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A distributed computing task scheduling algorithm based on Bayesian network

A Bayesian network and distributed computing technology, applied in computing, computer components, program control design, etc., can solve problems such as high time complexity and cumbersome operation, achieve low time complexity, solve cumbersome operation, and widely The effect of learning and simulation

Inactive Publication Date: 2019-02-01
HARBIN UNIV OF SCI & TECH
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Problems solved by technology

[0005] In order to solve the problems of cumbersome operation and high time complexity in the traditional scheduling algorithm, a distributed computing task scheduling algorithm based on Bayesian network is designed

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  • A distributed computing task scheduling algorithm based on Bayesian network
  • A distributed computing task scheduling algorithm based on Bayesian network
  • A distributed computing task scheduling algorithm based on Bayesian network

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Embodiment Construction

[0035] A distributed computing task scheduling algorithm based on Bayesian network, its steps are:

[0036] Step 1: Use the HEFT scheduling algorithm to schedule the randomly generated directed acyclic graph, and obtain the result that all subtasks of each directed acyclic graph are assigned different CPU numbers, and all subtasks of each directed acyclic graph The result of assigning different CPU numbers to tasks is the scheduling result of the HEFT scheduling algorithm. as attached figure 2 with 3 As shown in , it is a DAG graph of tasks to be scheduled in a distributed computing and their processing times on different CPUs. There are 10 tasks in the figure, which can be processed on 3 different CPUs, and each task has different attributes; figure 2 The beginning and end of the middle arrows represent the sequence required for processing the tasks, and the weights on the sides of the connecting arrows between different tasks represent the migration time required to swi...

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Abstract

The invention relates to a distributed computing task scheduling algorithm based on a Bayesian network, which utilizes a HEFT algorithm to schedule a directed acyclic graph to obtain a scheduling result and further constructs a data set D1; Bayesian network model is used to calculate the probability of each subtask of data set D1 on different CPU, and data set D2 is constructed; calculating a priori probability that each task in the data set D2 is scheduled to a different CPU; calculating the conditional probabilities of all subtasks of the directed acyclic graph to be scheduled on different CPUs; the Bayesian network model is used to predict the scheduling results of all the subtasks in the directed acyclic graph, output Gantt chart and complete the task scheduling. The invention realizesthe simulation of the HEFT algorithm, has universal adaptability, and solves the problems of tedious operation and high time complexity of the traditional algorithm.

Description

1. Technical field [0001] The invention relates to the technical field of distributed computing task scheduling, and is a distributed computing task scheduling algorithm based on a Bayesian network. 2. Background technology [0002] With the development of computing technology, some applications require huge computing power to complete. If centralized computing is used, it will take a long time to complete. Distributed computing breaks down the application into many small parts, which are distributed to multiple computers for processing. This can save the overall calculation time and greatly improve the calculation efficiency. Distributed computing is to use a high-speed network to link many different but internally related resources together. It can provide users with powerful parallel computing and task distribution capabilities. Among them, an important index to measure the capability of distributed computing is the scheduling efficiency of tasks during distributed comp...

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Application Information

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IPC IPC(8): G06F9/48G06K9/62G06F16/901
CPCG06F9/4806G06F18/29
Inventor 辛宇王亚迪
Owner HARBIN UNIV OF SCI & TECH
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