272 results about "Cache hit rate" patented technology

The invention relates to the technical field of chip scheduling and optimization, in particular to a neural network-based main control chip task scheduling and dynamic performance optimization method, which comprises the steps of data acquisition and feature engineering, neural network model design, simulation environment training, model compression and deployment preparation, real-time state monitoring, dynamic decision reasoning, scheduling strategy execution and performance optimization. The data acquisition and feature engineering comprises the following steps: S1, hardware index acquisition; analyzing task attributes (calculation-intensive / IO-intensive), a dependency relationship (DAG), deadline (Deadline) and a resource demand (CPU / GPU occupancy rate); collecting data during chip operation through a performance counter (IPC, cache hit rate and branch prediction error rate), a temperature sensor and a power consumption monitoring unit (PMU); the neural network scheduler can achieve the energy efficiency ratio which is 20%-40% higher than that of a traditional method (such as a CFS scheduler), meanwhile, the neural network scheduler adapts to sudden load changes, and the practicability and the application range of a main control chip are wider.

The invention belongs to the technical field of wireless communication, and discloses an edge computing and cache enabling meta-universe system and an intelligent optimization method, and the technical scheme of the invention comprises the following core optimization objectives: firstly, through an intelligent content cache strategy, a user cache hit rate is maximized, and unnecessary data transmission is reduced; secondly, task unloading decisions are optimized, computing resources are reasonably distributed, and energy consumption of user terminals is reduced; and thirdly, dynamic intelligent allocation of computing resources is realized, and the overall resource utilization efficiency of the system is improved. The ADRL algorithm provided by the invention has the following unique advantages: 1, by introducing an active reasoning mechanism, the decision ability of the algorithm in an uncertain environment is enhanced; secondly, in combination with preference information of the intelligent agent, an optimization strategy is more targeted; and thirdly, comprehensive balance of a multi-dimensional optimization target of the system is realized, and powerful technical support is provided for efficient operation of the element universe network.

The invention discloses a cache space control method of a storage system, electronic equipment and a storage medium, and relates to the technical field of data caching, and the method comprises the steps of identifying dirty data, comparing the size relation between the hit rate of cached data in a cache space and a first hit rate threshold value and a second hit rate threshold value, and when the hit rate is greater than the first hit rate threshold value, controlling the cache space of the storage system. When the hit rate is smaller than a first hit rate threshold value, executing an expansion instruction to control the cache module to expand a cache space, and when the hit rate is smaller than a second hit rate threshold value, controlling the cache module to brush dirty data down to a rear-end memory, executing a reduction instruction to control the cache module to reduce the cache space, and performing a cache hit rate judgment mechanism and a linkage telescopic cache mechanism in a full random access scene. The problems of performance reduction, high delay of data access, high power consumption and the like of the cache system due to the fact that the cache space is fixed and cannot adapt to different application scenes and changes of data loads in the prior art are solved, performance self-adaption of the IO model is achieved, the response speed is increased, and the performance of the cache system in multiple storage scenes is improved.

The invention belongs to the field of integrated circuits and computer system structures, and provides a caching method and system for a memory access unit of a superscalar processor, and the method comprises the steps: receiving a plurality of memory access instructions in the same period, and determining a corresponding Bank in a to-be-accessed cache through the memory access instructions; after the memory access instruction obtains a cache access permission, if cache line missing occurs, generating a missing request, merging all the missing requests, and performing parallel prefetching training on the merged missing requests by utilizing a mode of fusing a constant step length prefetching mode and a complex step length prefetching mode to obtain a prefetching request and a prefetching cache address corresponding to the prefetching request; requesting a missing cache line from the first-level cache to the second-level cache based on the missing queue, and writing the missing cache line back to the cache line of the corresponding data cache in the first-level cache; and storing the bus consistency request by using the sniffing queue, judging whether the data in the multi-core cache are consistent or not by using the consistency request, and performing consistency modification according to a judgment result. The cache hit rate and the bandwidth utilization rate are improved.

The invention discloses a storage and calculation separation key value storage delay optimization method based on prediction driving, and aims to solve the problems of performance bottleneck and high tail delay caused by passive management and high network delay in a key value storage system in a storage and calculation separation scene. The method comprises the following steps: time load prediction: deploying a time sequence prediction model at a client, and predicting a future read-write request based on a historical access sequence; active cache prefetching: according to the predicted read request, actively preloading hotspot data from a server side to a client side for caching so as to improve the cache hit rate and hide network delay; active write-in optimization: according to the predicted write request, executing maintenance at a server side through predictive pre-insertion and active node splitting, and moving high index structure adjustment overhead out of a key request path to eliminate a write delay peak; and structure sensing batch synchronization: pre-fragmenting a local write buffer by using a server index top layer model of a client cache, and combining multiple independent remote insertion operations into one efficient batch update to reduce data synchronization overhead. Compared with an existing passive management system, the characteristics of model prediction and active cooperation are fully utilized, and the average delay and the tail delay of the system are reduced.

The invention discloses a data cache management method and system based on big data, and relates to the technical field of data cache management, and the method comprises the steps: collecting access mode metadata, constructing a data cache state space and an action space, and screening an effective action set of data cache; on the basis of the state space, calculating a context feature weight, and constructing a CO-IAM model to predict a data caching action; performing data caching action implementation based on a CO-IAM model prediction result; by constructing a state space and an action space of data caching, introducing a CO-IAM model, performing precise modeling and dynamic adjustment based on context feature weight, generating an invalid action mask, filtering useless caching actions, optimizing a caching hit rate and access delay, and improving the performance of the whole caching system.

The invention relates to the technical field of artificial intelligence chips and storage management, in particular to a cache line replacement method and device, a chip and electronic equipment. The method is applied to a chip comprising a data cache, the data cache comprises N cache lines in which data are stored, an access path corresponding to each cache line is managed based on a binary tree structure comprising N-1 nodes in a PLRU algorithm, and a data replacement instruction is responded, obtaining a path status bit from a root node to a leaf node on an access path corresponding to each cache line; for a specified cache line, calculating a PLRU path value according to the path status bit, and calculating a replacement score in combination with the importance level and the corresponding weight of the stored data; determining a replaceable cache line according to the replacement score; and storing the to-be-cached data and the importance level thereof into a replaceable cache line to replace the original data and the original level. Therefore, the cache line replacement priority is evaluated by combining the PLRU path value and the importance level of the stored data, the cache hit rate is effectively improved, and the system performance is optimized.

The invention discloses a method for accelerating secure metadata access in a secure memory system, a memory controller and a system, and belongs to the field of secure memory systems, and the method comprises the following steps: when a page table item corresponding to a logic page where data accessed by a processor is located does not hit a TLB, obtaining the page table item from a memory page table, extracting a physical page address from the page table item, and storing the physical page address in a memory; a counter corresponding to a physical page where the data to be accessed is located and a father node of the counter in the integrity tree are prefetched through the physical page address; adding a replacement dirty block address in a miss request sent by the last level of cache, after receiving the miss request containing a field of the replacement dirty block address, executing conventional memory reading and decryption, positioning a counter corresponding to the replacement dirty block address, and performing prefetching by using an idle memory bandwidth; in addition to the secure metadata cache, the prefetching queue is maintained to temporarily store the prefetched metadata. The cache hit rate of the security metadata in the security memory system can be improved, and the performance overhead caused by the cache miss can be reduced.

The invention discloses a multi-core scheduling system and method based on interrupt affinity and a storage medium. The technical problem that data locality guarantee and load balancing are difficult to consider at the same time is solved by constructing a self-adaptive optimization closed loop of perception-decision-execution-feedback, and the method comprises the steps that a routing interruption configuration mechanism provides a basis for dynamic adjustment; an interrupt guide task placement mechanism ensures that an interrupt service program and an associated task are executed in the same CPU core, and the cache hit rate is increased; the execution overhead of an interrupt service program is brought into core load statistics through task context switching and a'task + ISR 'two-dimensional precise load evaluation mechanism triggered by ISR inlet / outlet double nodes, and real load awareness is achieved; a load-driven dynamic interrupt routing mechanism is combined with dual control of a hysteresis threshold and cooling time, so that a ping-pong effect is avoided. According to the method, dynamic load balancing is realized on the premise of keeping data locality, and the real-time response performance and throughput of the multi-core system are remarkably improved.

The invention provides a cache elimination method and device, equipment, a medium and a program product, relates to the technical field of data processing, and aims to solve the problem of low cache hit rate. The method comprises the following steps: monitoring a key operation event of a Redis node based on an EBPF program to obtain a first key name of a first key value pair operated by the key operation event and access information corresponding to the key operation event; inputting a first service label corresponding to the first key name and the access information into a preset machine learning model for judgment to obtain a target cache elimination strategy of the first key value pair; updating a preset strategy mapping table based on the first service label and the target cache elimination strategy to obtain a first updated strategy mapping table; and performing cache elimination on the key value pair of the Redis node based on the first updating strategy mapping table. The cache hit rate can be improved.

The invention discloses a front-end cache management method, system and device for a lightweight large-model dialogue state and a medium, belongs to the technical field of front-end cache management of a large-model dialogue system, and aims at solving the technical problem of how to overcome the defects that in a traditional scheme, long context cache is low in efficiency, storage redundancy and insufficient in dynamic semantic adaptation capacity, and the large-model dialogue state cannot be managed easily. In order to realize dialogue context volume compression, improve semantic similar request hit rate and reduce cross-end synchronization delay, the adopted technical scheme is as follows: data acquisition and preprocessing: capturing user interaction behaviors in real time through front-end burying points, and performing preprocessing operation on the acquired user behavior data; semantic normalization processing: performing embedded vector conversion and semantic clustering on the text input by the user to generate a unique semantic identifier and a context vector; querying and updating the multi-level cache; and dynamic collaborative updating: dynamically adjusting the cache based on the cache hit rate, the response delay and the user feedback, and optimizing the cache effect in real time.

The invention provides a distributed large model reasoning method and system based on modular cache, and the method comprises the steps: disassembling a prompt into modules which can be independently reused through modular division driven by Schema, and calculating the attention Key-Value state of each module in advance before reasoning for caching. When a user request arrives, the system firstly performs module analysis and cache assembly on a prompt, loads a cache as required and supplements a missed part, and then injects a complete KV state into a reasoning process. Meanwhile, a layered and distributed scheduling mechanism is designed, a global scheduler selects a target GPU based on a cache hit rate and a node load, and a local scheduler manages cache copying and elimination in a node, so that load balancing and cache multiplexing are both considered. According to the scheme, repeated calculation can be remarkably reduced, the first Token delay is reduced, the system throughput rate is increased, and the method is suitable for high-repetition prompt scenes such as code generation and long text question and answer.

The invention discloses a cache partition dynamic allocation method and device, equipment and a storage medium, and relates to the technical field of storage, and the method comprises the following steps: obtaining an initial cache hit rate and an initial data transmission bandwidth of each I / O data stream; adjusting cache resources corresponding to the I / O data stream for multiple times according to a preset cache step length adjustment rule, and obtaining an adjusted cache hit rate and a data transmission bandwidth; according to the initial cache hit rate, the initial data transmission bandwidth, the adjusted cache hit rate, the adjusted data transmission bandwidth and the cache resource adjustment vector, generating cache sensitivity; after all the cache sensitivities are obtained, sorting all the cache sensitivities; and according to the sorting result and a pre-configured cache resource adjustment rule, determining to dynamically adjust the cache resources of part or all of the I / O data streams. According to the method, performance reduction caused by cache pollution and resource competition is effectively reduced, and cache resources are utilized more efficiently.

The invention discloses a data synchronization method based on edge side distributed cache, and relates to the technical field of data management. The method comprises the following steps: S1, calculating the occupational behavior entropy OBE of data, wherein the OBE is calculated through three dimensions of access frequency variance, timeliness attenuation rate and multi-source correlation Cr; according to the method, a dynamic fragmentation mechanism is constructed by introducing occupational behavior entropy OBE, the problems of low cache hit rate and back-to-source request sharp increase caused by a traditional fixed fragmentation strategy are effectively solved, and the operation principle of the method is as follows: firstly, the data activity degree is quantized based on three dimensions of access frequency variance, timeliness attenuation rate and multi-source association degree Cr; wherein the data distribution imbalance is reflected through the access frequency variance of different edge nodes of a certain type of data in a dynamic time window, the data type is combined to set an attenuation coefficient to calculate the timeliness, and the Cr represents the data association degree through knowledge graph association edge number normalization.

Provided in the embodiments of the present disclosure are a data processing method and apparatus, and a data insertion method and apparatus. The data processing method comprises: in response to a data processing request, on the basis of a target key of target data carried in the data processing request, determining a target prefix tree corresponding to the target data; searching for the target key from a root node of the target prefix tree, and determining a leaf node corresponding to the target key, wherein the number of bytes used by the leaf node is consistent with the number of bytes of a corresponding cache line, and the leaf node comprises a data mapping area and a data management area; loading at least one initial key-value and the cache line corresponding to index information, and acquiring the at least one initial key-value and the index information from the cache line; and on the basis of the index information and the target key, determining the target data from the at least one initial key-value, and on the basis of the data processing request, performing data processing on the target data, so as to obtain a data processing result. Therefore, a cache hit rate and a data access speed are improved, such that target data can be quickly located.

The invention discloses a private cloud platform-based micro-service application remote main and standby disaster recovery rapid switching method, relates to the technical field of computers, and is used for solving the problems of long remote main and standby disaster recovery switching time, cold start storm, insufficient capacity evaluation and discharge strategies and complex back switching. The switching process is decomposed into controllable steps by combining real-time state feedback through a clear sequential arrangement and batch reconnection strategy of a data surface and a control surface, so that the switching time is remarkably shortened, the target of minute-level continuous availability is met, and meanwhile, the switching efficiency is greatly improved through heat set construction and standby side quasi real-time preheating before switching. Core hotspot data and indexes are loaded to the standby side in advance, it is ensured that the standby side has the high cache hit rate and index availability during master raising, and therefore the performance bottleneck caused by access of a large amount of cold data is avoided.

The invention discloses a water engineering scheduling intelligent parallel caching system for intelligent water conservancy and an optimization method. The system comprises a constraint-driven asynchronous hierarchical caching module, a topology-sensitive cumulative hash module and a polymorphic hierarchical storage module. According to the system, a three-level framework of thread local buffering, global batch processing and an generational sliding window is adopted, and boundary constraint pre-verification is executed on a scheduling scheme; decision vectors are rearranged according to the upstream and downstream topological relation of the water engineering, an upstream engineering decision preferentially participates in Hash calculation and is endowed with a higher weight, and the weight is decreased progressively according to an exponential function; target value arrays are dynamically distributed, and volume class, flow class and time sequence class constraint violation degrees are stored in a layered mode; according to the method, through a topology-aware Hash mechanism and a self-adaptive caching strategy, the recognition precision and the caching hit rate of similar schemes are remarkably improved, repeated calling of a hydraulic simulator is effectively reduced, and the calculation efficiency of water engineering scheduling optimization is improved.

The invention belongs to the technical field of computers and artificial intelligence, and discloses a priority differential cache-based MoE batch reasoning method and system, and the method comprises the steps: recognizing global hot experts with higher activation frequency in each layer, enabling the global hot experts to remain in a high-priority cache during initialization, and building a priority list of the experts in each layer; in the reasoning stage, selecting an expert set needing to be activated in the current layer; the expert priority score is dynamically updated according to the activation and reuse behaviors of the experts, and the experts are migrated and replaced among the high-level cache, the middle-level cache and the low-level cache according to the priority score and a cache threshold value; according to a current layer gating result and a historical activation track, predicting an expert set which may be activated in a next layer, and asynchronously loading the expert set to a low-priority cache during current layer calculation so as to realize time overlapping of calculation and communication. According to the method, the expert migration frequency can be reduced, the cache hit rate and the throughput rate are improved, the video memory utilization rate is remarkably optimized, and good expansibility and universality are achieved.

The embodiment of the invention provides a multi-level cache access method, system and device and a storage medium, and relates to the technical field of data cache access, and the method comprises the following steps: obtaining a key field in a user auditing request, and converting the key field into a feature vector; searching whether a reasoning result corresponding to the feature vector exists in a first preset cache or not; if yes, a reasoning result is returned to the user; if not, a corresponding semantic partition is found in a second preset cache, and a historical cache vector with the highest similarity and a corresponding reasoning result are determined from the semantic partition; and determining a reasoning result corresponding to the feature vector according to the feature vector, a historical cache vector and a reasoning result corresponding to the historical cache vector. In this way, a two-stage cache mechanism based on the feature vector is constructed, a corresponding reasoning result is found in combination with semantic partition, the cache hit rate is increased, repeated reasoning of a large model is reduced, response delay is reduced, and therefore the requirements for high redundancy and high real-time performance of cache data reasoning are met.

The invention discloses a performance monitoring system and method for expansion and upgrading of a big data platform, and relates to the technical field of big data analysis. According to the performance monitoring system and method, key information of newly-added Web servers is collected to form a standardized configuration data set, and node deployment normalization and cluster compatibility in the expansion stage are improved; operating load logs before capacity expansion are directionally pulled and screened, and a historical cluster server operating load set is constructed through structural analysis; first dynamic weight configuration is completed by combining two types of load sets, the cache types of the Web servers are distinguished according to cache configuration identifiers, and secondary weight adjustment is performed on the shared distributed cache Web servers based on the hit rate, so that the defects of existing weight configuration are overcome, accurate weight adaptation is realized, the cache hit rate is increased, the frequency of requesting to penetrate through a database is reduced, and the service life of the Web servers is prolonged. The database load is reduced, the response delay is shortened, resource imbalance and service instability are avoided, and the performance of platform expansion upgrading operation is guaranteed.

The invention relates to the technical field of storage control, in particular to a memory control method, a storage device, a medium and computer equipment, a three-level index structure is arranged, only a first-level root table resides in a cache, an index is rapidly extracted through bit operation so as to reduce cache occupation and invalid IO overhead, the search range is narrowed through a hash bucket of a three-level page table, and the search efficiency is improved. Conflicts are solved through a linked list, and the mapping efficiency and the system stability can be improved through a fixed structure; and the second-level block table and the third-level page table are loaded in a non-blocking manner without occupying a CPU, so that the bandwidth is saved, and the concurrency performance and the cache hit rate are improved. Through the overall scheme of three-level index, chained hash table, loading on demand and bit operation, the delay of the whole process from logic address input to physical address output is remarkably reduced, and the low delay requirement of a high-performance storage system is met; the three-level index structure supports flexible adjustment of the sizes of a root table, a block table and a page table, adapts to storage devices with different capacities, and has good expandability.

The invention relates to the technical field of database query optimization and distributed system data processing, and discloses an intelligent routing optimization method and system for multi-table association query, and the method comprises the steps: a query topology analysis unit analyzes an object relation mapping configuration file to construct a query topological graph; the environment parameter detection unit collects real-time state data of the server cluster based on the query topological graph, wherein the real-time state data comprises a pre-estimated cache hit rate, associated sparseness and a target table data line number estimation value; the routing strategy decision-making unit compares the real-time state data with a preset threshold value, and selects a cache-driven step-by-step query strategy, a column-type compression transmission strategy or an asynchronous intermediate table unloading strategy; and the dynamic execution control unit executes data acquisition and assembly operation according to a selected strategy. According to the method, the problems of low database computing resource utilization rate and application server memory overflow caused by the adoption of a static strategy in a traditional framework are solved, and the system response speed and the service availability of multi-table association query are effectively improved.

The invention relates to the technical field of server testing, in particular to a server multi-core computing processor load optimization testing method, device and equipment and a medium, and the method comprises the following steps: automatically identifying hardware configuration information of a server; generating a multi-dimensional pressure test plan based on the hardware configuration information; executing the test plan, and monitoring the load rate, the memory bandwidth, the cache hit rate and the I / O performance index of each CPU core in real time; dynamically adjusting a task allocation strategy based on a set load difference threshold to realize task dynamic optimization, and if the difference between the maximum core load rate and the minimum core load rate is monitored to exceed the threshold, triggering a task migration operation; system performance data are collected again, performance indexes before and after optimization are compared and analyzed, and system performance bottlenecks are recognized; and automatically generating a performance test report. The utilization rate and the overall task throughput of the multi-core processor are improved, and the problem that a static task allocation strategy is difficult to adapt to dynamic load changes is solved.

The invention discloses a high-concurrency time series data processing method and device for an anti-unmanned aerial vehicle system. According to the method, a lock-free hash table is adopted in a cache space to cache time sequence data, lock overhead is reduced through lock-free cache design and CAS atomic operation, the batch writing throughput is high, the conflict probability is low, multi-thread high-concurrency reading and writing are supported, thread competition and blocking caused by a traditional cache mechanism are thoroughly eliminated, and the concurrent processing capacity is improved; meanwhile, the expenditure of dynamic memory allocation is reduced through batch writing, and the memory utilization rate is increased; through quick positioning of a compound key, priority query of a hotspot data index structure and a lock-free reading mechanism, the query delay of hotspot data is extremely low, the delay of related query in the future is further reduced through a data preloading mechanism, and the real-time response capability is improved; based on a dynamic elimination mechanism of an elimination score, hotspot / recent data can be reserved preferentially, the resource utilization rate is ensured, the cache hit rate is remarkably improved, and the access frequency of the TSDB is greatly reduced.

The invention discloses a data warehouse intelligent construction and management method and system, and belongs to the technical field of data warehouse construction and management, and the data warehouse intelligent construction and management method comprises the steps: obtaining an enterprise data source, and extracting the feature parameters of the data source; constructing a data warehouse logic model; self-adaptive ETL configuration parameters are defined; constructing a three-layer physical architecture comprising a data access layer, a data storage layer and a data service layer; intelligent cache strategy configuration is executed based on a deep learning algorithm, a three-level cache architecture is established, and a pre-cache rule and a failure strategy are defined; and running management optimization is implemented through a multi-objective optimization function. According to the method, the construction automation level is remarkably improved, the cache hit rate is increased, the average query response time is shortened, the data consistency and the system performance are effectively balanced, the elastic adaptive capacity of the system is enhanced, the operation and maintenance complexity and cost are reduced, and meanwhile efficient sharing of cache resources is achieved on the premise that data security isolation is guaranteed.

The invention discloses a storage system based on AI drive and a data collaborative management method thereof. The method comprises the following steps: receiving a data block priority score and a fault prediction result reasoned from each storage node; the data block priority score is obtained by fusing the behavior feature, the semantic feature and the aging feature of each data block acquired by using an AI reasoning model integrated by storage nodes; the fault prediction result is obtained by predicting the state of the storage node by using the storage node; aggregating the data block priority score of each storage node, the fault prediction result and parameters of the AI reasoning model and the LSTM anomaly detection model through a federated learning framework to train a local global prediction model; and outputting a data synchronization strategy, a fault recovery plan and a cache allocation strategy of the dynamic cache pool by using the global training model, so that the storage nodes and the dynamic cache pool carry out data distribution adjustment. Therefore, the cache hit rate and the fault recovery rate are improved.

The invention discloses a BFS-like algorithm-oriented graph processing system caching method, a controller and a system, and belongs to the technical field of graph processing system performance optimizing.The method comprises the steps that based on the relation between cache page access popularity and the number of unaccessed vertexes in a BFS-like algorithm, more accurate popularity level-to-level management is carried out on cache pages; when the cache is fully loaded, pages with low access potential are effectively eliminated, so that the cache hit rate is increased; fragmented effective edge data are extracted from a cache page and are independently stored in a cache of fine-grained management; the scheduling sequence of the vertexes in the task queue is adjusted by obtaining the proportion of the edge lists of the vertexes in the BFS-like algorithm task queue residing in the cache and combining the attributes such as the out-degree of the vertexes, and the data residing in the cache is utilized to the maximum extent. According to the method, the cache hit rate of the graph processing system when the graph processing system processes the BFS-like algorithm task can be effectively improved, so that the access performance of the graph processing system is improved.

A GEMM load-oriented GPU modeling method is characterized in that through a multi-stage collaborative modeling mechanism, cache behaviors, instruction overhead and calculation intensity are deeply coupled, accurate performance prediction of GPU execution GEMM operators is realized, the method can be widely applied to scheduling optimization of GPU intensive scenes such as AI training and scientific calculation, firstly, a three-stage cache weight distribution mechanism is established, and then, a three-stage cache weight distribution mechanism is established; quantifying the contribution of the L1 / L2 cache hit rate and the DRAM bandwidth degradation factor to the effective bandwidth; secondly, an instruction-level memory access overhead correction mechanism is introduced, and the mixing precision and the real calculation strength of a sparse calculation scene are captured through dynamic parameter adjustment and optimization; then combining the calculation force peak value and the bandwidth upper limit to construct a double-boundary constraint model, and generating a theoretical performance critical value; further predicting a stream multiprocessor utilization rate based on a neural network, and quantifying efficiency loss caused by hardware resource contention through a multi-layer perceptron structure; and finally, the integration module outputs task execution time to realize end-to-end performance prediction.

The invention discloses a low-orbit satellite communication data caching and forwarding method based on edge computing, which comprises the following steps of: collecting and preprocessing communication service input data, and generating a continuous time input track set and a resource constraint set; constructing a liquid structure state space model, and generating a prediction result set; generating a penalty coefficient set and a Whittle Index priority index set based on the prediction result set and the resource constraint set; generating a cache scheduling scheme and a forwarding scheduling scheme in combination with the resource constraint set; executing the cache scheduling scheme and the forwarding scheduling scheme, and generating an online execution result set and a feedback observation sequence; and on the basis of the online execution result set and the feedback observation sequence, performing online updating on the liquid structure state space model parameter set and the penalty coefficient set. The cache hit rate and the link utilization rate are improved, and the data forwarding efficiency and the system adaptability are enhanced.

The invention relates to the technical field of database optimization, in particular to a database preloading method and device based on a hybrid architecture and hotspot prediction.The method comprises the steps that on the basis of a preset sliding window mechanism, time sequence characteristics of historical access logs are extracted to recognize high-frequency hotspot data; based on a preset cache preheating mechanism, dynamically generating a loading priority queue according to the data access frequency of the high-frequency hotspot data so as to preferentially load the key data to a memory hotspot; and based on the monitoring data of the LRU list, the flush list and the check point state, a preset self-adaptive mechanism is linked to dynamically adjust a preloading strategy, and the preloading strategy is deeply integrated with a database check point mechanism to form closed-loop control, so that the preloading of the database is realized. Therefore, the problems that frequent changes of an access mode are difficult to cope with due to the fact that a database preloading method mostly depends on a static strategy or simple statistics, and particularly when the concurrency is high and the load fluctuation is large, hot area identification lagging, resource waste, cache hit rate reduction, response delay increase and the like are easily caused are solved.