A Batch Scientific Workflow Task Scheduling Algorithm Based on Improved Genetic Algorithm
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A technology for improving genetic algorithms and task scheduling, applied in the field of batch scientific workflow task scheduling algorithms, can solve the problems of slow algorithm convergence, less attention to parallelism, high complexity, and achieve cost control and time control improvement, high efficiency The effect of task scheduling
What is AI technical title?
AI technical title is built by PatSnap AI team. It summarizes the technical point description of the patent document.
A technology for improving genetic algorithms and task scheduling, applied in the field of batch scientific workflow task scheduling algorithms, can solve the problems of slow algorithm convergence, less attention to parallelism, high complexity, and achieve cost control and time control improvement, high efficiency The effect of task scheduling
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[0024] Embodiments of the present invention are described in further detail below in conjunction with the accompanying drawings:
[0025] A batch scientific workflow task scheduling algorithm based on improved genetic algorithm, such as figure 1 shown, including the following steps:
[0026] Step A: Generate an initial population according to the integer encoding method.
[0027] In this step, in order to ensure that the distance between the initial solution and the optimal solution is not too far, the best virtual machine configuration method for each task node is generated according to the optimal virtual machine configuration method with the optimal time-cost ratio, and then the full combination method is generated. In the process of generating the initial solution, we introduce the concept of time-cost ratio, that is, the ratio of the cost of upgrading the virtual machine or increasing the number of virtual machines used and the reduction of scheduling time. When generat...
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Abstract
The invention relates to a batch-processing scientific workflow task scheduling algorithm based on an improved genetic algorithm, comprising the following steps: generating an initial population according to an integer encoding method; continuously performing cross-mutation operations of a genetic algorithm according to a fitness function value, and eliminating inferior solutions , to generate a new high-quality solution; after multiple iterations, an optimal solution is retained according to the fitness function; on the basis of not changing the critical path of the optimal solution, shrinking the number of virtual machines used in the non-critical path is performed to obtain the final solution, and the final solution output. The invention has a reasonable design, can efficiently perform task scheduling of batch scientific workflow, is beneficial to reduce task scheduling cost and task scheduling time generated in the process of batch scientific workflow task scheduling, and can be widely used in various scales Cost-oriented cloud computing task scheduling.
Description
technical field [0001] The invention belongs to the technical field of cloud computing task scheduling, in particular to a batch processing scientific workflow task scheduling algorithm based on an improved genetic algorithm. Background technique [0002] With the development of computer network technology, technology has entered the era of big data, and a large amount of data is generated every moment. Data is the driving force behind the development of new technologies, and it contains endless business opportunities, making more and more enterprises invest in the research of the data field, and cloud computing technology emerged as the development of distributed and grid computing. In the era of big data, how to effectively manage the increasing amount of data and how to extract useful information from a large amount of data, that is to say, the transformation of data from "number" to "data", has become a problem for IT companies and Research institutions urgently need to...
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