Scientific work flow scheduling method with safe perception under cloud calculation environment

Abstract

The invention relates to a scientific work flow scheduling method with safe perception under a cloud calculation environment. The method comprises the following steps of: firstly, calculating idle time of a single task in a scientific work flow task according to calculating time of the single task in a scientific work flow and data transmission time among the tasks; randomly setting an allowable safety service of the idle time for the single task in the scientific work flow according to a dependence relation among a safety service model, the idle time and the task; adding an expenditure brought about by the single task safety service in the scientific work flow into predication of task executing time, and adding an improvement on the calculation of the task expenditure into an MCP (Mixed Complementary Problem) algorithm; and finally, mapping a resource for the single task in the scientific work flow according to a resource condition in a data center of the cloud calculation environment and utilizing the resource to establish a virtual machine and execute the task. According to the scientific work flow scheduling method disclosed by the invention, the safety service grade of a whole body is improved under the precondition of not influencing the scheduling performance of the scientific work flow, and the safety risk of deploying under the cloud calculation environment is reduced.

Description

technical field [0001] The invention relates to a scheduling method of a data-intensive scientific workflow, in particular to a security-aware scientific workflow scheduling method under a cloud computing environment. Background technique [0002] Cloud computing is a typical network computing model, emphasizing the scalability and availability of running large-scale applications in a virtual computing environment. Large-scale network applications based on cloud computing present heterogeneous distribution, heterogeneous characteristics, and data-intensive trends, such as scientific workflow systems. Such applications are called data-intensive applications. At present, data-intensive applications have been widely used in fields such as astronomy, high-energy physics, and bioinformatics. The data-intensive nature of this type of application is mainly reflected in the fact that the size of the data it processes usually reaches terabytes or even petabytes, including existing i...

AI Technical Summary

Problems solved by technology

However, the above-mentioned research on scientific workflow data scheduling in the cloud computing environment does not comprehensively consider the data security and performance of scientific workflow.

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