Inevitable service quality constraint guarantee method based on copy placement

Abstract

The invention provides an inevitable quality of service constraint (CQR) guarantee method based on copy placement in a data grid system. According to the method, aiming at application scenes such as stock markets and large-scale joint operation with extremely strict QoS requirements in a data grid system, a CQR replica placement algorithm based on matrix operation is provided by constructing a replica placement model and formally describing the CQR, and a replica strategy test and verification platform is designed and developed, so that the CQR replica placement algorithm based on the matrix operation is provided; according to the method, the QoS of each user request can be satisfied by 100%, the QoS satisfaction rate is improved, the delay of the data request is reduced, the robustness and reliability of the system are enhanced, and the method has wide applicability.

Description

Technical Field

[0001] The present invention relates to the technical field of data grid systems, and more specifically, to a replica placement method for inevitable quality of service (CQR) constraints in data grid systems. The method aims to ensure that the QoS (quality of service) requested by each user can be 100% met, especially in application scenarios with extremely strict QoS requirements, such as stock markets, large-scale joint operations, finance, real-time communications, data distribution, and multimedia online games. Background Art

[0002] As grid applications continue to expand, quality of service (QoS) requirements are increasing across applications. In scenarios like the stock market and large-scale joint operations, users require real-time or near-real-time access to critical information. Any delay can result in significant economic losses or operational failure. These scenarios share a common requirement: every user's requested QoS must be met 100% without exception. This is known as a necessity quality of service (CQR). However, existing data grid systems often struggle to fully meet these stringent QoS requirements. Summary of the Invention

[0003] This technical solution aims to address the replica placement problem in data grid systems with extremely stringent Quality of Service (QoS) requirements. By constructing a replica placement model, formalizing CQR (Consumer Quality Reliability), designing a CQR replica placement algorithm based on matrix operations, and building a replica strategy testing and verification platform, this solution ensures that every user's requested QoS is 100% met.

[0004] The technical solutions of the present invention are as follows:

[0005] Replica Placement Model Construction: Data servers in a data grid system are abstracted as an undirected connected graph G(V,E), where V represents the set of data server nodes and E represents the set of links connecting the nodes. The length of each link represents the communication overhead between the nodes. Furthermore, the model should include features such as allowing direct or indirect connections between any two nodes, allowing nodes to read or copy data from other nodes, and allowing nodes to modify local data contents. A source node is defined as the node containing the original data.

[0006] CQR formalization: This defines the relationship between CQR and QoS metrics, such as response time, throughput, and reliability. CQR requires that all user-requested QoS metrics must meet or exceed a predetermined peak value. It also defines the specific requirements for CQR replica placement, specifically how to place replicas to ensure that all user QoS requirements are met.

[0007] Design of CQR replica placement algorithm based on matrix operation

[0008] Initialization matrix: The rows and columns of the matrix correspond to data server nodes, and the matrix elements represent the communication overhead or data access probability between nodes.

[0009] According to the difference between the QoS index requested by the user and the QoS index that the current system can provide (i.e., the QoS request difference), the matrix elements are adjusted to reflect the priority and urgency of different users' data access.

[0010] Through iterative calculations, the replica placement strategy in the matrix is continuously optimized. In each iteration, the algorithm selects the optimal replica placement strategy based on the current matrix elements and the QoS request difference, and updates the matrix elements. This iterative process continues until the CQR of all users is met.

[0011] Replica strategy testing and verification platform construction

[0012] A replica strategy testing and verification platform was constructed, consisting of a data generation module, an algorithm execution module, a performance evaluation module, and a results presentation module. The data generation module generates test data for a simulated data grid system; the algorithm execution module executes the matrix-based CQR replica placement algorithm and outputs a replica placement strategy; the performance evaluation module evaluates the algorithm's performance metrics, such as QoS satisfaction rate, latency, and system robustness; and the results presentation module displays test results and conducts comparative analysis.

[0013] Dynamic adjustment and optimization

[0014] Replica placement strategies are dynamically adjusted and optimized based on the needs of actual application scenarios and changes in the system environment. For example, in the stock market, replicas can be prioritized on nodes close to trading centers to reduce data request latency. Furthermore, when a node fails or network congestion occurs, the algorithm automatically selects other nodes for replica placement to ensure system stability and reliability.

[0015] The present invention provides a method for ensuring the necessary quality of service constraints based on replica placement. It has the following beneficial effects:

[0016] 1. Improve QoS satisfaction rate: By accurately calculating and optimizing the replica placement strategy, we ensure that the QoS requested by each user can be 100% satisfied.

[0017] 2. Reduce latency: By optimizing the placement of replicas, the transmission distance and waiting time of data requests are reduced, thereby reducing latency.

[0018] 3. Enhance system robustness: By dynamically adjusting and optimizing the replica placement strategy to adapt to different QoS requirements and system conditions, the system's stability and reliability are enhanced. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 The percentage of replica nodes in all nodes

[0020] Figure 2 A graph showing the relationship between the number of replica nodes

[0021] Figure 3 This is the relationship between the algorithm running speed and the number of nodes DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] Example:

[0024] The following is an embodiment of a method for ensuring quality of service (CQR) based on replica placement in a specific data grid system:

[0025] 1. System Environment

[0026] Suppose there is a data grid system that consists of multiple data server nodes. Each node has a unique identifier and can communicate with each other. The data resources in the system are distributed on different nodes, and users can access these resources through the network.

[0027] 2. Building a replica placement model

[0028] The data servers in the data grid system are abstracted as an undirected connection graph G(V,E), where V is the set of data server nodes and E is the set of links between nodes.

[0029] The length of an edge represents the communication cost between node pairs, which can be measured and calculated according to actual conditions.

[0030] Any two nodes can establish a connection directly or indirectly. One node can read or copy the data of another node, and the node can also modify the local data content.

[0031] The nodes that contain the original data are called source nodes.

[0032] 3. Formal Description of CQR

[0033] Clarify the peak relationship between CQR and QoS indicators, that is, CQR requires that the QoS indicators of all user requests must reach or exceed a preset peak value.

[0034] We formalize the CQR replica placement problem, i.e., how to place replicas to ensure that all users’ QoS requests can be satisfied.

[0035] 4. Propose a CQR replica placement algorithm based on matrix operations

[0036] Initialize the matrix, where the rows and columns correspond to the data server nodes respectively, and the matrix elements represent the communication overhead or data access probability between nodes.

[0037] The matrix elements are adjusted based on the QoS request gap to reflect the priority and urgency of different users' data access. The QoS request gap refers to the difference between the QoS index requested by the user and the QoS index currently provided by the system.

[0038] The replica placement strategy in the matrix is optimized through iterative calculations. In each iteration, the algorithm selects the optimal replica placement strategy based on the current matrix elements and the QoS request difference, and updates the matrix elements. This iterative process continues until the CQR of all users is met.

[0039] 5. Design and Development of a Copy Strategy Testing and Verification Platform

[0040] Data generation module: Generates test data for a simulated data grid system, including the number of nodes, data resource distribution, user request patterns, etc.

[0041] Algorithm execution module: executes the CQR replica placement algorithm based on matrix operations and outputs the replica placement strategy.

[0042] Performance evaluation module: evaluates the performance indicators of the algorithm, such as QoS satisfaction rate, delay, system robustness, etc. These indicators can be obtained through simulation experiments or actual tests.

[0043] Result presentation module: displays test results and comparative analysis, including performance indicator comparison under different replica placement strategies and algorithm running time comparison.

[0044] VI. Experimental Verification and Comparative Analysis

[0045] Experimental verification is carried out on the replica strategy testing and verification platform to compare the performance of different replica placement strategies.

[0046] The experimental results are analyzed to verify the effectiveness and superiority of the proposed algorithm. The experimental results show that the proposed algorithm can significantly improve the QoS satisfaction rate, reduce delay, and enhance the robustness of the system.

[0047] 7. Optimization and Adjustment

[0048] Adjust and optimize the replica placement strategy based on the needs of the actual application scenario. For example, in the stock market, you can prioritize placing replicas on nodes close to the trading center to reduce the latency of data requests.

[0049] The replica placement strategy is dynamically adjusted based on changes in the system environment. For example, when a node fails or the network is congested, the algorithm can automatically select other nodes for replica placement to ensure system stability and reliability.

[0050] Through the above examples, we can clearly see the specific steps and effects of the method for ensuring the necessary quality of service (CQR) based on replica placement in data grid systems. This method not only improves QoS satisfaction and reduces latency, but also enhances the robustness and reliability of the system, and has broad application prospects.

[0051] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for ensuring quality of service (CQR) based on replica placement in a data grid system, characterized in that: The following steps are involved: a) Build a replica placement model for the data grid system. Abstract the data servers in the system as an undirected connected graph G(V,E), where V is the set of data server nodes, E is the set of links between nodes, and the length of an edge represents the communication overhead between node pairs. b) Formalize the description of CQR, clarify the peak relationship between CQR and QoS indicators, and define the formal definition of the CQR replica placement problem; c) A CQR replica placement algorithm based on matrix operations is proposed. This algorithm uses the QoS request difference as heuristic information and obtains a replica placement strategy that satisfies all users' CQRs through matrix operations. d) Design and develop a replica strategy testing and verification platform to experimentally verify and compare the performance of different replica placement strategies to ensure the effectiveness and superiority of the proposed algorithm.

2. The method according to claim 1, characterized in that The replica placement model also includes the following features: a) Any two nodes can establish a connection directly or indirectly; b) A node can read or copy the data of another node; c) Nodes can modify local data content; d) The node containing the original data is called the source node.

3. The method according to claim 1, characterized in that The matrix operation-based CQR replica placement algorithm further includes: a) Initialize the matrix. The rows and columns of the matrix correspond to data server nodes, and the matrix elements represent the communication overhead or data access probability between nodes; b) Adjust the matrix elements according to the QoS request difference to reflect the priority and urgency of data access of different users; c) Optimize the replica placement strategy in the matrix through iterative calculation until the CQR of all users is met.

4. The method according to claim 1, wherein The replica strategy testing and verification platform includes: a) Data generation module, used to generate test data for simulating data grid system; b) an algorithm execution module, configured to execute a CQR replica placement algorithm based on matrix operations; c) Performance evaluation module, used to evaluate the performance indicators of the algorithm, such as QoS satisfaction rate, delay, system robustness, etc. d) Result display module, used to display test results and comparative analysis.

5. The method according to any one of claims 1 to 4, characterized in that The method further includes: a) Adjust and optimize the replica placement strategy based on the needs of actual application scenarios to adapt to different QoS requirements and system environments.

Images