1511 results about "Data migration" patented technology

The invention discloses a radioactive measurement data processing method for improving uranium exploration efficiency. The method comprises the following steps: standardized data acquisition: integrating a multi-parameter module, carrying out time-space synchronous acquisition of geological geophysical environment parameters, standardizing protocol alignment data, and supporting three-dimensional modeling; a three-dimensional coupling model is used for processing interference in a sub-module mode, LiDAR-DEM is used for correcting gamma attenuation in the terrain, an optical fiber thermopermeability instrument is used for correcting daughter errors in the hydrology, signal attenuation of a borehole is compensated through a drilling fluid chart, and a space-time continuous interference field is formed; performing dynamic equilibrium coefficient inversion: constructing equilibrium coefficient isoparametric mapping, and inputting rock core, logging and geochemical data into a random forest model; the transfer learning is trained by using historical data, and dynamic inversion and updating of a new area are carried out; intelligent data processing three-dimensional visualization: self-adaptive denoising and spectral shape matching reinforcement abnormity are carried out; constructing a three-dimensional model, and carrying out transfer learning to optimize the precision; the WebGL platform integrates multi-parameter display, virtual drilling functions and high-dimensional data intuitive interpretation.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical treatment and health and the like, and discloses a data hierarchical storage method, device, equipment and medium based on access characterization. Writing the data into a matched first target storage device according to a preset device performance corresponding relation; after the data are stored, continuously monitoring subsequent access characteristics of the data, and dynamically judging whether the heat state is changed or not; and if the popularity state is changed and a specific migration condition is met, redistributing the second target storage device and completing data migration operation so as to realize automatic hierarchical storage management of cold and hot data. By acquiring the data access representation and dynamically identifying the popularity state, hierarchical migration of data with different popularity among storage devices with different performances is realized, the resource use efficiency and access performance of a storage system are improved, and the storage cost is reduced.

The invention discloses a laser processing parameter autonomous generation system and method based on digital twinning, and relates to the field of digital twinning, and the method comprises the steps: collecting and preprocessing processing data in real time through a multi-source sensor; processing and analyzing the task instruction by using a natural language, extracting a constraint condition and forming a structured demand; a processing parameter candidate set is generated through a Transform model in combination with historical data transfer learning, and virtual processing is performed by means of multi-physics field simulation; an improved non-dominated sorting genetic algorithm is adopted to dynamically optimize the parameter weight, and a global optimal parameter combination is obtained through digital twin iteration verification; and after full-process virtual processing verification and task demand comparison, parameters are adaptively corrected, and model parameters are continuously optimized according to physical and simulation data deviation after actual processing. The method has the advantages that the digital twin is used as a core, multi-source real-time data, the AI algorithm and multi-physical field simulation are fused, and autonomous generation, multi-target optimization and virtual-real closed-loop iteration of laser processing parameters are achieved.

The invention discloses a distributed time sequence library data management method supporting cold and hot data level-to-level management, and relates to the technical field of computer databases, comprising: receiving a write-in request of time sequence data, and executing preliminary write-in data aggregation and sorting; dynamically identifying data cold and hot attributes based on a multi-dimensional data cold and hot degree calculation model in combination with a write-in behavior and an access behavior of time series data; according to the cold and hot attribute recognition result, in combination with the hierarchy boundary of self-adaptive division, automatic hierarchical storage of the time series data is executed; based on a cold and hot data dynamic migration and scheduling mechanism, according to the access frequency and time decay characteristics of the time series data, dynamically adjusting the storage hierarchy of the time series data, and executing a data migration task; and constructing a hierarchical index system adaptive to different cold and hot attribute time series data, and combining query frequency dynamic identification and index structure automatic upgrading. By adopting a cold and hot data automatic identification and hierarchical storage mechanism, the overall performance and the resource utilization rate of the system are improved.

The invention provides a semantic-based migration and consistency verification method, system and device and a medium, and relates to the technical field of databases. The method comprises the following steps: performing metadata topology scanning analysis by obtaining metadata, including generating an abstract syntax tree and constructing a semantic graph; according to a metadata topology scanning analysis result, performing data mapping and conversion, including loading a YAML rule and generating corresponding type mapping and constraint conversion; data migration is carried out through primary key fragmentation parallel migration, batch writing optimization and real-time double-writing verification; through structure-data-business three-layer verification, simulation business SQL comparison and automatic difference repair, the integrity of migrated data and business compliance are ensured, the semantic gap problem in heterogeneous database migration is solved, high-precision and high-efficiency database migration is realized, the migration efficiency is improved, and the data migration efficiency is improved. The method is suitable for scenes with strict requirements on data consistency and migration efficiency, such as financial, government affair and enterprise-level data centers.

Provided in the present disclosure are a task scheduling method applicable to a large language model (LLM), and a computing device, a storage medium and a computer program product. The method comprises: when determining that a target task processing node currently executing a target task meets a resource reporting condition, a target second task scheduler sending a target running resource of the target task processing node to a first task scheduler; when determining that the target running resource meets a task migration condition, the first task scheduler determining a migration task processing node from among a plurality of task processing nodes, and sending node information of the migration task processing node to the target second task scheduler; and the target second task scheduler determining the migration task processing node on the basis of the node information, determining the target task currently executed by the target task processing node, as well as task data of the target task, and migrating the target task and the task data in batches to the migration task processing node for execution, so as to obtain a task processing result of the target task.

The invention belongs to the field of data storage, and discloses a frequency access data storage migration method and device for big data, and the method comprises the steps: obtaining a data access log, carrying out the preprocessing of the data access log, and obtaining a time window statistical matrix; carrying out timeliness attenuation weighting calculation on a plurality of data access times in the time window statistical matrix to obtain a weighted access frequency vector; obtaining a multi-dimensional feature matrix based on the weighted access frequency vector; clustering the feature vector of each time window slice in the multi-dimensional feature matrix through a Gaussian mixture model to obtain a clustering tag vector; inputting the clustering label vector into an access frequency prediction model to obtain an access frequency prediction value of the data in a future preset time step; the migration decision matrix is determined based on the access frequency predicted value and the data storage cost parameter, data migration is carried out based on the migration decision matrix, the data migration complexity can be reduced, and the access speed and the storage cost are balanced.

The invention discloses a memory data migration method and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining the access heat of a storage device accepting the access of a computing node based on a memory access task executed by a switching controller and an address mapping table, so as to determine a source device and a candidate data unit, according to the access parameter of the candidate data unit and the state parameter of the storage device, calculating a performance score after the candidate data unit is stored in the storage device so as to determine the data unit to be migrated and the target device, so that cold and hot data identification from the perspective of a computer fast interconnection memory heterogeneous system is realized; the rationality of data migration is improved on the basis of quantitative performance scores; according to the method, the to-be-migrated data unit is controlled to be migrated from the source device to the target device, and the address mapping table is updated, so that the data migration task is prevented from influencing the host work of the computing node, and a cold and hot data migration scheme without being perceived by the host in the computer fast interconnection memory heterogeneous system is realized.

The invention discloses an intelligent water and fertilizer integrated irrigation system based on an Internet of Things sensor and a large language model, and relates to the technical field of intelligent agriculture. The system obtains soil, weather and plant growth state data in real time through Internet of Things sensors, including a soil sensor, a weather sensor and a multispectral sensor, deployed in a farmland; the method comprises the following steps: based on a large language model architecture, integrating preset agricultural field knowledge base data, performing field adaptive fine tuning on model parameters by adopting a data migration technology, and constructing an intelligent irrigation control model with a dynamic decision-making function; the control model analyzes sensor data and crop growth requirements, generates a water and fertilizer supply strategy, calls an internet-of-things control interface of the water and fertilizer all-in-one machine, and automatically adjusts irrigation water quantity and proportional supply of nutrient elements such as nitrogen, phosphorus and potassium. According to the invention, Internet of Things perception and large model decision are fused, and different environments are adapted through fine adjustment, so that closed-loop precise control is realized, and the water and fertilizer utilization rate and the crop yield are remarkably improved. Actual measurement shows that the system can improve the water and fertilizer utilization rate by 30% or above, and the crop yield is increased by 15-20%.

The invention relates to the technical field of data processing, and particularly discloses an improved inter-ERP (Enterprise Resource Planning) system data migration and synchronization method, which comprises the following steps of S1, metadata acquisition and preprocessing; s2, field intelligent matching; S3, machine learning recommendation; s4, mapping confirmation and feedback; s5, converting rule configuration; s6, carrying out total migration; s7, performing increment synchronization; and S8, monitoring and alarming: counting delay, throughput and failure rate of increment synchronization in real time, and automatically sending an alarm notification when an abnormal threshold is triggered. According to the method, a complete technical system which is infinitely infinitely and covers the whole migration and synchronous life cycle is constructed from metadata acquisition to intelligent mapping, rule driving and full / incremental ETL (Extract Transform Load), and then to monitoring alarm and rollback management; the method is suitable for efficient, reliable and extensible data migration and synchronization between heterogeneous ERP systems, and provides solid support for enterprise informatization upgrading, system splitting / merging and big data analysis enabling.

The invention relates to the technical field of storage, in particular to an SSD wear leveling method and device based on dynamic data migration and a medium. The method comprises the following steps: monitoring access frequency, recording read-write times of a logic block in real time, and identifying hotspot data; tracking a wear state, counting the erasure times of the physical flash memory block, and evaluating the wear degree of the physical flash memory block; data migration decision: determining whether to migrate data based on the access frequency and the wear frequency; preferentially migrating data with high access frequency and high wear degree according to access frequency and wear program sorting; and data migration execution: migrating the hotspot data from the high-wear area to the low-wear area, and updating the mapping relation. According to the method, the data storage position can be intelligently adjusted according to the real-time data access characteristics and the abrasion state, and therefore the service life of the SSD is more efficiently prolonged.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data migration method, device, equipment and medium based on task offload, which comprises the following steps: scanning a source end directory hierarchy and a target end directory hierarchy, and generating source end file metadata and target end file metadata; files are classified into source-end unique files, target-end unique files and two-end shared files based on file paths, and the files are transmitted to a comparison processing module; generating a source end newly-added migration task, a difference coverage migration task and a target end deletion migration task; and transmitting the migration task to a migration processing module and executing the migration task to generate a migration result. According to the method, source-end unique files, difference files and target-end redundant files are processed in a classified mode, full-amount migration is disassembled into incremental tasks to be executed in a split mode, and the invalid data transmission amount is reduced; a difference coverage task is generated based on double comparison of metadata and content, repeated coverage of homonymous files is avoided, and redundant data is automatically cleaned.

The invention provides an automatic processing method for real-time observation data of an ocean station, and belongs to the technical field of ocean observation data processing. A wavelet packet decomposition multi-scale noise separation algorithm is established to distinguish environmental noise and real signals, dual-sensor redundancy configuration is combined with Bayesian inference to identify sensor drift, and a calibration coefficient is updated in real time through a recursive least square method. A one-dimensional time sequence is mapped to a high-dimensional phase space by utilizing a phase space reconstruction algorithm to realize high-precision prediction of a chaotic signal, a hierarchical data storage architecture is established, and a data migration strategy is iteratively optimized through a hierarchical correlation degree function; the technical problem that time synchronization signals are difficult to reconstruct accurately according to asynchronous sampling data of multiple sensors of an ocean station is solved.

The invention relates to the technical field of SOC chips, and discloses an efficient memory management method and system based on an SOC chip, which are used for constructing a complete topological graph and calculating access delay distribution by comprehensively analyzing an internal physical structure of the chip. According to the method, the memory access behavior of each processing core is monitored in real time, dynamic memory partitioning is executed according to the access characteristics and the physical topological structure, and the optimal access area is distributed for the processing core. And when an access hot spot is detected, triggering a data migration mechanism to copy hot spot data to a relatively close memory area. According to the method, a memory address space is recombined by adopting a topology-aware address mapping algorithm, a multi-level cache collaboration mechanism is established, and the working mode of a memory controller is dynamically adjusted according to an application type. Through load balancing monitoring and periodic memory recombination, the memory access delay is effectively reduced, the bandwidth utilization rate is improved, and the overall memory access performance of the SOC chip is optimized.

A heterogeneous storage-oriented hot and cold data management method and system, wherein hot and cold data are managed by means of the following steps: at table granularity, storing data from each table into an independent data bucket in the form of object storage, each data bucket comprising a plurality of partitions, the data being randomly distributed in each partition, and the consistency of replicas of the partitioned data being ensured by means of a distributed consensus algorithm; collecting statistics about the access frequencies of the partitions in one data bucket; when the access frequency of one partition reaches a set threshold, adding a new replica to a target storage medium by means of a membership change in the distributed consensus algorithm; and when data synchronization between the new replica and an old replica is completed, detecting the old replica. In this way, the migration of the partitioned data on different storage media is realized, and data cooling and heating are implemented. The method and system can maximize the use efficiency of high-performance storage media, ensure the access efficiency of users, and can reduce the costs of data migration and maintenance.

The invention relates to the technical field of data storage, and discloses an intelligent hierarchical storage method and system for cold and hot data of a solid state disk, and the method comprises the steps: collecting spatial position change data and vibration state data, and carrying out the preprocessing to obtain a multi-dimensional feature data set; performing quantification processing on the influence degree of the spatial position change data and the vibration state data to obtain an interference weight value; constructing a joint feature matrix based on the interference weight value and the historical access record, and predicting hotspot data distribution; if the matching degree of the hotspot data distribution and the current storage layout is lower than a preset matching degree threshold value, migrating the high-frequency access data to a low-delay area to obtain an optimized storage layout; when the access response time is larger than a preset time threshold value, log data are generated, and delay load features are extracted; and carrying out secondary adjustment on the optimized storage layout to obtain a final dynamic storage scheme. According to the method, dynamic optimization of the storage performance of the solid state disk can be realized, and delay loss caused by cold and hot data mismatching is reduced.

The invention discloses a solid state disk data storage method, device and equipment and a storage medium, and relates to the technical field of hard disk management, and the method comprises the steps: obtaining a life value corresponding to each flash memory block in a target solid state disk, and dividing the life value into a preparatory TLC region and a preparatory SLC region; based on the current space utilization rate of the target solid state disk, the data proportion of the current hot data to the cold data and the access proportion of the cold data write access times to the total write access times, determining the target partition proportion of the TLC area and the SLC area, and based on the target partition proportion, determining the current TLC area and the current SLC area; and migrating and storing hot data in the first current storage data to the current SLC area and migrating and storing cold data in the second current storage data to the current TLC area based on the data access frequencies corresponding to the first current storage data in the current TLC area and the second current storage data in the current SLC area. Therefore, automatic hierarchical storage of the hard disk data can be realized.

The invention relates to the technical field of data storage, in particular to a mass data multilayer hierarchical storage method fusing access frequency and life cycle weight, which comprises the following steps: calculating the value life cycle weight of a data object and the physical life cycle health degree of a target storage medium in parallel, and outputting a composite weight factor through a coupling function; calculating a net benefit score value based on the composite weight factor and the data real-time access frequency in combination with a migration I / O loss estimated value of cross-layer migration; when the net benefit score value breaks through a preset migration threshold value, data migration is executed, and physical storage positions are dynamically distributed on the target storage layer according to the physical life cycle health degree. According to the method, the migration threshold is dynamically adjusted through the business key factors, the core data migration responsiveness is guaranteed, and meanwhile, high-cost migration behaviors are prevented from being triggered by temporary or edge data by mistake.

The invention belongs to the field of target detection, particularly relates to a bubble detection method based on multi-modal input and an improved target detection network, and aims to solve the problems that an existing bubble detection model is sensitive to environmental parameter changes, depends on a large amount of annotated data, is poor in migration adaptability, lacks multi-modal information fusion and physical law constraints, and is poor in migration efficiency. The precision is low and the cost is high. According to the method, gas-liquid two-phase flow images and temperature, pressure and flow velocity parameters are collected, a target detection network is constructed, and multi-scale visual features are extracted. After physical parameters are coded into high-dimensional features, a comprehensive feature map is generated through dynamic weighted fusion, and a detection head is input to output a bounding box and convert the bounding box into an actual size. An ideal gas equation and fluid mechanics are adopted to construct a physical constraint loss function correction size, environment self-adaption is achieved in combination with a dynamic weight adjustment and fine adjustment mechanism, and the detection precision and physical consistency are improved. According to the method, the bubble detection precision and the environmental adaptability are improved, and the labeling cost is reduced.

The invention discloses a cross-device memory dynamic complementary utilization method based on CXL protocol interconnection and a computer system. The method comprises the following steps that an ECC module of a memory detects data in the data transmission process; when a data error is detected, the error is positioned by the ECC module, the error counting module corresponding to the position is updated, and an error report is generated; when the counting value of the error counting module reaches a set threshold value, the area corresponding to the position is marked as a failure area; evaluating the states of all memories based on the spatial dimension, the health dimension, the time delay dimension and the load dimension, and selecting a target memory according to an evaluation result of the states of the memories; distributing a new logic address for the failure area based on the error report and a selected target memory; the logic address is transmitted to a target memory through a CXL protocol, and an address mapping table is updated at the same time; and after the mapping is successful, migrating the data of the failure area to the healthy area of the target memory by the system through a CXL protocol.

The embodiment of the invention discloses a distributed data transfer method and device based on fault prediction and a medium, belongs to the technical field of data migration, and solves the problem that when a distributed system breaks down, the timeliness of task completion is seriously influenced. Comprising the following steps: performing health degree evaluation and stable operation duration prediction on nodes in a distributed system through a preset fault prediction algorithm to obtain a node fault prediction result; wherein the fault prediction result at least comprises a node in which a fault is predicted to occur, fault occurrence time and a to-be-transferred task corresponding to the node in which the fault is predicted to occur; performing priority analysis on the to-be-transferred tasks based on the time sequence diagram neural network to obtain task priorities; determining a target node based on the multi-dimensional features corresponding to the nodes and the association relationship between the nodes; and transferring the task to be transferred to a target node through a preset multi-stage progressive transfer strategy according to the node fault prediction result and the task priority.

The invention relates to the technical field of cloud storage, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a load and message queue regulation-based migration method, device, equipment and medium. Monitoring message backlog amount and consumption rate to generate message queue state information, generating migration regulation and control parameters based on the two types of state information, determining concurrency level and issuing rhythm by combining object size classification information, reducing issuing frequency and preferentially scheduling small objects when the message backlog amount is increased and oversize objects exist, and distributing the tasks to the working nodes according to the concurrency level and the issuing rhythm to execute migration, and adjusting migration regulation and control parameters based on execution state updating information. According to the method, concurrency and rhythm are regulated and controlled in real time, object size classification is combined, the problems that traffic is uncontrollable and throughput is affected by large files are solved, and efficient and stable data migration is achieved.

The embodiment of the invention provides a data migration method, an access processing method, a data migration system and electronic equipment. The data migration method is applied to a switch, the switch communicates with a plurality of memory devices in a memory pool based on an interconnection protocol, and each memory device is provided with a plurality of memory areas. When data migration needs to be carried out on a first memory area in a memory pool, a switch sequentially migrates a plurality of data pages obtained by dividing data in the first memory area into available second memory areas according to pages, and in the migration process, a page bitmap determined for the first memory region is utilized to record migration states of the plurality of data pages. By adopting the scheme, the problem of large-area fault of the server caused by the memory problem can be effectively solved, the page-level granularity is adopted to migrate the data in the first memory region page by page in the migration process, the access of the server to the first memory region is continuously processed in the migration process, and the influence on the performance of the server is reduced.

The embodiment of the invention provides a task scheduling method suitable for a large language model (LLM), a computing device, a storage medium and a computer program product. A target second task scheduler of the method determines that a target task processing node currently executing a target task meets a resource reporting condition; sending the target operation resource of the target task processing node to a first task scheduler; under the condition that the first task scheduler determines that the target running resource meets the task migration condition, a migration task processing node is determined from a plurality of task processing nodes, and node information of the migration task processing node is sent to a target second task scheduler; and the target second task scheduler determines a migration task processing node according to the node information, determines a target task currently executed by the target task processing node and task data of the target task, and migrates the target task and the task data to the migration task processing node in batches for execution so as to obtain a task processing result of the target task.

The invention discloses a load balancing optimization method and device, electronic equipment and a storage medium, and relates to the technical field of load balancing optimization, and the method comprises the steps: carrying out the differentiation processing of a data migration task according to a file storage threshold value, and combining a migration performance index and server resource occupation information collected in real time, the task allocation strategy is adjusted through the dynamic task allocation algorithm, so that the reasonable allocation of the files with different sizes and the dynamic load optimization in the migration process are realized. The technical problems that in the prior art, server overload and resource idling coexist due to a single migration server or a static task allocation strategy, high delay and data inconsistency risks are generated due to network congestion and uneven task allocation in the migration process, and the overall performance and reliability of a system are affected can be solved. The technical effects of optimizing the load balance degree, reducing overload and idle conditions of the server, reducing migration delay, ensuring data consistency and improving the overall performance and reliability of the system are achieved.

The embodiment of the invention provides a model reasoning cache management method based on software and hardware collaboration, which comprises the following steps: a user side sends a model access request, executes reasoning calculation, and stores a newly generated key value cache in a video memory cache layer; in the video memory cache layer, updating the access popularity corresponding to each unit-level key value cache, and sorting according to the access popularity from high to low to form a first popularity sequence; and updating the hierarchical access popularity corresponding to each hierarchical key value cache, and sorting according to the hierarchical access popularity from high to low to form a second popularity sequence. And when the space utilization rate of the video memory cache layer reaches a set threshold value, determining and evicting a unit-level key value cache and / or a hierarchical key value cache to be evicted based on the first heat sequence and / or the second heat sequence. The cross-layer data migration efficiency is optimized by utilizing the characteristics of a multi-level memory and through mechanisms such as cache elimination prediction and the like, and efficient utilization of a video memory, reduction of reasoning delay and improvement of the throughput capacity of a system are realized.

The invention relates to the technical field of data storage, in particular to a distributed data storage and access processing method. The method comprises the following steps: presetting a guide configuration file in a distributed storage cluster to carry out identity authentication and communication link establishment on each storage node and a cluster management center so as to generate a security data interaction channel; the data sending end equipment sends a data storage request to the cluster management center through the secure data interaction channel and issues data fragments to proper storage nodes; the data receiving end equipment sends a data access request to the cluster management center through the security data interaction channel, generates an access path and feeds back the access path to the data receiving end equipment; the corresponding resource use condition in each storage node is monitored in real time, and when a preset threshold value is reached, the resource is reallocated to other storage nodes according to a preset data migration strategy and a load balancing algorithm based on a resource use insufficiency result. According to the invention, efficient storage and rapid access of data in a distributed environment can be realized.

The invention relates to the technical field of computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a back-end system creativity transformation method and device, electronic equipment and a storage medium. The method comprises the following steps: deploying a credential back-end system environment by using a credential server and credential middleware, and physically isolating credential traffic and non-credential traffic; carrying out dynamic segmentation scheduling on the traffic of the credential and credential gateway in the credential and credential environment and the traffic of the non-credential and credential gateway in the non-credential and credential environment; deploying a domestic database, and finishing data migration from the original database to the domestic database through an automatic tool chain; segmenting the traffic to a credential and credential gateway in the credential and credential environment for gray verification, and switching a back-end service chain of the credential and credential environment to a domestic database after the verification is passed; and monitoring the operation performance data of the credential environment in real time, and switching the traffic to the non-credential environment and the original database through the global traffic management tool if an exception occurs. According to the method, the risk controllability and smooth transition of creative transformation are realized.

The invention belongs to the technical field of artificial intelligence chip and robot control, and relates to a heterogeneous calculation and control fusion chip control system and method, a robot and a storage medium. A GPU, an NPU, a BPU and an MCU are integrated in a central large core cluster, a communication network subunit and a memory control subunit are matched, and a low-delay data channel is constructed in a chip, so that perception, cognition and control instructions can be directly interacted, the cross-chip communication delay is reduced, and the real-time performance is improved. The three-level cache module is accessed through the memory control subunit, high-bandwidth sharing and centralized scheduling are achieved, data migration is reduced, the bandwidth bottleneck is relieved, and the complex task processing efficiency is improved. A trust root subunit is arranged in each unit, chip-level trusted verification and access isolation are provided, and the reliability under key action control and security sensitive scenes is enhanced.

Methods, systems, and devices for data management are described. A data management system (DMS) may receive a request to migrate data from a source data storage entity to a target data storage entity managed by the DMS. The DMS may verify that the data from the source data storage entity is compatible with one or more constraints associated with the target data storage entity and may identify one or more properties of the source data storage entity. The DMS may create the target data storage entity in accordance with the one or more constraints and properties of the source data storage entity. The DMS may provide a user with access to the target data storage entity, and a migration tool may be used to migrate the data to the target data storage entity. On completion, the DMS may commit the migration and activate the target storage entity.