Cloud Workflow Scheduling Optimization Method Based on Hierarchical and Load Balancing Genetic Algorithm

Abstract

The invention discloses a cloud workflow scheduling optimization method based on a hierarchical and load balancing genetic algorithm, comprising the following steps: obtaining information required for scheduling optimization; calculating the hierarchical value of a task; initializing a contemporary population; Population, perform mutation operation on the new population, decode and calculate the fitness value and improve it by means of hierarchy and load balancing, select N different individuals from the contemporary and new populations to form a new contemporary population, until the evolution termination condition is met; Output scheduling optimization scheme. On the premise of realizing global search, the invention adopts serial individual decoding based on insertion mode, individual improvement based on hierarchy and load balancing, parameterized uniform crossover based on preference and other methods to improve the optimization ability and search efficiency of the algorithm.

Description

technical field [0001] The invention relates to the fields of computer technology, information technology and system engineering, in particular to a cloud workflow scheduling optimization method, more specifically, to a cloud workflow scheduling optimization method based on a hierarchical and load balancing genetic algorithm. Background technique [0002] Workflow in cloud computing environment, referred to as "cloud workflow", is the integration of cloud computing and workflow-related technologies. Management, supply chain management and health care and other fields have broad application prospects. In cloud workflow, there are timing constraints between tasks, and virtual machines are usually used as the minimum allocation unit of computing resources to receive and process these tasks during execution. Cloud workflow scheduling refers to how to assign tasks in cloud workflow to appropriate virtual machines under the constraints of task timing and user needs, and how to ar...

AI Technical Summary

Problems solved by technology

[0011] In order to overcome that the quality of the heuristic method solution is usually not very high and depends on the type of workflow, combined with the heuristic semi-intelligent computing method and the incompleteness of the search space of the existing intelligent computing method based on hierarchical coding, based on the global search The intelligent calculation method will lead to the decrease of search efficiency and other deficiencies. The present invention provides a cloud workflow scheduling optimization method based on hierarchical and load balancing genetic algorithm, which effectively improves the efficiency and quality of the solution.

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