Heuristic type coarse grain parallel grid task scheduling method

Abstract

The invention discloses a heuristic type coarse grain parallel grid task scheduling method which is characterized by comprising the following steps: firstly, a task submitter inputs a to-be-schemed task set, a usable computing resource set, an execution time set of tasks on the computing resource, a maximum number of iteration times, a threshold value delta and an entropy value epsilon; secondly, a task scheduler represents a scheduling problem of allocating resources to execute the tasks to be a standard minimum solving problem under task optimization and constraint conditions; thirdly, a grid task scheduling problem is solved by an iterative process of the heuristic type coarse grain parallel algorithm; and fourthly, after the algorithm is finished, the task scheduling result is output. In the way, the invention provides a new multipoint crossing method and the increasing property of a population optimal solution is maintained by an elitist strategy; during a variation stage, a directed variation method based on task immigration is adopted to prevent degeneration of the population; and the method is high in performance and better than the traditional random algorithm in computing power and convergence rate.

Description

technical field [0001] The invention relates to the field of grid computing, in particular to a heuristic coarse-grained parallel grid task scheduling method. Background technique [0002] With the development of network technology, people's work needs more and more complex and diverse resources. How to make full use of resources is a problem that needs to be studied urgently. Grid is a technology that makes full use of resources. [0003] Grid is a new type of network computing platform that emerged after the World Wide Web, and it provides users with a comprehensive infrastructure for sharing various resources including web pages. Computing grid is currently the most researched and practical grid. It is mainly aimed at cutting-edge scientific research and large-scale industry applications, and can integrate and share geographically distributed computing resources. As a technology designed for cutting-edge scientific research and large-scale applications, computing grid ca...

AI Technical Summary

Problems solved by technology

Compared with the random algorithm, the deterministic algorithm has fast search speed and high accuracy, but its universality is not strong, and it is easy to fall into the local minimum point; the random algorithm has the advantages of global search and wide adaptability, but lacks effective Local search mechanism, and slow convergence

[0007] Analyzing the advantages and disadvantages of the above two algorithms, we can see that the deterministic algorithm is not universal and not suitable for grid task scheduling, while the random algorithm lacks an effective local search mechanism

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