and a multi-target optimization model of a photovoltaic module production line; the multi-objective optimization model takes cache layout, cache capacity and equipment speed as joint decision variables; the discrete event simulation model takes equipment operation data in a set time as input; the output yield, the quantity of products in process and the production energy consumption are taken as output parameters; updating the discrete event simulation model to perform simulation operation by using a joint decision variable output by the multi-objective optimization model to obtain a production efficiency evaluation index; and when the production efficiency evaluation index meets the optimization condition, controlling the operation of the photovoltaic module production line by using the corresponding joint decision variable. According to the invention, global optimization control of cache layout and speed dynamic optimization can be realized at the same time.

The invention provides a method for improving a cache hit strategy and a response speed, and relates to the technical field of cache management policies, the method comprises the following steps: monitoring the use state of a cache in real time, obtaining a cache hit rate, a cache capacity and a data validity index, and generating a real-time monitoring report of the use state of the cache; according to the real-time monitoring report of the cache use state, a cache management strategy is determined from a preset strategy library, and cache data updating is triggered; and when a data request is received, adjusting cache data according to a cache management strategy, including replacing old data and updating data layout, and recording a final access record and a cache state. According to the method, the cache hit rate and the response speed are improved by monitoring the cache state in real time, intelligently selecting the cache strategy, optimizing data access and prefetching, concurrent processing and dynamically adjusting the cache hierarchy, so that the overall performance of the computer is enhanced.

The invention provides a cloud vehicle data caching method, which is applied to a multi-layer storage architecture, and the multi-layer storage architecture comprises a memory queue layer, a distributed cache layer and a persistent storage layer. The caching method comprises the following steps: acquiring cloud vehicle data; caching the cloud vehicle data to a memory queue layer, obtaining a first cache capacity and a first cache duration of the memory queue layer, and when the first cache capacity exceeds a preset first capacity threshold or the first cache duration exceeds a preset first time interval, transferring the cloud vehicle data to a distributed cache layer; and acquiring a second cache capacity and a second cache duration of the distributed cache layer in real time, and when the second cache capacity exceeds a preset second capacity threshold or the second cache duration exceeds a preset second time interval, transferring the remaining cloud vehicle data to a persistent storage layer. According to the caching method provided by the invention, the memory resource utilization rate can be improved, the vehicle data caching efficiency is improved, and the fluency of the caching process is ensured.

The invention discloses a data management method based on a real-time data warehouse, and the method comprises the steps: obtaining column type data blocks through vectorization processing according to collected data, dividing data types according to the access frequency of the column type data blocks, and correspondingly setting storage positions; in response to the data request, determining a data access mode according to the cache hit rate of the data request; and according to the real-time monitoring index, a load trend is predicted through a time sequence prediction statistical model, and vectorized nodes and cache capacity are adjusted. Through vectorization processing, cache hit rate analysis and dynamic resource adjustment driven by load prediction, the core problems of low storage efficiency, poor resource utilization rate, high response delay and the like of a traditional data warehouse architecture are solved, and multi-dimensional breakthrough in performance, cost and stability is achieved.

The invention discloses a request processing method of a multi-core processor, the multi-core processor, a product and a server, and relates to the technical field of multi-core processors. In the method, a main node queries a directory according to a request and obtains a query result, and based on the query result, different request response strategies are adopted to respond to the request. According to the method, the consistency of the shared caches in the non-inclusion relation is maintained in a directory mode, the utilization rate of the shared caches is increased, and the chip area occupied by the caches is reduced under the condition that the cache capacity is fixed; secondly, based on the shared cache architecture of the non-inclusion relationship, the data is allowed to be stored in the private cache and the shared cache at the same time, so that the problem of excessive data exchange caused by cache position exchange in an exclusive architecture can be avoided, meanwhile, the private cache is allowed to reserve the data which does not exist in the shared cache, and the problem of reverse failure in an inclusion mode is avoided. According to the method, the utilization rate of the shared cache is improved, and the data exchange amount and the maintenance cost of cache consistency are reduced.

The invention discloses a medical data preloading method, a computer device, a medium and a product. The method comprises the steps that the type of a patient medical record is determined, and a target strategy is called according to the type to extract a target entity from the patient medical record; constructing a multi-modal medical knowledge graph according to the multi-dimensional data source, and obtaining multiple pieces of relevance information associated with the target entity according to the graph; according to the method, multiple factors are comprehensively considered, the data emergency degree and weight, the semantic association degree and weight and the cache capacity factor and weight are respectively determined, then weighted summation is carried out, the cache level score of a target entity and each piece of relevance information is obtained, and dynamic preloading of the corresponding relevance information is carried out according to the ranking of the scores from high to low. According to the method, dynamic decision making and dynamic preloading can be carried out according to the importance of different data information, multi-source data does not need to be manually called for multiple times, the operation complexity and time cost are effectively reduced, and the method is simple and efficient.

The invention discloses an ultra-high-definition video adaptive code rate control method and system based on deep learning, relates to the technical field of video coding transmission, and solves the problem of data congestion or frame loss easily caused by network space fluctuation in the traditional coding behavior. According to the method, the current total cache amount and the to-be-processed video frame flow of the network nodes are comprehensively considered, the characteristic flow is accurately calculated, the network interval which the processing time period needs to reach is further determined, the data volume which can be processed in the processing time period is determined based on the network interval, and the data volume can be processed by analyzing the past data characteristics of the video stream path nodes and other nodes. According to the technical scheme, the interaction flow of the multiple path nodes is accurately controlled, network resources can be reasonably distributed, data congestion caused by too large flow of the network nodes is prevented, it can be ensured that the video code rate reaches the optimal state in the adjustment and verification process, and the video transmission efficiency and quality are improved.

The invention relates to the technical field of electric data processing, in particular to an intelligent data burning method and system based on data streams. The method comprises the following steps that format recognition and semantic analysis are carried out by collecting input source data to be burnt in real time, so that metadata information corresponding to the data to be burnt is generated; hardware storage structure information and burning protocol specifications corresponding to the target equipment are obtained, data stream description output is carried out, and a corresponding data stream description file is generated; obtaining a storage bandwidth and a cache capacity corresponding to the target equipment, and carrying out burning dynamic task scheduling and low-density redundancy coding partitioning to generate fault-tolerant coding data stream partitioning; equipment state data corresponding to target equipment is monitored in real time, burning write-in processing and block-by-block CRC check are carried out, and meanwhile online repair verification analysis is carried out, so that a data stream burning verification completion result is generated. According to the method, a repair mechanism and dynamic resource scheduling can be designed to realize efficient and reliable data burning.

The invention relates to the technical field of computing resource optimization, and discloses a joint resource allocation and task unloading optimization method and system, and the method comprises the steps: constructing a global resource view to sense the real-time residence state of each computing node cache line in a distributed system; a cache-aware task unloading and data prefetching combined optimization model is established, decision variables of the model include task unloading variables and data prefetching variables at the same time, and cache capacity constraints are constructed in an endogenous mode by means of a task unloading scheme so as to predict a cache line set to be expelled due to task execution; the joint optimization model is solved with the purpose of minimizing the total task completion time, a task unloading decision and a collaborative prefetching decision are synchronously generated, and the prefetching decision is determined according to the predicted expelling set and the task data dependency relationship. According to the method, task unloading and data prefetching are cooperatively decided in a unified optimization framework, so that the problem of mutual interference caused by independent optimization of task unloading and data prefetching is solved, and global active arrangement of calculation and cache resources is realized.

The invention discloses a high-speed multi-port cache arbiter with a variable word length. Along with the high-speed development of the modern network technology, storage management and scheduling take up most of time in the processing of data packets by network equipment, so that the low-speed caching capability of most memories becomes a bottleneck for limiting the further improvement of a network processor. Therefore, by managing the shared cache of not less than 32 blocks of 256Kbit SRAM units and supporting simultaneous cache writing of a plurality of ports through an arbitration mechanism, write scheduling and read scheduling can be realized without mutual influence, and concurrent read-write conflicts are avoided; meanwhile, each port supports eight priority queues, so that the data transmission bandwidth of each port can reach 1G bps; by processing a data packet, caching and data scheduling according to packets are supported, the length of the data packet is 64-1024 bytes variable, and the influence of the length of the data packet on memory resources can be avoided; and the purpose of storing resources can be achieved by dynamically adjusting space saving through memory recovery, and the data storage efficiency is improved.

The invention relates to a serial port instruction intelligent processing method and device based on multi-frame cache and a storage medium. The method comprises the steps that a system is initialized; starting serial port interruption to receive a serial port AT instruction; the received instruction is analyzed; after analysis is completed, the AT instructions are further classified; after instructions are classified and stored in corresponding queues, the system adopts a priority scheduling algorithm to determine which instruction is processed preferentially; and the system executes corresponding operation according to the specific content of the instruction. According to the method, a multi-level cache pool technology is adopted, a virtual cache queue is constructed on a software layer, the equivalent cache capacity is expanded, and the situation that a hardware buffer overflows or instructions are lost due to insufficient process processing capacity is avoided; a priority instruction scheduling algorithm is introduced, instruction grades are dynamically divided, key instructions are ensured to be processed preferentially, and the problem that a traditional single-frame mode cannot respond in time when the MCU is in a high load state is solved; and meanwhile, enhanced frame identification of the protocol is realized, and the problem of insufficient error correction capability of a traditional mode is solved.

The invention discloses a cache capacity control method, device, equipment and system, relates to the technical field of computers, and can improve the utilization rate of memory resources. The method can be applied to a cluster, the cluster comprises a scheduler management node and a plurality of computing nodes, the method is executed by the scheduler management node, and the method comprises the following steps: the scheduler management node receives a cache resource request sent by a first computing node, and allocates a cache resource to the first computing node according to the cache resource request and a resource allocation strategy; determining a target resource quantity allocated to a cache system of the first computing node from a computing memory of the first computing node to obtain a resource allocation result; and sending the resource allocation result to the first computing node. Wherein the resource allocation result is used for indicating that a target resource quantity in idle resources of the computing memory is divided into the cache system, and the resource allocation strategy comprises at least one of the following items: an occupation parameter of the computing memory, an occupation parameter of the cache system and a reserved resource quantity of the computing memory.

This invention discloses a cache adjustment method, apparatus, device, and computer-readable storage medium, comprising: collecting access characteristic data from the client layer, object storage daemon layer, and device layer respectively; determining the object access mode of the system based on the access characteristic data; and adjusting the cache priority of each access task, the cache capacity ratio between the client layer and the object storage daemon layer, and the data residence time in the cache layer according to the object access mode; wherein the cache layer includes the client layer and the object storage daemon layer. This invention improves cache hit rate and overall read / write throughput performance, and reduces access latency.

This application provides a cache space management method and storage device. The method includes: acquiring indicators for adjusting the allocation of cache resources in the cache space, and calculating the cache urgency factor of the cache space; determining the caching strategy of the cache space based on the cache urgency factor and preset first and second thresholds; in response to the caching strategy of the cache space, adjusting the cache capacity of the cache space, and calculating the cache adjustment amount of the cache space based on the cache urgency factor; and adaptively adjusting the capacity allocation of the first cache area and the second cache area in the cache space based on the cache adjustment amount. The above method, by monitoring cache usage and access patterns in real time, adaptively adjusts the allocation of cache resources, optimizes cache resource utilization, and improves the overall performance of the storage device in scenarios such as random reads.

The invention discloses an online recharging service management system based on cloud edge cooperation. The system comprises a sudden change detection module, an implicit state judgment module, an adjustment factor construction module, a duration modeling module, a cloud updating module and a parameter cooperation adjustment module. The mutation detection module is used for monitoring delay, packet loss density and jitter amplitude and generating a mutation mark. And the implicit state judgment module is used for dividing network states into stable, jitter, weak network and interrupt categories. And the regulation factor construction module is used for extracting interval offset, period increment, category switching and active period information. The duration modeling module is used for calculating the state staying duration. And the cloud updating module is used for correcting duration time distribution in an increment mode and deducing duration time prediction parameters of a future window. And the parameter cooperative adjustment module is used for mapping the prediction result into an acceptance limit, a cache capacity and a strategy threshold value and issuing the values to the edge node. According to the invention, the processing continuity and the resource allocation efficiency under the weak network condition are improved.

The invention discloses a multi-path CameraLink signal optical fiber transmission method and system. The method comprises the following steps: a data decoding module receives an original data stream through four differential data channels; the data decoding module switches a working mode and inserts a mark byte in the original data stream according to the auxiliary signal to obtain a target data stream; the data caching module dynamically adjusts the caching capacity so as to smooth the data flow of the decoded data; the data encapsulation module encapsulates the cached data into a target data packet according to a preset data encapsulation protocol; and the optical fiber transmission module outputs a target data packet through a single-path optical fiber. According to the scheme, long-distance transmission is realized by efficiently multiplexing multiple paths of signals and by means of optical fibers, and the length limitation of traditional cable transmission is broken through; the system adopts a modular architecture design, supports flexible configuration to adapt to different CameraLink standards and variable channel number requirements, and improves the universality and reliability of the system through software and hardware collaborative optimization.

The invention provides a cache management method and device, a storage medium and a product. The method comprises the steps that Internet of Vehicles system data in each time period are obtained, the Internet of Vehicles system data comprise state information, position information, cache capacity and an accessibility matrix of each roadside unit, the state information comprises data demand information and identification information and concentration value of cached data, and the data demand information comprises a target position and identification information of target data; based on the Internet of Vehicles system data, a data caching strategy of each time period is obtained through a strategy generation model, and the strategy generation model is established according to a deep reinforcement learning algorithm; based on a data caching strategy, caching management is performed on a target roadside unit set corresponding to the target position, the target roadside unit set comprises a first roadside unit and a second roadside unit, the first roadside unit is the roadside unit closest to the target position, and the second roadside unit is the roadside unit closest to the target position; the first roadside unit and the second roadside unit have communication accessibility meeting a preset condition.

The embodiment of the application discloses a kind of matrix operation method, device, electronic equipment and storage medium, belong to data processing technical field.The method includes: according to the cache capacity of each exclusive cache layer, determine a plurality of candidate basic block parameters, according to the transmission bandwidth between each adjacent cache layer, determine the estimated data throughput corresponding to each basic block parameter, according to estimated data throughput, determine target basic block parameter from multiple basic block parameters;According to target basic block parameter, determine a plurality of candidate core task quantity parameters, determine the data loading time corresponding to each core task quantity parameter according to target basic block parameter;According to data loading time, determine target core task quantity parameter;Get the first matrix and the second matrix to be processed, according to target basic block parameter and target core task quantity parameter, each computing core is scheduled, matrix operation is carried out on the first matrix and the second matrix and corresponding matrix operation result is obtained.The application improves the efficiency of matrix operation.

This invention discloses a large-scale two-dimensional convolution operator acceleration method for DSP platforms, belonging to the field of digital signal processing. This method selects the im2col or col2im algorithm to rearrange data based on the convolution type; adopts a three-segment matrix partitioning strategy to adapt to L3 cache capacity; utilizes an EDMA buffer ping-pong architecture to achieve pipeline parallelism for data transmission and computation; implements SIMD instruction-level optimization, using DMPYSP and DADDSP instructions in conjunction with pipeline optimization to achieve four FP32 multiplication and addition operations per cycle; and implements multi-core parallel scheduling, using OpenMP to achieve task-level and data-level parallelism. This method has been tested on a TI TMS320C6678 platform to achieve efficient inference of SAR target detection networks, providing a feasible solution for real-time inference of CNN networks on DSP platforms.

This application relates to an instruction fetching method, apparatus, and computer device based on a reduced instruction set (RISC). The method includes: obtaining the address of the instruction to be executed; searching for the address of the instruction to be executed in a cache block group; if a first or second cache block is hit, reading and outputting the instruction to be executed corresponding to the address of the instruction to be executed from the corresponding hit cache block; if a third or fourth cache block is hit, writing the instruction currently stored in the corresponding hit cache block into a free cache block in the first or second cache block; and reading and outputting the instruction to be executed corresponding to the address of the instruction to be executed from the free cache block. The entire instruction fetching process, by adding a small-capacity static prediction cache and a victim cache, forms a four-cache group interconnected structure with the dual cache blocks that implement general read / write operations. This improves the cache hit rate and achieves fast instruction fetching even with limited instruction cache capacity.

The invention discloses a cache optimization method and device based on distributed storage, and relates to the technical field of distributed storage systems. The method comprises the following steps: dynamically calculating a weight factor of a cache file, attenuating the weight factor based on the access frequency and timeliness of the file, and compensating and adjusting according to the size of the file; the maximum file threshold value set by the system is dynamically adjusted according to the size distribution of the files in the cache, and the maximum file threshold value is determined based on a preset quantile range so as to eliminate interference of extremely large files on weight calculation; when the weight factor of the cache file is lower than a preset threshold value, the file is removed from the cache, and the cache space is released; when the cache capacity is insufficient, the file with the minimum weight is selected for replacement based on the weight factor of the current cache file, and the high-value file is preferentially reserved in the cache. According to the method, the cache hit rate of small files in the distributed storage system is effectively increased, cache space waste and metadata operation overhead are reduced, and system performance and resource utilization rate are remarkably improved.

The invention relates to a Sencil calculation memory access concurrency performance prediction method based on a queuing theory. The method comprises the following steps: calculating memory access missing numbers under different cache capacities by utilizing a memory access missing number prediction model calculated by Stencil; obtaining CPU storage subsystem information, and extracting a processing process of the memory access request in the hardware device supporting memory access concurrency according to the CPU storage subsystem information; according to the CPU storage subsystem information, the memory access missing number, the processing process and the assumed condition, a memory access concurrency performance prediction model is constructed by using the queuing theory and the Littele law; calculating the memory access concurrency bottleneck of each memory access stream according to the memory access concurrency performance prediction model, and calculating the actual memory access bandwidth according to the memory access concurrency of the memory access concurrency bottleneck and the dynamic memory access delay by utilizing the Littele law; according to the actual execution bandwidth and the data access amount, the actual execution time of Sencil calculation is obtained through calculation. By adopting the method, the prediction precision of the Sencil calculation performance can be remarkably improved.

The invention discloses an underwater AUV (Autonomous Underwater Vehicle) service caching and switch state switching method based on a D3QN (Digital 3QN). The method comprises the following steps: firstly, for an ocean network formed by a water surface buoy node and an AUV, establishing an AUV service cache state and dormancy / activation state coupling control model; then, through consistent binding of a target switch state and a service cache write-in action, dormancy isolation constraint and a dormancy cache freezing rule, a feasible action mask meeting cache capacity constraint and anti-repeated write-in constraint is constructed; the method comprises the following steps of: firstly, constructing a comprehensive cost function according to cache update time overhead, write-in energy consumption, switch state switching cost and invalid activation penalty, and finally, solving in a legal action subspace by using D3QN, and outputting an AUV service cache and switch state switching control strategy. According to the invention, AUV service cache updating and switch state switching under the dynamic load can be effectively realized, and underwater network energy consumption and service cache updating delay are reduced.

The present disclosure provides a memory resource management method, an electronic device, a readable medium and a program product. The memory resource management method of the present disclosure is applied to a storage server, the storage server comprising at least one adjustment object, the adjustment object being a storage service unit meeting a cache memory adjustment requirement, and the method comprising: obtaining, in response to a dynamic adjustment instruction, memory parameter target values of the adjustment objects according to a preset allocation strategy based on an allocable memory capacity; the memory parameter target value of the adjustment object being a parameter value for adjusting the cache capacity of the adjustment object, and the preset allocation strategy comprising allocation according to allocation weights of the adjustment objects or average allocation according to the number of the adjustment objects; and for any one of the at least one adjustment object, updating the memory parameter of the adjustment object by using the memory parameter target value of the adjustment object in the case that the memory parameter target value of the adjustment object and the corresponding memory parameter current value are different.