A geographical information computer cluster system and its method

Abstract

A geographical information computer cluster system and its method, which relates to the field of geographical information technology, includes a cabinet. A number of installation slots A are symmetrically opened on both sides of the cabinet. An installation slot B is provided on the right side of the cabinet. An operation master node is provided on the front side inside the cabinet. A display module is provided on the installation slot B, and the display module is communicatively connected to the operation master node. Sub-node installation components are symmetrically arranged on both sides of the mounting rack. An operation sub-node is detachably arranged on the installation slot A. A node control module is provided at the rear side of the operation master node. A power supply module and a data interaction module are respectively arranged on the rear side of the bottom wall of the cabinet. A storage expansion module is arranged inside the cabinet, which solves the problem that in the traditional cluster system, data is evenly distributed to each operation node, resulting in the situation that when the data volume increases, some operation nodes are idle while other nodes are still busy, causing resource waste and low efficiency.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, and particularly relates to a geographic information computer cluster system and a method thereof. Background Art

[0002] Geographic Information System (GIS) is a crucial spatial information management tool. Relying on the support of computer hardware and software, GIS can comprehensively collect, store, manage, calculate, analyze, and display geographic distribution data on the earth's surface (including the atmosphere). GIS plays an indispensable role in many fields such as resource management, urban planning, and land survey.

[0003] Currently, with the advent of the big data era, both the data volume level and data types processed by GIS are continuously increasing. When dealing with massive data, traditional cluster systems usually evenly distribute the data to each computing node. However, as the data volume grows, this distribution method may cause some computing nodes to be idle while other nodes are still busy, resulting in resource waste and low efficiency. Summary of the Invention

[0004] Embodiments of the present invention provide a geographic information computer cluster system and a method thereof. By setting a node control module inside the cabinet to monitor each sub-node, and the node control module dynamically adjusts the distribution of computing tasks according to the load evaluation results of each node, it solves the problem that in traditional cluster systems, data is evenly distributed to each computing node, resulting in some computing nodes being idle while other nodes are still busy after the data volume increases, causing resource waste and low efficiency.

[0005] A geographic information computer cluster system includes a cabinet. Inside the cabinet, there is an installation rack. On both sides of the cabinet, a number of installation slots A are symmetrically opened. On the right side of the cabinet, there is an installation slot B. Inside the front side of the cabinet, there is a main computing node. On the installation slot B, there is a display module, and the display module is communicatively connected to the main computing node. On both sides of the installation rack, there are symmetrically arranged sub-node installation components. On the installation slot A, a computing sub-node is detachably arranged, and the computing sub-node passes through the installation slot A and is arranged on the sub-node installation component. Behind the main computing node, there is a node control module. On the rear side of the bottom wall of the cabinet, there are respectively a power supply module and a data interaction module. The data interaction module is communicatively connected to the main computing node and the node control module respectively. The power supply module supplies power to the sub-node installation component and the data interaction module respectively, and the sub-node installation component distributes the power to the computing sub-node. Inside the cabinet, there is a storage expansion module, and the storage expansion module is communicatively connected to the main computing node.

[0006] Further, the display module can be unfolded by flipping it to the side, which is used to provide a user interface, facilitating the viewing of the load conditions of each operator node and providing interactive operations.

[0007] Further, the main operation node is used to coordinate and control the work of each node in the system, read, analyze, delete, and store input data, and handle system exceptions.

[0008] Further, the data interaction module is used to provide a standardized data interaction method to ensure communication between each node.

[0009] Further, the node control module is used to monitor the load conditions of the operator nodes and dynamically schedule and adjust task allocation, specifically including:

[0010] The node load evaluation unit is used to monitor the load conditions of the operator nodes in real time;

[0011] The task scheduling unit allocates operation tasks through a task scheduling algorithm based on the above load evaluation;

[0012] The adaptive adjustment unit resets the task scheduling unit for reallocation according to the load conditions of the operator nodes monitored in real time;

[0013] The monitoring and logging unit is used to monitor the status and task execution of the operator nodes and record necessary log information for subsequent analysis and optimization.

[0014] Further, the evaluation steps of the load evaluation unit include the following:

[0015] Data collection: Collect the operation status data of the operator nodes through monitoring tools on the cluster system or custom monitoring scripts, including CPU occupancy, memory usage, disk I / O, and network bandwidth;

[0016] Data processing: Appropriately process the original data, including data cleaning, format conversion, and aggregation;

[0017] Algorithm evaluation: Use the weighted average algorithm to evaluate the processed data;

[0018] Load status classification: Classify and rate based on the evaluation results.

[0019] Further, the calculation formula of the weighted average algorithm is: weighted average load = [(load of node A * processing capacity of node A) + (load of node B * processing capacity of node B) + (load of node C * processing capacity of node C)] / total processing capacity.

[0020] Further, the execution steps of the task scheduling unit are as follows:

[0021] Adjust the priority of the task according to the node load evaluation result;

[0022] Rearrange the execution order of the tasks according to the adjustment result of the task priority;

[0023] Allocate the adjusted tasks to each of the operator nodes for execution according to the new execution order;

[0024] Dynamically adjust the parameters and strategies of the algorithm according to the actual situation and performance requirements to optimize the effect of task scheduling.

[0025] In a second aspect, an embodiment of the present invention provides a geographic information computer cluster method, including the following steps:

[0026] Analyze the input massive data stream through the main operation node to identify the characteristics and change trends of the data, and provide a basis for subsequent data processing and task scheduling;

[0027] Real-time monitor the load conditions of each node through the node control module;

[0028] Dynamically adjust the allocation of computing tasks based on the load evaluation result, and perform reasonable scheduling according to the node load conditions, data characteristics and change trends;

[0029] Through the display unit, the system will display the log information of relevant nodes in real time;

[0030] Further process and analyze the data, and store it in the storage module of the cluster system to ensure the effective utilization and management of the data.

[0031] The beneficial effects of the above technical solutions provided by the embodiments of the present invention at least include:

[0032] The present invention is composed of a main operation node arranged inside the chassis and several sub-operation nodes arranged on the sub-node installation component. The operator nodes support hot plugging, which is convenient for adding or replacing operator nodes without shutting down the machine, improving the flexibility and maintainability of the system. At the same time, the node control module dynamically monitors the load conditions of each node and dynamically adjusts the task scheduling, improving the unit computing efficiency of each node. At the same time, the installation and arrangement of each node are compact, reducing the volume and weight of the cabinet, and making the use and handling more flexible.

[0033] Other features and advantages of the present invention will be described in the following description, and, in part, will be obvious from the description, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0034] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0035] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, but do not constitute a limitation to the present invention. In the accompanying drawings:

[0036] Figure 1 is a schematic structural diagram of a cabinet disclosed in an embodiment of the present invention;

[0037] Figure 2 is a schematic structural diagram of a geographic information computer cluster system disclosed in an embodiment of the present invention;

[0038] Figure 3 is an architecture diagram of a geographic information computer cluster system disclosed in an embodiment of the present invention;

[0039] Figure 4 is a flowchart of a geographic information computer cluster method disclosed in an embodiment of the present invention.

[0040] Reference Signs:

[0041] 10, cabinet; 101, mounting rack; 102, mounting groove A; 103, mounting groove B; 11, main computing node; 12, display module; 13, data interaction module; 14, node control module; 141, node load evaluation unit; 142, task scheduling unit; 143, adaptive adjustment unit; 144, monitoring and logging unit; 15, sub-node mounting assembly; 151, computing sub-node; 16, power supply module; 17, storage expansion module. Detailed Embodiments

[0042] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully communicated to those skilled in the art.

[0043] As Figures 1 - 3As shown in the figure, an embodiment of the present invention provides a geographical information computer cluster system, including a cabinet 10. Inside the cabinet 10, there is an installation rack 101. On both sides of the installation rack 101, sub-node installation components 15 are symmetrically arranged. A computing sub-node 151 is detachably arranged on the installation rack 101. The computing sub-node 151 supports hot plugging, which facilitates adding or replacing the computing sub-node 151 without shutting down the machine, improving the flexibility and maintainability of the system. On both sides of the cabinet 10, a number of installation slots A102 are symmetrically opened. On the right side of the cabinet 10, there is an installation slot B103. A display module 12 is arranged on the installation slot B103. The display module 12 is communicatively connected to the computing main node 11 and is used to display information such as computing results and system status. At the front side inside the cabinet 10, there is a computing main node 11. At the rear side of the bottom wall of the cabinet 10, a power supply module 16 and a data interaction module 13 are respectively arranged. The data interaction module 13 is communicatively connected to the computing main node 11 and the node control module 14 respectively. The power supply module 16 supplies power to the sub-node installation component 15 and the data interaction module 13 respectively. The sub-node installation component 15 distributes power to the computing sub-node 151, making the system structure clear and the connection between each module simple and clear. In addition, the system further includes a storage expansion module 17. The storage expansion module 17 is communicatively connected to the computing main node 11 and is used to store a large amount of geographical information data. This system consists of a main computing node plus a number of sub-computing nodes. The node control module 14 improves the computing efficiency per unit of each node. At the same time, the installation and arrangement of each node are compact, reducing the volume and weight of the cabinet 10 and making it more flexible to use and carry.

[0044] In this embodiment, the display module 12 is designed as a side-flipping and unfolding type, providing an intuitive operation interface for users, displaying the load conditions of each computing sub-node 151, enabling users to have a clear understanding of the system operation status at a glance, and allowing users to perform interactive operations to achieve quick configuration and adjustment, enhancing the convenience and practicality of the system.

[0045] In this embodiment, the computing main node 11 plays a role of coordination, control, and management in the whole system. Specifically:

[0046] Coordination and control: The computing main node 11 is responsible for coordinating the work of other nodes in the system, ensuring the stable operation of the whole system, controlling other nodes by sending messages and instructions, and monitoring and managing the status of the nodes.

[0047] Data management: The computing main node 11 is responsible for managing the data in the system, including data storage, reading, updating, and deletion. By allocating and scheduling tasks, the computing main node 11 distributes data to other nodes for processing and summarizes and integrates the processing results.

[0048] Exception handling: When an exception occurs in the system, the operation master node 11 takes corresponding measures for handling. For example, when some nodes fail or malfunction, the operation master node 11 can reassign tasks to other nodes to ensure the normal operation of the system;

[0049] Maintain the directory structure of the file system: Maintain the namespace of the file system to ensure the integrity and consistency of the file system in the cluster;

[0050] Provide storage services: Provide storage services for real file data to ensure the reliability and availability of the data.

[0051] In this embodiment, the data interaction module 13 is used to implement data interaction and communication between each node in the cluster, ensuring the smooth progress of collaborative work among nodes. To complete specific tasks, nodes need to efficiently transmit and exchange data. The data interaction module 13 provides a standardized data exchange method, enabling nodes to communicate with each other and coordinate work. This includes functions such as data transmission, data synchronization, data routing, data management, and load balancing. Through these functions, the data interaction module 13 ensures the accuracy and consistency of data, while optimizing the allocation of system resources and enhancing the performance and stability of the entire cluster system.

[0052] In this embodiment, the node control module 14 is used to monitor the load condition of the operation sub-node 151 and dynamically schedule and adjust task allocation, specifically including:

[0053] The node load evaluation unit 141 is used to monitor the load condition of the operation sub-node 151 in real time. By collecting resource usage data of each node, it accurately evaluates the node load, providing an important basis for subsequent task scheduling;

[0054] The task scheduling unit 142, based on the load data provided by the node load evaluation unit 141, allocates operation tasks through a task scheduling algorithm. The algorithm schedules according to factors such as load condition, task priority, and data dependency relationship to ensure the reasonable allocation of tasks among nodes;

[0055] The adaptive adjustment unit 143, in order to cope with changes in system load, the adaptive adjustment unit 143 will dynamically adjust task scheduling according to the load condition of the operation sub-node 151 monitored in real time. When it is found that the load of a certain node is too high, it will trigger the reallocation of tasks to ensure the overall load balance of the system;

[0056] The monitoring and logging unit 144 monitors the status and task execution of the operation sub-node 151 in real time and records detailed log information, which can be used for subsequent performance analysis, fault troubleshooting, and system optimization.

[0057] Further, the evaluation steps of the load evaluation unit are as follows:

[0058] Data collection: Collect the operating status data of the operator node 151 through the monitoring tools or custom monitoring scripts on the cluster system, including key performance indicators such as CPU occupancy, memory usage, disk I / O, and network bandwidth.

[0059] Data processing: Appropriately process the collected raw data, including data cleaning, format conversion, and aggregation, to ensure the accuracy and consistency of the data.

[0060] Algorithm evaluation: Use the weighted average algorithm to comprehensively evaluate the processed data. This algorithm takes into account the importance of various performance indicators and provides a scientific basis for load evaluation.

[0061] Load status classification: Based on the evaluation results of the weighted average algorithm, classify and rate the load status of the operator node 151 to provide decision support for subsequent task scheduling.

[0062] Through the above steps, the load evaluation unit can provide timely and accurate load information for the node control module 14 to ensure the reasonable allocation and efficient utilization of system resources.

[0063] Among them, the calculation formula of the weighted average algorithm is: Weighted average load = [(Load of node A * Processing capacity of node A) + (Load of node B * Processing capacity of node B) + (Load of node C * Processing capacity of node C) ] / Total processing capacity.

[0064] Example 1: Suppose a batch of data is assigned to three nodes: node A, node B, and node C, and each node has different processing capacities, which are 4 units, 3 units, and 2 units respectively. Now, 10 units of load need to be assigned to these three nodes to maximize the performance of the cluster.

[0065] First, calculate the weighted average load according to the processing capacity of each node:

[0066] Let the processing capacities of nodes A, B, and C be 4, 3, and 2 respectively, and the total load be 10. Substitute into the formula for calculation:

[0067] [(10 * 4) + (0 * 3) + (0 * 2) ] / (4 + 3 + 2) = 40 / 9 = 4.44

[0068] Therefore, the weighted average load is approximately 4.44 units.

[0069] Then, based on the processing capacity of each node, calculate the load that should be assigned to each node:

[0070] Load of Node A = 4 * (10 / (4 + 3 + 2)) = 4 * (10 / 9) = 4.44 units

[0071] Load of Node B = 3 * (10 / (4 + 3 + 2)) = 3 * (10 / 9) = 3.67 units

[0072] Load of Node C = 2 * (10 / (4 + 3 + 2)) = 2 * (10 / 9) = 2.22 units

[0073] According to the calculation results, Node A should be assigned a load of 4.44 units, Node B should be assigned a load of 3.67 units, and Node C should be assigned a load of 2.22 units. This can ensure that the load in the cluster is evenly distributed, thus maximizing performance.

[0074] Finally, assign the load of each node to it, and monitor and adjust. By monitoring the load situation of the nodes, problems of overloading or uneven load distribution can be discovered in a timely manner, and corresponding adjustment measures can be taken.

[0075] For example, if it is found that the load of a certain node is too high, part of the load can be transferred to other nodes to achieve load balancing.

[0076] Through the above steps, the load evaluation based on the weighted average method can help the server cluster achieve more optimized load distribution, thereby improving the performance and reliability of the cluster.

[0077] Furthermore, the execution steps of the task scheduling unit 142 are as follows:

[0078] According to the node load evaluation results, the task scheduling unit 142 will adjust the priorities of the corresponding tasks. The priority adjustment is based on the load evaluation results to ensure that high-priority tasks can obtain more resources when the load is low;

[0079] Rearrange the execution order of the tasks according to the adjusted task priorities to ensure that high-priority tasks can be processed first, thereby optimizing the overall performance of the system;

[0080] Assign the adjusted tasks to each computing sub-node 151 for execution according to the new execution order to ensure the reasonable distribution of tasks among the nodes and perform dynamic scheduling according to the load situation;

[0081] Dynamically adjusting the parameters and strategies of the algorithm according to the actual situation and performance requirements can better cope with the changes in system load and improve the efficiency and accuracy of task scheduling.

[0082] Through the above steps, the task scheduling unit 142 can dynamically adjust the priorities and execution orders of tasks according to the node load conditions, ensuring the reasonable utilization of system resources and efficient task scheduling.

[0083] The embodiment of the present invention also discloses a geographic information computer cluster method, as Figure 4 shown, including the following steps:

[0084] The operation master node 11 analyzes the input massive data stream, mainly identifying the characteristics and changing trends of the data, providing a basis for subsequent data processing and task scheduling.

[0085] The node control module 14 monitors the load conditions of each node in real time, which determines how to efficiently allocate and adjust the operation tasks.

[0086] Based on the load evaluation results, the node control module 14 dynamically adjusts the allocation of operation tasks, ensuring that the tasks can be reasonably scheduled according to the node load conditions, data characteristics, and changing trends, and making full use of system resources.

[0087] Through the display unit, the system will display the log information of relevant nodes in real time, and these logs are very important for monitoring the node status, task execution situation, and system performance optimization.

[0088] After completing the above steps, the data will undergo further processing and analysis and be stored in the storage module of the cluster system, ensuring the effective utilization and management of the data.

[0089] Through the above steps, the data can efficiently enter and flow through the geographic information computer cluster system, providing a solid foundation for subsequent data processing and analysis.

[0090] It should be understood that the specific order or hierarchy of the steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of the steps in the process can be rearranged without departing from the protection scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0091] In the foregoing detailed description, various features are combined in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. Rather, as reflected in the appended claims, the invention lies in less than all of the features of a single disclosed embodiment. Accordingly, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0092] Those skilled in the art should also understand that the various illustrative logical blocks, modules, circuits, and algorithmic steps described in connection with the embodiments herein can be implemented as electronic hardware, computer software, or combinations thereof. To clearly illustrate the interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as departing from the scope of the present disclosure.

[0093] The steps of a method or algorithm described in connection with the embodiments herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination thereof. The software modules may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may be located within an ASIC. The ASIC may be located within a user terminal. Of course, the processor and the storage medium may also exist as discrete components in a user terminal.

[0094] For a software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor, and in the latter case, it is coupled to the processor in a communication manner by various means, which are well known in the art.

[0095] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but those of ordinary skill in the art should recognize that the various embodiments can be further combined and arranged. Accordingly, the embodiments described herein are intended to cover all such changes, modifications, and variations that fall within the scope of the appended claims. In addition, with respect to the term "comprising" as used in the specification or claims, that term is inclusive in a manner similar to the term "including," as that term is interpreted when used as a transitional word in a claim. Further, any use of the term "or" in the claims or specification is to be meant "non-exclusive or."

Claims

1. A computer cluster system for geographic information, characterized in that, It includes a cabinet (10), inside which there is an installation rack (101). On both sides of the cabinet (10), a number of mounting grooves A (102) are symmetrically arranged. On the right side of the cabinet (10), there is a mounting groove B (103). Inside the front side of the cabinet (10), there is a main operation node (11). On the mounting groove B (103), there is a display module (12), and the display module (12) is communicatively connected to the main operation node (11). On both sides of the installation rack (101), there are symmetrically arranged sub-node installation components (15). On the mounting groove A (102), there is a detachable operation sub-node (151), and the operation sub-node (151) passes through the mounting groove A (102) and is arranged on the sub-node installation component (15). Behind the main operation node (11), there is a node control module (14). On the rear side of the bottom wall of the cabinet (10), there are respectively arranged a power supply module (16) and a data interaction module (13). The data interaction module (13) is communicatively connected to the main operation node (11) and the node control module (14) respectively. The power supply module (16) supplies power to the sub-node installation component (15) and the data interaction module (13) respectively, and the sub-node installation component (15) distributes the power to the operation sub-node (151). Inside the cabinet (10), there is a storage expansion module (17), and the storage expansion module (17) is communicatively connected to the main operation node (11). Among them, the data interaction module (13) is used to provide a standardized data interaction method to ensure communication between nodes. The node control module (14) is used to monitor the load conditions of the operation sub-nodes (151) and dynamically schedule and adjust task allocation, specifically including: A node load evaluation unit (141), which is used to monitor the load conditions of the operation sub-nodes (151) in real time. A task scheduling unit (142), which allocates operation tasks through a task scheduling algorithm based on the above load evaluation. An adaptive adjustment unit (143), which resets the task scheduling unit (142) for reallocation according to the load conditions of the operation sub-nodes (151) monitored in real time. A monitoring and logging unit (144), which is used to monitor the states and task execution conditions of the operation sub-nodes (151) and record log information for subsequent analysis and optimization.

2. The geographical information computer cluster system according to claim 1, characterized in that, The display module (12) can be turned over and unfolded to provide a user interface for facilitating the viewing of the load conditions of each operation sub-node (151) and providing interactive operations.

3. A geographic information computer cluster system according to claim 1, characterized in that, The main operation node (11) is used to coordinate and control the work of each node in the system, read, analyze, delete, and store input data, and handle system exceptions.

4. A geographic information computer cluster system according to claim 1, characterized in that, The evaluation steps of the load evaluation unit include the following: Data collection, collecting the operation state data of the operation sub-nodes (151) through monitoring tools on the cluster system or custom monitoring scripts, including CPU occupancy, memory usage, disk I / O, and network bandwidth. Data processing, which appropriately processes the original data, including data cleaning, format conversion, and aggregation; Algorithm evaluation, which evaluates the processed data using the weighted average algorithm; Load status classification, which classifies and rates based on the evaluation results.

5. A geographic information computer cluster system according to claim 4, characterized in that, The calculation formula of the weighted average algorithm is: weighted average load = [(load of node A * processing capacity of node A) + (load of node B * processing capacity of node B) + (load of node C * processing capacity of node C)] / total processing capacity.

6. A geographic information computer cluster system according to claim 1, characterized in that, The execution steps of the task scheduling unit (142) are as follows: Adjust the priority of the tasks according to the node load evaluation results; Rearrange the execution order of the tasks according to the adjustment results of the task priorities; Allocate the adjusted tasks to each of the operator nodes for execution according to the new execution order; Dynamically adjust the parameters and strategies of the algorithm according to the actual situation and performance requirements to optimize the effect of task scheduling.

7. A geographic information computer clustering method, which applies a geographic information computer clustering system as described in any one of claims 1-6, characterized in that, It includes the following steps: Analyze the input massive data stream through the operation master node (11) to identify the characteristics and change trends of the data, providing a basis for subsequent data processing and task scheduling; Real-time monitor the load conditions of each node through the node control module (14); Based on the load evaluation results, dynamically adjust the allocation of operation tasks and perform reasonable scheduling according to the node load conditions, data characteristics, and change trends; Through the display unit, the system will display the log information of relevant nodes in real time; Further process and analyze the data and store it in the storage module of the cluster system to ensure the effective utilization and management of the data.

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