542 results about "Cache hit rate" patented technology

The invention relates to the technical field of data storage and processing, in particular to an RAID card static cache management method and device based on data popularity, and the method comprises the steps that the physical position of a data block needing to be accessed in an SSD array is acquired according to a read-write request; historical data access information of the RAID card is collected, a historical data set is generated, and a neural network model is trained by using the historical data set to obtain a cache management model; predicting and outputting data blocks which are possibly accessed in the future and the access probability of the data blocks through the cache management model; the RAID controller dynamically adjusts a cache strategy in combination with an LRU strategy according to a prediction result output by the cache management model and a read-write request of a file system, and optimizes a storage position and an updating mechanism of a data block in a cache space; and when the hit rate or the data access efficiency does not reach the preset value, adjusting the parameters of the cache strategy and updating the cache strategy. According to the method, cache resources can be more effectively distributed, the cache hit rate is improved, the cache replacement overhead is reduced, and therefore the performance of a storage system is optimized.

The invention provides a cache information processing method and device of a port platform and a medium, and belongs to the technical field of port distributed information processing. An update request is received, and authentication and flow limiting processing are performed on the request; obtaining a cache key prefix and a data structure type, routing a cache key to a fragment node based on a consumption write-back event, executing a write-in operation in a distributed cache service, and setting survival time and multi-copy synchronization; delivering the message body to the message middleware and obtaining delivery confirmation; the messages are pulled from the queue according to the configured concurrency, deduplication verification is carried out on idempotent identifiers of the messages, and data persistence operation is executed after the verification message version is higher than the current version of the database; index collection points are implanted into all modules of the whole link, and the request delay, the cache hit rate and the message accumulation depth index are visually displayed. The reasonable distribution and optimization of system resources are realized; the consistency and integrity of the data are guaranteed, and the reliability of message transmission is improved at the same time.

The invention discloses a hybrid expert model reasoning method based on cooperation of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and belongs to the field of deep learning. According to the method, a CPU-GPU computing framework of a hybrid expert model is constructed, heterogeneous computing resource loads are effectively balanced, and the hardware utilization rate is remarkably increased; an intelligent cache management mechanism based on dynamic priority scores is provided, high-demand experts are reserved preferentially, and the transmission overhead caused by cache missing is reduced; through pipeline parallel design for separating calculation and transmission tasks, CPU calculation and PCIe transmission are overlapped in the GPU execution period, and delay is effectively hidden. In addition, in combination with a multi-layer expert activation prediction prospective prefetching mechanism, the expert cache hit rate is improved. The method is compatible with hybrid expert models of different scales and structures, and stable and efficient reasoning acceleration is realized on a resource-limited heterogeneous platform.

The embodiment of the invention provides a data processing method, end side equipment, a storage medium, a chip system and a computer program product, and relates to the technical field of terminals. Due to the fact that the second expert modules in the first storage are formed in the mode that the second thread executes the preloading process based on the expert preference strategy of the first request under the condition that the first thread executes the prefilling stage process through the first model, parallel execution of the preloading process and the prefilling stage process is achieved, I / O delay is reduced, and the processing efficiency is improved. And the reasoning efficiency of the first model is improved. And because the first request has expert preference, and the plurality of second expert modules stored in the first memory are determined according to the first request, the first thread performs the decoding stage flow by using the first model, and the second thread performs the decoding stage flow by using the second model. The first thread can directly obtain the second expert module corresponding to the first expert module from the first storage, so that the cache hit rate is improved, and the reasoning efficiency of the first model is improved.

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 discloses a performance test and verification method, system and device for a multi-core heterogeneous chip and a medium, and the method comprises the steps: configuring a test parameter set according to a target application scene, the test parameter set comprising a processor core type recognition parameter, a communication topology weight parameter and a load balancing threshold parameter; generating a test case covering the heterogeneous computing core cooperative working mode; the method comprises the following steps: acquiring characteristic data during testing through a reconfigurable monitoring sub-module, wherein the characteristic data comprises inter-core communication delay information, shared cache hit rate information and power consumption distribution thermodynamic information; and performing quantitative analysis on the feature data through a multi-dimensional evaluation model to obtain a performance deviation index and an architecture optimization suggestion parameter. By optimizing the chip performance verification process under the multi-core heterogeneous architecture, the verification precision is effectively improved, and the chip performance verification method can be widely applied to the technical field of chip design.

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 discloses a cache management method and device for memory access, equipment and a storage medium, and relates to the technical field of storage, and the method comprises the following steps: obtaining a data read-write message of a central processing unit; detecting a data popularity type of an access address of the data read-write message; searching cache data corresponding to the access address from a target cache region; if the cache data is not found in the target cache region, sending a corresponding read-write request to a memory so as to find memory data corresponding to the access address from the memory; by setting the preset cache regions corresponding to the cold data and the hot data respectively, the cache can be partitioned according to the heat of the data, so that cache resources are reasonably allocated, the cost is reduced, the memory access efficiency and speed are improved, finer consistency control can be realized, and the cache hit rate and the system performance are improved; therefore, the required data is searched in the corresponding preset cache region according to the heat of the data, and the memory access speed is increased.

The invention discloses an online monitoring system and method based on the use state of monitoring equipment, and particularly relates to the technical field of equipment monitoring. According to the method, whether the recovery performance of the RAID level is abnormal or not is judged by writing and reading data in an RAID array, the performance of a storage server is evaluated and divided according to the cache hit rate fluctuation condition of a read-write request in a cache in a storage system and the capacity of a storage device for processing an uncompleted I / O request, and a data writing strategy is optimized; when the system runs under a high load, video data are distributed to a plurality of storage nodes by configuring the storage system to realize load balancing and dynamically store the number of the nodes, so that the performance and the reliability of the storage system are remarkably improved, the load of single storage equipment is reduced, the service life of the equipment is prolonged, and the write-in delay and the data loss are reduced; the stable operation of the system under high load is ensured, and the maintenance cost and the downtime are reduced.

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 relates to the technical field of data processing, and particularly discloses an intelligent cache replacement method and system based on application awareness, and the method comprises the steps: sensing a cache state of an application scene, and determining a candidate cache replacement strategy according to a cache replacement strategy matching rule; the method comprises the following steps: collecting data of a storage node data access mode, a cache access state, a capacity state and a power consumption state of an application scene, and preprocessing the data to generate historical data; according to historical data, training a GRU model to predict future cache hit information, and determining a cache hit rate of the application scene; calculating strategy benefits according to the cache hit rate and the evaluation index of the cache hit rate, and comparing the candidate cache replacement strategy with the current cache replacement strategy; and when the cache hit rate is lower than a preset threshold value and a strategy benefit comparison result exceeds a set value, configuring the candidate cache replacement strategy as a current cache replacement strategy.

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 relates to an edge service cache optimization method and device, computer equipment and a storage medium. The method comprises the following steps: modeling an edge service cache optimization problem into an MDP model, and collecting state information of a current environment through an intelligent agent at each time step; based on the state information of the current environment, the intelligent agent adopts a depth deterministic strategy gradient algorithm to optimize a cache strategy, and generates a cache strategy through a strategy network; executing the caching strategy, calculating a service access delay after executing the caching strategy through a value network, and calculating a reward function according to the service access delay; and evaluating the execution effect of the cache strategy according to the reward function, and optimizing the strategy network to generate an optimal target network. According to the embodiment of the invention, through intelligent agent learning and cache strategy optimization, the storage resources of the edge server can be utilized more effectively, so that the cache hit rate, the resource utilization efficiency and the service quality are improved.

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 discloses a content caching and replacing method and system based on context awareness, and relates to the field of content caching, and the method comprises the steps: obtaining context information, constructing a context feature vector, constructing a training sample, training an XGBoost regression model, and carrying out the prediction, thereby obtaining the future content access popularity; a multi-objective optimization function and constraint conditions are designed, an NSGA-II multi-objective evolutionary algorithm is used for solving, a comprehensive scoring function is defined, and a solution with the maximum comprehensive scoring function value is selected from an obtained solution set to serve as a final caching strategy; and designing a cache scoring function, calculating to obtain a cache score, and replacing the cache content by adopting a greedy strategy. According to the content caching and replacing method, the caching hit rate and the resource utilization efficiency can be improved, the service capability and the response efficiency of the edge node in a complex environment can be further enhanced, and therefore a more intelligent and more efficient content caching and replacing strategy is achieved in a dynamic network scene.

According to the database cache management method and system based on deep learning provided by the invention, the situation of cache miss is effectively reduced by predicting and loading data which is possibly accessed in the future into the cache in advance. In the model training process, various characteristics of data are fully considered, such as time characteristics, historical access frequency characteristics and relevance characteristics between tables, so that the prediction accuracy is improved. An improved long short-term memory (LSTM) network is adopted, and associated information between database tables is introduced, so that the prediction of a data access mode is more accurate, and the cache hit rate and the system response speed are remarkably improved. Through intelligent analysis and efficient prediction, resource utilization is optimized, access delay is reduced, the overall system performance is improved, and a more intelligent cache management strategy is provided for a complex and changeable database access mode.

The invention discloses an embedded image decoding display system based on an ARM, and relates to the technical field of computers, and the system comprises a dynamic sensing module which is used for monitoring the CPU occupancy rate, the cache hit rate and the memory bandwidth utilization rate of an ARM processor in real time, establishing a three-dimensional resource vector containing a dynamic weight coefficient, and calculating a threshold boundary; the adaptive decoding engine comprises an optimization unit which is used for reconstructing a bit stream processing channel and a register data exchange mechanism based on an SIMD expansion instruction of an ARMv8.2 instruction set; the prediction unit is used for predicting instruction-level hotspot distribution of the decoding task by adopting a TinyLSTM model and generating an instruction transmitting strategy; the hardware optimization module is used for executing a heterogeneous collaboration strategy of the NEON coprocessor and the MaliGPU, and the heterogeneous collaboration strategy comprises the following steps: dividing DCT / IDCT (discrete cosine transform / inverse discrete cosine transform) calculation granularity; determining a mixing precision conversion assembly line of the GPU shader according to the color gamut of the display equipment; and the display driving module is used for implementing a decoding parameter dynamic confusion algorithm in the security domain and forming a tamper-proof closed loop with the optimization unit.

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 provides a task scheduling method, which is applied to the technical field of artificial intelligence (AI), and comprises the following steps: obtaining a computational graph, the computational graph comprises a plurality of operators, each of the plurality of operators represents a task, according to the dependency relationship of the plurality of operators in the computational graph, sorting the plurality of operators to obtain an ordered operator sequence, the ordered operator sequence comprises a first operator and a second operator depending on the first operator, the first operator is arranged in front of the first operator, the plurality of operators are grouped according to the ordered operator sequence to obtain a plurality of operator groups, and the plurality of operator groups are sequentially scheduled to equipment according to the priority of the plurality of operator groups. According to the method, the sorted operators are grouped, and task issuing is performed by taking the operator groups as the granularity, so that effective scheduling under limited concurrency of the computational graph is realized, reduction of memory reuse probability and reduction of cache hit rate caused by overlarge concurrency are avoided, and the computational efficiency and performance of the AI model 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 heterogeneous sharing dynamic adjustment cache method, equipment and a medium, belongs to the technical field of computers, and aims to solve the technical problem of how to fully consider the characteristics of a CPU application program and a GPGPU application program and reasonably divide the cache capacity so as to relieve the competition of the CPU application program and the GPGPU application program for a memory and improve the cache hit rate. According to the technical scheme, a second-level cache receives a cache request from a first-level cache, an LRU replacement strategy or an LFU replacement strategy is used, and an initialization proportion is distributed to the second-level cache; the memory access information of the GPGPU is monitored in the GPGPU source program through a GPGPU memory access counter and a GPGPU memory access missing counter at a self-defined time interval; and calculating the memory access missing rate of the GPGPU in the corresponding time interval according to the memory access counting information and the memory access missing information collected in the continuous time interval.

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 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 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 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 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 relates to an optimization method for mobile edge cache based on federated learning, and belongs to the field of Internet of things and artificial intelligence. According to the method, the situation that the user mobility and the content popularity change continuously in the range of a single base station is considered, and the cache hit rate is increased by predicting the content popularity and placing the request content in an edge cache in advance. The method specifically comprises the steps of obtaining a user moment trajectory table to simulate a moving path of a user by using an RWP random path point model, selecting the user participating in FL local training in a clustering, and threshold value combination mode in consideration of local training consumption, performing global model aggregation by using an attention mechanism to control model weight, and performing global prediction according to an obtained global prediction model. The predicted request content is cached to the server in advance to improve the cache hit rate. According to the method, a federated learning method is utilized; user selection and weight aggregation are optimized; and the effective federated learning method is implemented, so that the local training consumption is reduced, and the cache hit rate is increased.

A Collaborative Caching Framework for Multi-edge Systems with Robust Federated Deep Learning is provided. First, we design a new partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a VQ-VAE-based accurate prediction for content popularity by overcoming posterior collapse. Finally, we create a new training mode and proactive cache replacement strategy based on robust federated deep learning. Specifically, residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using real-world testbed and MovieLens datasets, extensive experiments verify that RoCoCache achieves higher cache hit rates and efficiency than benchmark methods while ensuring better robustness. Moreover, we demonstrate the effectiveness of the components designed in RoCoCache via ablation experiments.