66results about How to "Improve cache hit ratio" patented technology

The invention relates to the technical field of computer storage, and discloses a computer memory data processing system, which comprises the following modules: an intelligent cache prefetching module, which is used for predicting a future memory access address based on deep learning and reinforcement learning technologies and loading data into a cache in advance; the heterogeneous storage management module is used for dynamically classifying the data based on the data popularity value and realizing migration of the data among different storage media; and the storage consistency guarantee module is used for managing the data consistency among different storage layers through the improved MES I protocol. Cache prefetching is optimized by introducing deep learning and reinforcement learning technologies, and by predicting a future memory access address in real time and loading data in advance, the system effectively reduces the cache missing rate, improves the cache hit rate, remarkably reduces the memory access delay, and improves the memory access efficiency. And the system can keep more efficient data access performance in a large-scale data processing scene.

The invention provides a front-end performance optimization method based on cache management, and the method comprises the steps: S1, collecting and processing user behavior data, and obtaining target data; s2, analyzing a user behavior based on the target data, constructing a user behavior model, and predicting an access demand of a user by using a prediction algorithm to obtain a prediction model; s3, generating a caching strategy based on the user behavior model and the prediction model; and S4, storing resources on the client, the server and the content distribution network node based on the caching strategy. The cache hit rate is improved; the caching strategy is dynamically adjusted according to the actual use condition of the user, and the user requirement is more accurately met; the cache preloading mechanism can shorten the response time of the user request, can adapt to different user behaviors, and improves the user experience. The system and the electronic equipment provided by the invention have corresponding advantages.

The invention provides a meta-universe scene distributed two-stage cache optimization method combining MADDPG and GCN, and the method comprises the steps: dividing a meta-universe scene in the horizontal and vertical directions, obtaining a preset number of sub-scenes, and carrying out the integration of resources needed by the rendering of each sub-scene; the sub-scenes are compressed through an edge network, and the similarity of user tracks is calculated and obtained through a fast dynamic time warping algorithm; based on the similarity of different track fragments, sub-scene popularity ranking is obtained by weighting, summarizing and sorting the number of access times of a GPU server, sub-scenes predicted to be frequently accessed are requested and cached in advance according to the popularity ranking of the sub-scenes, and a two-stage cache replacement framework combining GCN and MADDPG is adopted to adjust a cache strategy of the server. According to the method, intelligent cache distribution and adjustment can be realized according to sub-scene popularity prediction, the cache hit rate is improved, and the delay response of a user request is reduced.

The invention discloses a cloud storage acceleration method and system based on edge computing, and relates to the field of electrical digital data process.The method comprises the steps that a file access track ring is constructed; extracting historical access files as a first associated file set; calculating the access time difference of each historical access file; determining a plurality of association clusters and two association file sets according to the access time difference; performing cyclic displacement in the second associated file set to obtain a displacement associated cluster; extracting a third associated file set; determining a prefetching file; setting an annular cache queue, and determining a prefetching position according to the position information; when the space of the annular cache queue is smaller than a preset value, the prefetched file corresponding to the prefetched position in the preset interval of the annular cache queue is reserved, and a final prefetched file is obtained; and when it is detected that the user accesses the target file, loading the final prefetched file from the cloud storage server to the edge server. The method and device are used for solving the problem that the cache hit rate of the associated files is reduced, and the cloud storage acceleration speed is increased.

The invention relates to the technical field of cache optimization, and discloses a cache optimization method and device, equipment, a storage medium and a computer program product.The method comprises the steps that data information of all cache nodes in different cache hierarchies is obtained, a cache strategy is updated based on the data information, and an updated cache strategy is obtained, the different cache levels comprise a local cache level, a regional cache level and a global cache level; on the basis of the updated caching strategy, each caching hierarchy is optimized, and the optimized caching content is obtained; in response to a content request of a user, carrying out dynamic path selection at different cache hierarchies to obtain a target request path; and obtaining target cache content from the optimized cache content based on the target request path. The method can effectively adapt to data requests of different scales and types, efficient utilization of storage resources and optimization of access performance are achieved, the cache hit rate is effectively increased, and the content transmission cost is reduced.

The invention relates to a virtual core binding method and device, computer equipment and a storage medium. The method comprises the steps of obtaining load data of a plurality of virtual cores in a non-consistent memory access architecture of a cloud host, determining load mean value data of physical cores in the non-consistent memory access architecture according to the load data of the plurality of virtual cores and a preset over-matching ratio, and determining the load mean value data of the physical cores in the non-consistent memory access architecture by taking a difference value between an actual load of the physical cores and the load mean value data as a target, and respectively binding the plurality of virtual cores to each physical core, wherein the physical cores bound by the virtual cores belonging to the same virtual machine are located at the same node of the non-consistent memory access architecture. By adopting the method, the cloud host performance can be improved.

The Spark memory replacement method based on dynamic capacity disclosed in the present application includes: configuring a first replacement algorithm and a second replacement algorithm; when a new RDD needs to be stored, selecting a replacement algorithm according to the degree of memory tension; when the second replacement algorithm is triggered, dividing the existing RDD into two tables with dependency count and without dependency count for maintenance; then obtaining the weight of the existing RDD; then confirming whether the RDD table without dependency count is empty: if it is not empty, then evict the existing RDDs from the memory one by one in the RDD table without dependency count according to the weight from small to large until the new RDD is cached enough and then stop; if it is empty, then traverse the RDD table with dependency count and evict the RDD in the same way until the space is enough to accommodate the new RDD. This solution can reduce the read and write overhead of Spark during operation and the impact on performance to a greater extent under the conditions of different memory environments, improve the cache hit rate, and improve the operating efficiency of Spark.

The invention provides a multi-agent cooperative edge caching method and system based on a gating communication mechanism, and the method comprises the steps: deploying agents in a plurality of edge servers, judging the caching states of the edge servers through employing a gating model, and determining whether to upload the state information of the corresponding agents to a central server or not according to a judgment result. After receiving the agent state information, the central server comprehensively considers the state information of each agent based on a multi-agent decision model, determines an optimal cache decision through a reinforcement learning algorithm, issues the optimal cache decision to the corresponding edge server, and receives the optimal cache decision by the edge server; and transmitting the file data to the client, and performing cache management according to the cache decision. According to the invention, the optimal position of the cooperative edge server can be automatically determined, the servers can selectively exchange information, and the waste of bandwidth resources is reduced.

The present invention proposes a "cloud-edge-end" collaborative caching method for a metaverse scene, the method comprising: dividing the metaverse scene into several sub-scenes, and organizing and compressing the resources required for rendering each sub-scene to obtain a sub-scene compression package; using a fast dynamic time warping algorithm to calculate the similarity between different trajectory segments in each sub-scene; based on the similarity between different trajectory segments, using a multi-GPU distributed streaming computing system to match the segment with the smallest similarity in real time and obtain a corresponding number of recommended predicted sub-scene trajectory sequences; distributing a number of recommended predicted sub-scene trajectory sequences to a number of edge servers to calculate the popularity of the sub-scene, and using a cache replacement framework consisting of states, actions and rewards to adjust the edge server cache strategy. The present invention can realize intelligent cache allocation and adjustment according to the sub-scene popularity prediction, improve the cache hit rate, and reduce the delayed response of user requests.

The invention discloses a low-cost video edge caching method based on federal deep reinforcement learning, and belongs to the technical field of edge caching of cloud edge collaboration. The method comprises the following steps: establishing a network model; the bottom-layer mobile terminal uses local implicit feedback data to train an auto-encoder, the edge server of the middle layer uses a federated learning framework to aggregate local models of the mobile terminal, and a global model is obtained after iteration; predicting by using the global model; according to the method, calculation and communication resources consumed in the federation training process are modeled as a comprehensive system cost problem, and a user calculation resource allocation problem is further expressed as a Markov decision process, so that the calculation resources of each federation training client can be effectively allocated on the premise of meeting resource constraints, and the resource allocation efficiency is improved. And the total cost of the federal learning process is reduced to the greatest extent. Experiments show that the cache hit rate of the method is higher than that of a baseline algorithm and is close to that of an optimal algorithm, and the system cost can be effectively reduced under the condition that local resources are limited.

The invention discloses a method for accelerating sparse matrix calculation on a tensor processing unit and a storage medium, and the method comprises the steps: obtaining a first matrix which comprises a row index value or a column index value, and is a sparse matrix obtained based on an input sequence; reordering rows and columns of the first matrix based on the row index value or the column index value to obtain a second matrix, so that the cache hit rate of the second matrix is higher than that of the first matrix; and reading a third matrix from a memory of the GPU, and performing operation on the second matrix and the third matrix, the third matrix being a matrix for performing operation with the second matrix, and the third matrix being obtained based on the input sequence. By means of the technology, the matrix operation is executed on the sensor core, redundant calculation operation is reduced, the cache hit rate in the running process is increased, efficient calculation of the sparse matrix and optimal utilization of operation resources are achieved, and the overall performance of matrix calculation is greatly improved.

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.

The invention provides a web page loading speed optimization method and device, and the method comprises the steps: carrying out the optimization of a network layer, so as to reduce the network delay and server load; static resources are optimized to reduce the resource volume and the loading time; and browser rendering is optimized, so that the page rendering efficiency and the user experience are improved. The web page loading speed optimization method and device have the advantages that through intelligent network acceleration, refined resource management and user perception oriented rendering optimization, the page loading time can be effectively shortened, and the user access speed is increased; and the access frequency and the update frequency of the resources are intelligently predicted by using a machine learning algorithm, and the cache strategy is dynamically adjusted, so that the cache hit rate can be effectively improved, the network request times are reduced, and the page loading speed is further improved.

The present invention provides a method for optimizing a memory and a cache block, wherein the memory includes: firmware, in which a free list, a write cache list and a read cache list are arranged; when performing a read and write operation, the main control unit is used to call a cache block from the write cache list and the free list to perform a write operation, and to call a cache block from the read cache list and the free list to perform a read operation; when performing a recycling operation, when the mapping relationship of the host data to be erased matches the mapping relationship of the host data stored in a certain cache block in the write cache list and the read cache list, the host data of the cache block is erased, and the relevant information of the cache block is stored in the free list. Through the method for optimizing a memory and a cache block provided by the present invention, the allocation mode of the cache block can be optimized, so that the cache blocks can be mixed in different scenarios.

This invention discloses a joint optimization method for mobile edge computing and service caching assisted by multiple UAVs, comprising: constructing a multi-UAV-assisted edge computing system model; constructing a communication model; dividing the system decision time into a set of large-timescale periodic sequences and a set of small-timescale time slot sequences, each large-timescale periodic sequence containing multiple consecutive small-timescale time slot sequences; deriving expressions for the total task processing latency and total energy consumption of the system in any time slot; establishing a joint optimization objective function with the goal of minimizing the total system latency and total energy consumption; constructing a Markov decision mechanism; employing the SAC deep reinforcement learning algorithm to implement the dual-timescale decision-making of the system; and solving the joint optimization objective function to obtain the optimal joint optimization scheme. This invention can significantly improve the edge service cache hit rate, optimize UAV load balancing, and reduce task processing latency and overall system energy consumption.

The invention relates to the technical field of wireless communication, in particular to a cache optimization method based on file freshness and content recommendation, and the method comprises the following steps: a BS collects file requests initiated by users before each time slot and randomly responds to requests of R users, and when a user i requests a file j and is responded by the BS, the user i sends the file j to the BS; the method comprises the following steps: firstly, determining whether a file j meeting an information age threshold requirement exists in users in a D2D communication range of a user i, and if so, acquiring the file j from the users in the D2D communication range of the user i for caching; if the file does not exist, a recommendation algorithm is executed, an appropriate file is selected from the files cached in the users within the D2D communication range of the user i to be recommended to the user i, and if the user i refuses to accept the recommended file, the requested file j is obtained from the BS to be cached. The system energy consumption is reduced.

This application provides a method, system, device, and medium for virtual terminal resource preloading and caching management based on the ISOBUS standard. This application innovatively constructs a multi-level caching architecture and combines a Markov chain-based resource prediction engine with adaptive memory management technology to achieve intelligent preloading and efficient caching of interface resources. Addressing the pain points of existing ISOBUS VT systems, such as high resource loading latency, low cache hit rate, and insufficient memory utilization, this invention provides a targeted technical solution. Experimental data verifies that this technical solution can reduce resource loading time by 50% to 70%, increase cache hit rate to over 92%, improve memory utilization by 40%, and reduce system crash rate by over 80%.

The present disclosure provides a blockchain contract processing method and device, electronic equipment and storage medium, comprising: loading N candidate smart contracts in batches from the to-be-processed smart contracts; in response to identifying that the candidate smart contract needs to perform a target external data read-write operation, judging whether the target external data exists in the local cache; in response to the target external data not existing in the local cache, performing saving processing on the first execution context data of the candidate smart contract, and determining a target contract from the to-be-processed smart contracts except the N candidate smart contracts; replacing the target contract with the candidate smart contract, and loading the target contract to the execution slot of the execution memory corresponding to the candidate smart contract, and executing the target contract. When it is found that the candidate smart contract needs to wait for the database to return, the context is saved and the execution is switched, avoiding the idling of the execution unit, improving the hardware utilization rate, and at the same time, by supporting batch preloading, the cache hit rate and execution continuity are improved.

The application relates to an active cache routing method and system of a satellite content distribution network, the satellite content distribution network comprising at least one satellite node. The method comprises: extracting first semantic information features of distributed content and second semantic information features of historical popular content by using a deep learning model; calculating semantic similarity between the first semantic information features and the second semantic information features; calculating a satellite semantic cache priority value of a satellite node corresponding to the distributed content according to the semantic similarity; and planning an active cache routing path of the distributed content according to the satellite semantic cache priority value of each satellite node. The application can enable the satellite to automatically make a cache decision of the distributed content in the content distribution process, and improve the efficiency of active cache by combining content distribution and cache deployment.

The invention discloses a strategy optimization method for cache garbage collection, relates to the technical field of computer cache management, and is used for solving the problem of low cache collection efficiency. According to the method, a cache storage area is abstracted into a plurality of cache partitions, a partition state vector composed of capacity, access and fragmentation degree operation indexes is constructed for each partition, and a strategy evaluation thread periodically reads the partition state vectors to calculate garbage recycling pressure and recycling priority. The upper limit of the number of scanning objects and the upper limit of time slices are distributed for the target cache partition by combining the idle time proportion of the processor, and a recycling execution thread conducts incremental recycling in a batch mode according to a strategy list and records the recycling progress; after recovery, index weight and scanning configuration are adjusted according to release capacity, fragment degree change, hit times and back-to-source request change, so that garbage recovery gives consideration to release space and overhead, the hit rate and the memory utilization rate are improved, and pause and invalid recovery caused by centralized recovery are reduced.

The invention relates to a data acquisition method, device and system, computer equipment and a storage medium. The method comprises the following steps: a client initiates a data request to a first edge node to request to obtain target data, the first edge node responds to the data request to query the target data in a local cache, and if the query fails, a target networking center is determined from a plurality of networking centers according to a hash result corresponding to a resource positioning identifier of the target data, and initiating a node query request to the target networking center to obtain a node address of the second edge node, providing the node address of the second edge node for the first edge node through the target networking center, and returning a 302 redirection address indicating the node address of the second edge node to the client by the first edge node, and enabling the client to access the second edge node according to the 302 redirection address so as to obtain the target data. In this way, the cache hit rate can be increased without reducing the number of service nodes, and the disaster recovery risk can be reduced through a plurality of networking centers.

The invention discloses a batch data processing method and device suitable for a database, equipment and a medium, relates to the technical field of data processing, and is applied to a processor. Monitoring whether a target instruction meeting a preset stride condition exists in a data processing instruction stream corresponding to the preset database in the target assembly line or not, if yes, entering an instruction pre-execution mode, vectorizing the target instruction and a subsequent instruction having a dependency relationship with a register of the target instruction, and outputting the vectorized instruction; performing instruction pre-execution based on the vectorized instruction and a first execution unit, and storing an instruction pre-execution result to a first physical register file and a result cache region; and when the data processing instruction stream in the target assembly line reaches the input end of the second execution unit, matching the instruction pre-execution result in the result cache region, and completing batch data processing operation based on an information matching result. According to the invention, the efficiency and accuracy of batch data processing are improved.

The present invention discloses a collaborative caching method based on quantized federated reinforcement learning in a fog wireless access network, including: 1. Initializing the local cache content of the cache node according to the global content popularity, initializing the local model weight parameter of the cache node, the number of local model iterations in a single cycle and the total model training cycle; 2. Each cache node makes content cache decisions and local model training according to the received user request information; 3. Calculate the cache hit rate and user request delay; 4. At the end of a training cycle, perform network pruning on the local model of each cache node; 5. Perform quantized compression and weight sharing processing on the local model update amount; 6. Upload the local model update amount to the cloud center for global model aggregation to obtain a global model, and then distribute the global model to each cache node to enter the next training cycle. The present invention improves the cache hit rate and reduces the network load pressure.

The present invention relates to a mobile edge cache optimization method based on effective federated learning, and belongs to the fields of Internet of Things and artificial intelligence. The method takes into account the changing user mobility and content popularity within a single base station, and places the requested content in the edge cache in advance by predicting the content popularity to improve the cache hit rate. Specifically, the RWP random path point model is used to obtain the user time trajectory table to simulate the user's mobile path. Taking into account the local training consumption, the users who participate in the FL local training are selected by combining clustering with thresholds, and the model weights are controlled by the attention mechanism to perform global model aggregation. According to the obtained global prediction model, the predicted request content is cached to the server in advance to improve the cache hit rate. The method uses a federated learning method to optimize client selection and weight aggregation, and implements an effective federated learning method to reduce local training consumption and improve cache hit rate.

The invention belongs to the technical field of data information processing, and discloses a similar object searching processing method based on a Huffman algorithm, a Hash algorithm and Cache, which comprises the following steps of: firstly, carrying out Huffman coding on a similar object searching array to generate compressed binary data, and processing the compressed binary data through the Hash algorithm to generate a Hash value; then, a cache is used for storing the hash value and the corresponding compressed binary data; when the data are accessed, corresponding compressed binary data are searched in the cache according to the hash value, and if the corresponding compressed binary data are found, the compressed binary data are read; if the original data source is not found, accessing the original data source, compressing the found data, storing the compressed data and the obtained Hash value into a cache, and reading the Hash value; and decoding the compressed binary data read from the cache, and after all threads finish processing, carrying out search and modification operations. According to the method, the Huffman algorithm and the Hash algorithm are fused, and the cache is optimized, so that efficient compression and quick search for searching similar objects by the EDA are realized.

This invention relates to a lock-free I / O scheduling method, apparatus, and storage medium for cloud PCs. Responding to a client session request initiated by a client, the method controls a metadata coordinator to allocate storage nodes to the client and, within those storage nodes, allocates I / O shards for the client, obtaining allocation information. The metadata coordinator then returns the allocation information to the client, and the client caches this allocation information to facilitate the use of the allocation information to fulfill I / O requests. Each I / O shard includes an I / O thread bound to a first CPU core, a data plane development toolset driver, a storage performance development toolset driver, a first lock-free circular buffer, and a second lock-free circular buffer. By binding the CPU core and using lock-free circular buffers, thread switching, lock contention, and most memory copying are eliminated, reducing I / O processing latency from milliseconds to hundreds of microseconds and significantly reducing latency jitter.