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A virtual asset data storage method based on load balancing

A virtual asset and data storage technology, applied in the Internet field, can solve problems such as load balancing, machine scalability defects, and failure to consider system dynamic load balancing

Active Publication Date: 2019-10-15
NAT UNIV OF DEFENSE TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] However, the method in Document 2 will lead to serious defects in machine scalability when dealing with data distribution. When machines are added or deleted, most of the data still needs to be remapped, and the amount of calculation is very large.
The method in Document 3 can reduce the amount of calculation very well, but it does not consider the differences between machines, and excessive averaging may cause system delays. The above two methods do not take into account the dynamic load balancing of the system
The method in Document 4 can basically meet the data storage requirements of virtual assets, but it cannot solve the load balancing problem during system operation

Method used

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  • A virtual asset data storage method based on load balancing
  • A virtual asset data storage method based on load balancing
  • A virtual asset data storage method based on load balancing

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0096] The following simulation experiments are used to illustrate the load balancing effect of the data redistribution algorithm. The simulation experiment sets up three simulation systems A, B and C. Simulation system A does not use the data redistribution algorithm, while simulation systems B and C use it. The load threshold of simulation system B is set to 0.4, and that of simulation system C is set to 0.5. Do the same for the three simulated systems respectively. Simulating the actual situation, the operation is divided into two types: data and nodes. The operation of data is to randomly increase or decrease 100-200 records, and the operation of nodes is to randomly increase or delete 1-2 nodes. The operations on nodes and data are interspersed for a total of 100 times, and the ratio of the two operations is 9:1. image 3 is the comparison between simulated system A and simulated system B, Figure 4 It is a comparison between simulation system B and simulation system C....

Embodiment approach

[0098] In order to simplify the calculation and explain the implementation plan more clearly, the following assumptions are made first:

[0099] Assume that the position on the hash ring ranges from 0 to 20;

[0100] Assume that system D has three nodes N1, N2, and N3 at this time, and the node position table is shown in Table 1;

[0101] Assume that system D has 10 data objects data1, data2, .....data10 at this time; their distribution on the hash ring is as follows Figure 5 shown;

[0102] Assume that the resource r required by each data object is the same;

[0103] Assume that the processing capacity l of each node is the same;

[0104] Suppose the load standard deviation threshold of the system D is set to 0.5.

[0105] Table 2 Node location table of systems N1, N2, N3

[0106] node position N1 10 N2 16 N3 20

[0107] According to the mapping rules, the node stores the data object results as follows:

[0108] Table 3 Storage result tab...

Embodiment 3

[0124] The hardware operating environment of the present invention consists of a server and a client. The server runs on an X86 platform equipped with a Linux operating system, a plurality of machine nodes of JDK1.7 or above, and the client runs on an ordinary personal PC. Taking 5 node servers as an example, the hardware operating environment is as follows Figure 6 As shown, the specific parameters are as follows:

[0125] Server side: consists of 5 node servers;

[0126] The five node servers are configured as follows:

[0127] CPU: Intel Xeon E3-1231v3, 4 cores, 3.4GHz

[0128] Memory: 16G

[0129] Hard disk: 1T*2

[0130] Client: Consists of ordinary personal PCs.

[0131]Compared with the existing technology, this patent application considers the performance of the machine, predicts the load required by the data, and applies the related processing distribution method to the storage of massive information of virtual assets, and the migration of data only occurs betwe...

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Abstract

The invention discloses a virtual asset data storage method based on load balancing, which mainly includes data initial storage journey design, data storage and data redistribution. After data storage, whether to redistribute system data is determined according to system load conditions. Data redistribution includes setting the system load standard deviation threshold and calculating the current system load standard deviation, comparing the size of the aforementioned two, performing data redistribution algorithms and data reading and other steps. The invention considers the performance of the machine, predicts the load required by the data, and applies the related processing distribution method to the storage of massive information of virtual assets. The migration of data only occurs between adjacent nodes. Under the circumstances, the system can achieve dynamic load balancing, which is suitable for data storage in massive multi-structure virtual asset data management systems.

Description

technical field [0001] The invention belongs to the technical field of the Internet, and in particular relates to a method for storing virtual asset data based on load balancing. Background technique [0002] Virtual assets refer to items that are competitive, persistent and can be exchanged or bought and sold in the online world, including online banking, online account numbers, online game equipment and weapons, virtual currency, etc. [0003] At present, my country has carried out research on eID-based virtual asset management and preservation technology in cyberspace to achieve standardized and unified management of virtual assets. Massive multi-structure virtual asset data has a large amount of data and is growing rapidly. How to efficiently distribute and store it is an urgent problem to be solved. Whether the system nodes are load-balanced, data loading efficiency, and data query efficiency are all measures of a distributed An important indicator of system performanc...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L29/08
CPCH04L67/1001
Inventor 傅翔贾焰韩伟红李树栋李爱平周斌杨树强黄九鸣朱伟辉李虎全拥邓璐刘斐
Owner NAT UNIV OF DEFENSE TECH