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Biological intelligence scheduling method for simulation grid system

An intelligent scheduling and grid system technology, applied to biological models, special data processing applications, instruments, etc., can solve the problem of low degree of improvement in convergence, inability to directly use heterogeneous environments, and failure to support large-scale model scheduling of simulation systems, etc. question

Inactive Publication Date: 2011-01-12
NO 709 RES INST OF CHINA SHIPBUILDING IND CORP
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Problems solved by technology

[0009] (5) Dynamic Priority Scheduling (DPS) algorithm: Generally speaking, CPM algorithms statically determine the priority of their tasks, so they cannot be directly used in heterogeneous environments
However, the degree of improvement in the convergence of this improved algorithm is still very low.
Moreover, this method needs a good resource scheduler as support. In addition, this method does not consider the characteristics of simulation grid computing, and does not support large-scale model scheduling of simulation systems.

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  • Biological intelligence scheduling method for simulation grid system
  • Biological intelligence scheduling method for simulation grid system
  • Biological intelligence scheduling method for simulation grid system

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Embodiment Construction

[0093] The present invention is a multi-path hybrid ant colony biological intelligent scheduling algorithm. Taking multi-node three-dimensional simulation roaming application as an example, that is, there are six simulated three-dimensional models on nodes for collaborative rendering and calling. The node distribution diagram is as follows: figure 2 As shown, the list of resources on its nodes is shown in Table 1.

[0094] Table 1 Node resource distribution list

[0095]

[0096] The initialization parameters are configured as follows:

[0097] Number of iterations: nc = 200;

[0098] The number of ants in each node: m=10, the total number of ants is 6*10=60;

[0099] Informational heuristic factor: a=0.5;

[0100] Pheromone volatilization coefficient: ρ=0.7;

[0101] Pheromone intensity: Q=200;

[0102] Its specific implementation steps are as follows:

[0103] 1. Initialization: Generate a resource ratio map of 6 nodes.

[0104] 2. Calculate the ratio matrix accor...

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Abstract

The invention provides a biological intelligence scheduling method for simulation grid system, comprising the following steps of: disassembling a simulation application into a group of calculation sub-tasks with a data input and output relationship by adopting a mode of facing a role model according to the features of simulation models on different nodes and the amount of simulation application tasks and forming corresponding simulation job resource groups; generating weight ratios of task resources, adopting a tree-hierarchy role diagram to express the data dependency among tasks in a simulation grid application, and making the consumption of the scheduling path of the selected sub-task be the lowest by using a multi-path hybrid ant colony intelligent scheduling method so as to realize a high-efficient scheduling of a simulation calculation operation with high performance. The biological intelligence scheduling method for simulation grid system has the advantages of ensuring that the simulation application resources are under the working state of proper load in most of the time, bringing more benefits to the resource owner, and ensuring the completion of the simulation tasks within a shortest time without too much calculation cost.

Description

technical field [0001] The invention belongs to the technical field of modeling and simulation, and in particular relates to a biological intelligence scheduling method for applying and simulating a grid system. Background technique [0002] With the continuous expansion of the scope of simulation tasks and the continuous complexity of simulation application requirements, the simulation grid system will be the trend of future development. The advantage of the simulation grid is that it introduces grid technology to solve the problem of dynamic allocation of resources in the simulation application system for the limitations of the existing HLA simulation system. In the implementation of the existing HLA simulation system, the coupling relationship between the simulation application and the simulation model is too close, and the system lacks flexibility, resulting in a decrease in the efficiency of the entire simulation system. To achieve dynamic assignment of jobs, the simula...

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Application Information

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IPC IPC(8): G06F19/00G06N3/00
Inventor 吴沉寒岳增坤陈炜赵文婷余昀冯天昊熊志强罗勤罗玉臣胡斌
Owner NO 709 RES INST OF CHINA SHIPBUILDING IND CORP
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