Workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring

A multi-objective particle swarm, real-time state technology, applied in instruments, computing models, data processing applications, etc., can solve problems such as incompleteness, low convergence of workflow scheduling solutions, and lack of real-time state detection mechanisms, and achieve good convergence. Sex and diversity, improve local search ability, improve the effect of global search ability

Active Publication Date: 2018-06-08
ZHEJIANG SCI-TECH UNIV
View PDF3 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing multi-objective particle swarm optimization algorithm lacks a real-time state detection mechanism, and it is impossible to determine when and what strategy the algorithm adopts to a certain extent

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring
  • Workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring
  • Workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0053] The present invention will be described in detail below in conjunction with the accompanying drawings and specific implementation, but not as a limitation to the invention.

[0054] 1. Method

[0055] Such as figure 1 , the implementation steps of this method are as follows:

[0056] A. Workflow pre-scheduling

[0057] Use the BHEFT algorithm to pre-schedule the workflow, and judge whether the time and execution cost of the workflow scheduling meet the deadline and budget constraints set by the user. If not met, remind user to reset deadline / budget. If the conditions are met, the subsequent steps are performed.

[0058] B. Initialize population individuals

[0059] Such as figure 2 As shown, first initialize the capacity V of the global elite document, the maximum number of iterations, the number of initial iterations, the velocity and position of the particles that satisfy the workflow optimization scheduling solution discrete characteristics.

[0060] Then cal...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a workflow scheduling method for multi-target particle swarm optimization based on real-time state monitoring and relates to the field of cloud computing workflow scheduling. According to the method, first, workflow pre-scheduling is performed through a BHEFT algorithm, and the feasibility of the algorithm is improved; and a Pareto variance is introduced to perform real-time monitoring on an evolution state, wherein when the evolution state is in diversification, an external elite swarm self-optimization strategy is adopted to improve local search capability of the algorithm, and when the evolution state is in stagnation, an escape strategy is adopted, so that workflow scheduling solution spaces are diversified. In this way, development and exploitation of the algorithm in the evolution process are effectively balanced, and the convergence of workflow scheduling solutions and the diversity of scheduling solution space distribution are realized.

Description

technical field [0001] The invention relates to the field of cloud computing workflow scheduling, in particular to a workflow scheduling method based on real-time state monitoring and multi-objective particle swarm optimization. Background technique [0002] With the rapid development of cloud computing technology, more and more organizations migrate traditional business processes and applications to the cloud computing environment. Cloud computing is a computing model that uses the Internet to realize anytime, anywhere, on-demand, and convenient use of resources such as shared computing facilities, storage devices, and applications. The main features of cloud computing are high scalability and high reliability, which means that users can rent and release resources on demand, and providers use measures such as multi-copy data fault tolerance and homogeneous swapping of computing nodes to ensure high reliability of services. A workflow consists of a set of tasks with data de...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06N3/00G06Q10/04G06Q10/06
CPCG06N3/006G06Q10/04G06Q10/0631
Inventor 包晓安曹云棣张娜
Owner ZHEJIANG SCI-TECH UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap