2364 results about "Byte" patented technology

To realize efficient control processing.SOLUTION: In a game machine, a CPU is capable of executing: third processing which performs first processing of writing, by the CPU, 55H into a 1-byte first general-purpose register and then writing, by the CPU, a first general-purpose register value into a 1-byte first RAM, and performs second processing of writing, by the CPU, AAH into the 1-byte first general-purpose register and then writing, by the CPU, the first general-purpose register value into a 1-byte second RAM; and sixth processing which performs, after the third processing, fourth processing of writing, by the CPU, a first RAM value into the first general-purpose register and then comparing, by the CPU, the first general-purpose register value with 55H to determine whether or not they correspond to each other, and performs fifth processing of writing, by the CPU, a second RAM value to the first general-purpose register and then comparing, by the CPU, the first general-purpose register value with AAH to determine whether or not they correspond to each other.SELECTED DRAWING: Figure 130

The invention discloses an encrypted malicious Trojan flow detection method based on a mask auto-encoder and multistage flow modeling, which comprises the following steps of: converting original bytes of encrypted malicious Trojan flow into a multistage flow modeling matrix, and extracting the arrival time interval of a data packet and the length sequence characteristics of the data packet; the matrix is segmented into small blocks which are not overlapped, each small block is mapped into a vector, and the vector is added with position coding information to serve as the input of an encoder; respectively capturing dependency relationships in the data packets and between the data packets by using a packet-level attention mechanism and a flow-level attention mechanism; fusing traffic behavior modes contained in a time sequence convolutional network explicitly modeling data packet time interval and a data packet length sequence; a self-supervision pre-training strategy based on a mask auto-encoder is adopted, a large amount of available label-free data is used for training the encoder, and a small amount of data is used for fine adjustment in downstream tasks, so that efficient encrypted malicious Trojan flow detection is realized. According to the method, different encrypted malicious Trojan traffic can be accurately detected by fully utilizing hierarchical structure characteristics of network traffic without depending on traffic statistical characteristics and a large amount of encrypted malicious Trojan traffic label data.

The invention discloses a network protocol reverse analysis method based on deep learning and a graph neural network, and the method comprises the steps: basic field detection: carrying out the byte-level feature extraction of a binary data stream through sliding window embedding, bidirectional LSTM coding and knowledge enhanced CRF decoding, and outputting a structured field labeling sequence; performing protocol format clustering based on the sequence: calculating a multi-dimensional similarity through an improved Needleman-Wunsch algorithm, realizing automatic classification of unknown protocols in combination with a dynamic density clustering algorithm optimized by LSH, and outputting a protocol cluster; and performing composite structure analysis based on the protocol cluster: constructing a protocol syntax tree based on a graph neural network, performing multiple rounds of message passing through a graph attention network GAT attention mechanism, identifying a nested structure and performing recursive analysis, and generating a multi-level protocol syntax tree. According to the method, the automation degree and accuracy of complex protocol analysis can be remarkably improved.

The invention relates to an implementation method and device for an EtherCAT master station system based on a non-operating system, and the method comprises the steps: achieving the adaptation of an SOEM protocol stack to a hardware abstraction layer of an EtherCAT master station through the byte order processing based on a bit mask and a staged shift strategy and the EtherCAT frame receiving and transmitting control based on annular buffer area index management; on the basis of non-real-time task priority scheduling processing of a hardware timer, main circulation driven by a single-thread event and overtime monitoring of a blocking communication synchronization mechanism in combination with the timer, adaptation of an SOEM protocol stack to an EtherCAT master station operating system abstraction layer is achieved; through network scanning and communication parameter configuration, a memory management mechanism based on CoE standard mapping and a periodic position mode of a slave station, adaptation of an EtherCAT master station application layer is realized. According to the invention, through optimization and adaptation of the SOEM protocol stack, operation-system-free and microsecond-level communication jitter of the EtherCAT master station system is realized.

The invention discloses an interpretable fine-grained industrial control network intrusion detection method, which comprises a model structure part and a data processing part, and is characterized in that the model structure part comprises a byte-level interpretable module, a packet-level feature extraction module, a flow-level time sequence analysis module and a result output module; the data processing part comprises a byte-level network model, a packet-level network model, a flow-level network model and a data processing process, and the data processing process comprises data preprocessing, feature extraction and final intrusion detection decision making. Compared with the prior art, the deep learning-based intrusion detection system model has the advantages that the deep learning-based intrusion detection system model is provided and is used for detecting advanced attacks in an ICS network. Through byte, data packet and flow level feature extraction and fusion, the accuracy, interpretability and efficiency of intrusion detection are improved, and the method has significant advantages especially in a complex attack scene.

The invention provides a multi-target tracking method based on a YOLOv8 model and a Byte Track algorithm, and relates to the technical field of computer vision and edge equipment. The method specifically comprises the following steps: acquiring data of a plurality of images, performing format conversion, and constructing an image data set; constructing a DC-YOLOv8 network structure, and performing training by using the image data set to obtain a target detection model based on the DC-YOLOv8 network structure; obtaining a to-be-detected video stream, extracting continuous image frames from the to-be-detected video stream, and preprocessing the extracted image frames; and inputting the preprocessed image frames into the target detection model for target detection, and performing target tracking on all detected targets by adopting a Byte Track algorithm to obtain tracking trajectories of the tracked targets in all the image frames and generate a video stream. According to the invention, target tracking can be carried out on a video with many targets more smoothly.

The invention discloses a cross-chain smart contract vulnerability detection method and system based on multi-feature fusion learning. The method comprises the following steps: collecting a cross-chain smart contract vulnerability data set for cleaning and labeling; feature extraction is carried out from the source code and the byte code, an abstract syntax tree (AST) is extracted from the cleaned source code, a basic control flow graph (CFG) is extracted from the byte code, and a cross-chain control flow graph (xCFG) is constructed; carrying out feature representation on AST and xCFG, generating a graph vector through a graph neural network (GNN), generating a semantic vector through CodeBert, and fusing the semantic vector into a feature fusion vector; performing model training and detection, taking the generated vectors as training data and test data, obtaining a cross-chain smart contract vulnerability detection model by adopting Transform-FC model training data, and finally evaluating model performance through accuracy, recall rate, precision rate and F1 value. According to the method, the structural features and semantic features of the codes can be effectively fused, potential vulnerability information in the codes can be fully mined, the recognition capability of the model for cross-chain vulnerabilities can be enhanced, and the accuracy and reliability of the cross-chain vulnerability detection model can be improved, so that the security of a block chain system can be more efficiently guaranteed.

The invention discloses an Internet of Things protocol analysis method and device based on multi-modal AI and a medium, and relates to the field of industrial Internet of Things, and the method comprises the steps: collecting a protocol byte stream of industrial equipment and associated multi-modal physical data; the protocol byte stream and the multi-modal data are analyzed through the multi-modal AI, and multi-modal features are extracted and fused; inputting the data into a pre-trained protocol grammar generation model, outputting a protocol grammar tree of a communication protocol corresponding to the industrial equipment, and performing semantic decoding on the protocol grammar tree to obtain structured protocol data; obtaining a multi-modal physical measured value of the industrial equipment, verifying the structured protocol data based on the multi-modal physical measured value, and calculating a comprehensive confidence coefficient; and calibrating the structured protocol data based on the comprehensive confidence coefficient. Various industrial protocol messages are automatically identified and analyzed through the multi-mode AI, protocol parameters do not need to be manually configured, personal errors are reduced, and the data acquisition speed and efficiency are improved.

The invention provides an intrusion prevention method, a management unit, a system and a storage medium, and relates to the field of network security, and the method comprises the steps: collecting context operation data corresponding to a to-be-tracked asset object in a kernel, and carrying out the security management of the context operation data, and obtaining a first target security control strategy corresponding to the to-be-tracked asset object; and mounting a first target BPF byte code corresponding to the first target security control strategy in a Linux security module so as to monitor and defend the target asset object through the Linux security module. Or in response to the security policy configuration request, extracting a second target security control policy from the security policy configuration request; and when the second target security control strategy is activated, mounting a second target BPF byte code corresponding to the second target security control strategy in the Linux security module. Therefore, through the above intrusion prevention method, the embodiment of the invention can immediately block attacks and improve the convenience of maintenance and deployment.

The invention relates to a ZNS SSD-based B + tree index construction method for dynamic placement of cold and hot data. A persistent memory PM, a dynamic random access memory DRAM and a ZNS SSD hard disk are included. The persistent memory PM and the dynamic random access memory DRAM are directly connected with a CPU memory bus and have the capability of being accessed by the CPU according to bytes; internal nodes of the B + tree are stored in a dynamic random access memory (DRAM), and the internal nodes only store index information used for positioning leaf nodes; the pseudo leaf nodes of the B + tree are stored in a persistent memory PM, and only the key index information of the leaf nodes is stored in the pseudo leaf nodes; leaf nodes really storing the key value pairs in the B + tree are divided into hot leaf nodes and cold leaf nodes according to the data access frequency, the hot leaf nodes are stored in a persistent memory PM, and the cold leaf nodes are stored in a ZNS SSD. According to the method, the leaf nodes are quickly positioned by using the DRAM, so that the query time is shortened; hot leaf nodes are stored in the PM, and the real-time service read-write requirement is met; and hot data is stored in a high-speed DRAM and PM, so that the random read-write delay is reduced, and the performance is excellent in scenes such as big data analysis and the like.

The invention discloses a malicious encrypted traffic decryption method and system based on memory data analysis, and the method comprises the steps: reading process activity data of a target computer; determining a target malicious process, and obtaining a target malicious process identifier; reading the memory space of the target malicious process to obtain memory data; capturing a network communication data packet of the target malicious process; determining candidate memory data; sequentially moving and extracting byte sequences; calculating an information entropy value and a hash value of each byte sequence; selecting byte sequences of which the information entropy values are greater than a preset threshold value and the hash values are not repeated in all byte sequences, and generating a key candidate set; and performing decryption operation on the network communication data packet by using each byte sequence in the key candidate set as a decryption key to obtain an effective decryption key and decrypted data content. According to the invention, decryption and analysis of malicious encrypted traffic are realized.

The invention relates to the technical field of network space security, and provides a DoH malicious tunnel traffic detection method and system based on feature fusion, and the method comprises the steps: carrying out the preprocessing of obtained to-be-detected DoH traffic data, carrying out the sequence segmentation processing, obtaining a Token sequence, and extracting features based on a byte sequence feature extractor; statistical features are extracted and standardized, features are extracted through a statistical feature extraction sub-network, and statistical feature vectors are obtained; fusing the byte sequence feature vector with the statistical feature vector to obtain a classification result; the byte sequence feature extractor and the statistical feature extraction sub-network extract features, and training is carried out by adopting a semi-supervised learning framework of dynamic pseudo-tag screening for non-tag training samples. According to the method, multi-modal features are fused, a semi-supervised learning mechanism is introduced, malicious DoH traffic generated by multiple DNS tunnel tools is effectively identified under a limited annotation data set, and the detection precision and generalization ability are improved.

The invention discloses a distribution network line facility defect detection method based on line magnetic field variable characteristics, and relates to the technical field of magnetic variable measurement. According to the method, high-precision spatio-temporal data are obtained through multi-point synchronous non-contact magnetic field sensing, codes are efficiently preprocessed through self-adaptive voxel division and bytes, and the detection accuracy of the distribution network line facility defects is improved; and the line topology information is deeply fused to Token for representation. A pre-trained large language model is combined with low-rank adaptation (LoRA), sliding window attention and an external memory bank for efficient reasoning, and understanding of long sequences and topological association is enhanced. Characterization quality is improved through mixed mask self-supervised learning, and multi-task (classification, positioning and scoring) output is optimized by adopting uncertainty weighted loss. And privacy protection federal training is supported. And finally, an interpretable diagnosis report is generated, and an alarm and work order process is automatically performed. The accuracy, efficiency, intelligent level and automation degree of distribution network fault detection are remarkably improved, and safe and reliable operation of a power grid is guaranteed.

The invention relates to the field of risk data visual tracing, and provides a visual track tracing method and system for a risk checking task, and the method comprises the steps: analyzing a heterogeneous data stream, separating a source metadata set, calculating an original structure entropy fingerprint, intercepting an end feature, embedding a global tracking identifier, and generating a packaging data package; generating transformed service data by using dynamic byte code instrumentation, constructing a runtime execution context and a differential snapshot, and transmitting an instantiated track node object according to a shunt delivery strategy; mapping and instantiating the logical topology execution graph based on the Hash fragments, and marking a pollution state and generating a total element execution link graph when the logic topology execution graph is abnormal; the method comprises the steps of generating a topology summary data packet, rendering a real-time state view, responding to physical interaction operation to construct a retrieval request data packet, reconstructing a full-amount service load through topology backtracking and reverse data evolution, and generating an attribution view in combination with static codes and rule description. According to the method, a dynamic trajectory tracing and full-link risk visualization mechanism of massive heterogeneous data is constructed.

The invention discloses a cross-language application program vulnerability mining method based on static analysis, which adopts a nested cross-programming language pointer analysis method and a micro-service cross-programming language taint tracking method to effectively solve the limitation of processing nested cross-language control flow and micro-service cross-language data flow. A nested language semantic boundary is accurately positioned by constructing a nested byte code, and a data stream is completely tracked by utilizing an interface relay technology, so that the detection precision is improved. According to the method, part of multi-end data streams and complex control streams of cross-programming language applications can be uniformly processed, and the analysis process in a cross-programming language environment is simplified; by nesting cross-programming language control flow diagram construction and double-end / multi-end data flow diagram construction, a unified analysis framework is constructed, seamless cooperation of static analysis among different languages is achieved, the analysis cost is reduced, the analysis efficiency is improved, efficient and accurate vulnerability mining is achieved in a complex cross-programming language application program, and the method is suitable for application and popularization. And the reliability of cross-language vulnerability detection is improved.

The invention provides a server-free dynamic management and control method based on an extended Burkley packet filter, and the method comprises the steps: mounting an eBPF program to a kernel cgroup subsystem, monitoring a function instance creation / destruction event in real time, extracting metadata, dynamically generating a fine-grained network strategy, compiling the fine-grained network strategy into an eBPF byte code, and injecting the eBPF byte code into a Map format strategy table, realizing data packet level allowing / discarding control by using a flow control hook; and when the instance is destroyed, the strategy table rule is automatically cleared through a cgroprease event. Through kernel-level dynamic management and control, precise security protection, zero-trust micro-isolation and kernel layer malicious behavior interception of the function instance in the full life cycle are realized, and the security efficiency and the resource utilization rate of the cloud native environment are remarkably improved.

Blockchain environments may mix-and-match different encryption, difficulty, and / or proof-of-work schemes when mining blockchain transactions. Each encryption, difficulty, and / or proof-of-work scheme may be separate, stand-alone programs, files, or third-party services. Blockchain miners may be agnostic to a particular coin's or network's encryption, difficulty, and / or proof-of-work schemes, thus allowing any blockchain miner to process or mine data in multiple blockchains. GPUs, ASICs, and other specialized processing hardware components may be deterred by forcing cache misses, cache latencies, and processor stalls. Hashing, difficulty, and / or proof-of-work schemes require less programming code, consume less storage space / usage in bytes, and execute faster. Blockchain mining schemes may further randomize byte or memory block access, further improve cryptographic security.

The invention is suitable for the technical field of file management, and provides a file analysis method and system and a vehicle, the file analysis method and system are applied to a file analysis system, the file analysis system runs at a browser end, and the file analysis system comprises the steps that binary header information of a to-be-analyzed file is analyzed according to the byte position, obtained in advance, of the binary header information in the to-be-analyzed file, and the binary header information of the to-be-analyzed file is obtained; binary index position information of the to-be-analyzed file is obtained; according to the binary index position information, reading and analyzing binary index data of the to-be-analyzed file to obtain index data, determining a target data block meeting a preset requirement according to basic information of each data block, and reading the target data block; and analyzing the data in the read target data block to obtain formatted target data. According to the invention, the problem that the cost of cloud data storage and data analysis is increased due to the fact that a large amount of file data is analyzed and stored at the cloud in the prior art can be solved.

The invention discloses an anomaly detection method and device, electronic equipment and a computer program product. The method comprises the steps that operation information generated by executing a memory operation instruction is collected through a monitoring hook inserted into a webpage assembly language module in advance, the webpage assembly language module is an executable unit written by using a webpage assembly language byte code, and the webpage assembly language byte code at least carries the memory operation instruction; and performing anomaly detection on the operation information by using a preset anomaly detection condition. According to the method and the device, the technical problem of poor accuracy of performing anomaly detection on the WASM module in the prior art is solved.

A federated byte latent transformer platform utilizing homomorphically-compressed and encrypted byte-level data. The system integrates dynamic entropy-based patching into federated learning to enable efficient, robust, privacy-preserving collaborative learning across distributed nodes. Client devices convert local data into dynamically sized patches based on entropy thresholds, encrypt these patches, and send them to a central server that processes them without decryption. The system offers improved robustness to input noise, enhanced character-level understanding, and better adaptation to low-resource languages compared to token-based approaches. It enables simultaneous scaling of both patch size and model size while maintaining fixed inference budgets, allowing efficient deployment on resource-constrained devices. These innovations address critical challenges in federated learning: efficiency, robustness to data heterogeneity, and privacy preservation.

The invention relates to a data operation method of a model, which is applied to the operation of an artificial intelligence (AI) model. In the method, based on an input tensor of an operation in an AI model during actual operation, a calculation graph corresponding to the operation in the AI model is compiled into a byte code instruction, and then the byte code instruction is interpreted and operated through a virtual machine which is pre-configured with a corresponding processing function, so that the operation in the AI model is executed. The execution of a traditional tedious compiling process is effectively avoided, and the running time of the AI model is shortened.

The invention discloses a micro burst traffic detection method, network element equipment, a computer readable storage medium and a computer program product, and relates to the technical field of communication, the method is applied to the network element equipment, the network element equipment is provided with a microsecond-level timer, and the method comprises the following steps: counting the number of forwarding message bytes of each sub-time window in a sliding time window; performing mean value calculation on the forwarding message byte numbers of all the sub-time windows in the sliding time window to obtain a sub-window byte number mean value of the sliding time window; determining a sub-window byte number threshold value of the sliding time window based on the sub-window byte number mean value; and determining the state of the sub-time window of which the forwarding message byte number is greater than the byte number threshold value of the sub-window as a flow micro-burst state. According to the invention, the size of the micro-burst flow and the corresponding time delay can be accurately detected in a fine-grained manner, and the detection precision of the micro-burst flow is effectively improved.

A neural-symbolic hybrid system for generating binary document formats directly from natural language input comprises a binary-aware hierarchical tokenizer operating across four levels (binary bytes, structural elements, semantic content, and concepts), a constraint satisfaction engine with 64 parallel processing cores for enforcing structural integrity and mathematical consistency, format-specific processors for Excel, PowerPoint, PDF, and CAD documents, and a formal verification system generating mathematical proofs of correctness. The system includes custom AI Document Generation Processor (AIDGP) silicon spanning 600 mm2 with specialized cores providing 500 TOPS processing power. Performance characteristics include 99.7% structural accuracy, 100% format compliance, 15.3 second average generation time for complex documents, and distributed capacity of 1,000,000 documents per hour. The system eliminates intermediate conversion steps while maintaining semantic preservation through hardware-accelerated constraint satisfaction and formal verification engines ensuring structural integrity, format compliance, and security through AES-256 encryption and automated regulatory compliance across 25+ international standards.

The invention relates to the technical field of data acquisition, in particular to an AOP-based user behavior data acquisition method. The method comprises the following steps: firstly, identifying user behavior related code features by statically analyzing Java source codes of an application program, and constructing a feature template library; during a compiling period, according to a template library identification target method, embedding a point burying instruction by utilizing a byte code enhancement technology, and constructing a monitoring section framework; and during running, dynamically adjusting an execution strategy according to the running state of the application program by virtue of a dynamic rule engine. Meanwhile, an annular buffer area is adopted to asynchronously process user behavior data streams, cache data are managed in a sub-generation mode, and asynchronous processing is triggered when a threshold value is exceeded. The method also constructs an adaptive thread pool model based on a hierarchical thread pool architecture, distributes tasks according to data value weights, monitors the task backlog rate of a high-priority sub-pool, adjusts the sampling frequency or acquisition dimension when the task backlog rate exceeds a threshold value, forms adaptive closed-loop control, and effectively acquires and processes user behavior data.

To achieve efficient control processing.SOLUTION: In a game machine, a CPU can execute first processing for adding the value of a first general-purpose register using a general-purpose register to the value of a pair register using two general-purpose registers, and second processing for referring to a value described in an address indicated by the pair register after the first processing. The CPU can execute third processing for writing a value stored in the ROM in a second general-purpose register using a general-purpose register and writing a value stored in a one-byte RAM in a third general-purpose register using a general-purpose register, and fourth processing for determining whether the value of the third general-purpose register is equal to or larger than the value of the second general-purpose register after the third processing.SELECTED DRAWING: Figure 201

The invention relates to an encrypted network traffic classification method based on a pre-trained large language model, and belongs to the technical field of encrypted network traffic classification. The method mainly comprises three stages: a pre-training stage: converting original encrypted network traffic data into a double-byte hexadecimal format through preprocessing, generating and optimizing a basic vocabulary by using a byte pair coding algorithm, then constructing a large language model, and obtaining a pre-training model through distributed training; in the retraining stage, the data is subjected to head byte shuffling processing, and the pre-training model is quickly retrained to improve the generalization ability. In the fine tuning stage, to-be-classified data is preprocessed to generate hexadecimal double-byte data with labels, and classification task training is performed by using the retrained model to obtain an encrypted network traffic classification fine tuning model and classification accuracy. Through the combination of pre-training and retraining and the fine tuning of the pre-training model by means of data classification, the efficient processing and accurate classification of the complex encrypted network traffic are realized.

The invention provides a control data collection method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the grouping of to-be-collected data point locations based on a point address and an address region, obtaining a plurality of point location groups, and obtaining a plurality of data collection point locations according to the point address and the reading offset of each to-be-collected data point location. Sorting the to-be-acquired data point locations in the plurality of point location groups from small to large according to the reading offsets, sequentially calculating the first communication byte number and the second communication byte number of each to-be-acquired data point location in the point location groups from front to back according to the sorting and based on the reading offsets, and partitioning the to-be-acquired data point locations into blocks, according to the method, the point location blocks are combined based on the number of block communication bytes to obtain a plurality of point location batches, a data acquisition request is sent to the controller based on the point location batches to acquire control data, and the technical problems of low communication efficiency and high delay of data acquisition for the programmable logic controller are solved through the method.

Disclosed are systems, apparatuses, methods, and computer-readable media for heterogenous network services using platform agnostic extensions. A method includes: instantiating a first service having a first data plane control point; instantiating a second service configured to access the first control point in a data plane; receiving a first packet at the first service in a network path; providing at least one of the first packet and first metadata associated with the first packet to the second service to analyze or process the first packet and the first metadata in conjunction with the first service; processing at least one of the first packet or the first metadata in the second service based on external bytecode and generating at least second metadata based on the processing; receiving second metadata to the first service; and processing the first packet or a second packet.

The invention relates to an encrypted traffic classification method fusing space, time sequence and frequency spectrum features, and belongs to the field of encrypted traffic classification and deep learning. The method comprises the following steps: analyzing a network original flow Pcap packet, segmenting the packet into different sessions according to a quintuple, and preprocessing each session: extracting first n data packets, and then extracting first m bytes and t time sequence data from each packet; utilizing byte data of the session to train a spatial feature module, and extracting spatial features of the session; training a time sequence feature module by using the time sequence data of the session, and extracting time sequence features of the session; the byte data of the session are converted into frequency spectrum information through fast Fourier transform, a frequency spectrum feature module is trained, and frequency spectrum features of the session are extracted; and fusing the extracted global spatial features, time sequence features and frequency spectrum features, and training by using multi-modal joint feature representation to realize fine classification of encrypted traffic. According to the invention, the precision and robustness of traffic classification can be improved.

The invention discloses a key value storage system indexing method oriented to an NVM-NVMe SSD (Non-Volatile Memory-Non-Volatile Memory Express Solid State Disk) hybrid architecture, which comprises the following steps of: constructing a heterogeneous storage architecture taking cold and hot data perception as a core driving mechanism, coordinating and managing two types of storage media, namely a non-volatile memory NVM and a solid state disk NVMe SSD, and realizing efficient identification, layered writing and dynamic migration of cold and hot data. According to the method, the characteristics of low delay, durability and byte addressing of the NVM are utilized, the frequency and I / O overhead of Flush and Compaction operations are reduced in a data write-in path, meanwhile, the problem of mixed storage of cold and hot data is avoided, and therefore the overall performance and storage efficiency of a system are remarkably improved. In addition, by introducing an asynchronous migration module, the method can dynamically adapt to the change of data popularity along with time evolution, and effectively support high performance and high availability of the key value system in long-term operation.