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Optimal point number constraint-based multi-objective optimization method for data stream processing system

A multi-objective optimization and processing system technology, applied in the field of multi-objective optimization of data stream processing systems, can solve problems such as randomness without considering response delay and throughput, Palito optimal solution

Inactive Publication Date: 2017-07-14
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] Aiming at the defect that the existing multi-objective optimization method based on weight summation does not consider the user's trade-off between response delay and throughput when deploying and using, which causes the Palito optimal solution to be random, an optimal optimization method is proposed. Multi-objective optimization method for data stream processing system with number of advantages constraints

Method used

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Embodiment 1

[0078] This embodiment illustrates the process of applying the present invention "a multi-objective optimization method for a data stream processing system constrained by the number of optimal points" to a specific real-time big data analysis system Apache Spark Streaming.

[0079] figure 1 It is an algorithm flow chart of this method and a flow chart of this embodiment. As can be seen from the figure, the method includes the following steps:

[0080] Step A: The upper bound L of the current response delay upper is initialized to 10.0, the lower bound L of the current response delay lower Initialized to 0.5, the upper limit of the number of Pareto optimal points Step max is initialized to 20, the current iteration step number Step now is initialized to 0, the expression is formula (1), and the set of uncertain regions Set area is initialized as an empty set, and the expression is formula (2); the final detection result group plan is initialized as an empty set, and the ex...

Embodiment 2

[0133] Change the specific real-time big data analysis system Apache Spark Streaming in embodiment 1 into other real-time big data analysis systems such as Apache Storm, Google Dataflow, etc., that is, the multi-objective optimization method proposed by the present invention is not limited to the source of data, Suitable for data processing in all engineering applications.

[0134] Relevant technical contents not mentioned in the above embodiments can be realized by adopting or referring to existing technologies.

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Abstract

The invention discloses an optimal point number constraint-based multi-objective optimization method for a data stream processing system, and belongs to the field of real-time big data analysis of computer application. The method comprises the steps of giving out an uncertain region area according to upper and lower bounds of a response delay and upper and lower bounds of a throughput rate specified by a user; and based on the goal of reducing the uncertain region area, dividing an uncertain region into two regions, and continuing to detect an uncertain region with a maximum area, until an upper limit of iteration, thereby efficiently obtaining a group of Pareto optimal solutions with typical representative meanings, maximally reducing the overall uncertain region area, and providing a selection space for the user in the response delay and the throughput rate. For the multi-objective optimization problem of the data stream processing system, the defect of randomness of the Pareto optimal solutions can be avoided, one group of the Pareto optimal solutions with the typical representative meanings and a quantity given by the user can be efficiently obtained, and the selection space can be provided for the user in the response delay and the throughput rate.

Description

technical field [0001] The invention relates to a multi-objective optimization method for a data flow processing system, in particular to a multi-objective optimization method for a data flow processing system with the number of optimal points constrained, and belongs to the field of computer application technology and real-time big data analysis. Background technique [0002] In recent years, a large number of real-time big data analysis applications have emerged, such as social network dynamic analysis, intelligent traffic data analysis, large-scale data center monitoring, genetic data analysis, etc. Such applications not only have a large amount of data but also continuously and rapidly generate or update the data, requiring the data analysis system to continuously and real-time return or update the analysis results, which we call real-time big data (Big&fast data) analysis. Such applications have an urgent need for real-time big data analysis systems, and the system need...

Claims

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Application Information

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IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 曹朝盛伟曲大成
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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