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Multi-objective workflow dynamic scheduling method based on quantum particle swarm optimization algorithm

A technology of quantum particle swarm and optimization algorithm, applied in the field of cloud computing, can solve the problem of less adjustment parameters, and achieve the effect of short execution time, low execution cost, optimal execution time and cost consumption

Active Publication Date: 2017-02-15
上海益源农业发展有限公司
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Problems solved by technology

However, PSO is easy to fall into local optimum. Sun Jun proposed a PSO algorithm with quantum behavior, that is, quantum particle swarm algorithm (QPSO). This algorithm has the advantages of simplicity, easy implementation and few adjustment parameters. Powerful global search capability

Method used

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  • Multi-objective workflow dynamic scheduling method based on quantum particle swarm optimization algorithm
  • Multi-objective workflow dynamic scheduling method based on quantum particle swarm optimization algorithm
  • Multi-objective workflow dynamic scheduling method based on quantum particle swarm optimization algorithm

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Embodiment Construction

[0033] The technical solutions provided by the present invention will be described in detail below in conjunction with specific examples. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention.

[0034] This implementation case dynamically divides the cloud computing workflow, and then uses the quantum particle swarm optimization algorithm to allocate the current optimal resources for the workflow tasks, thereby optimizing the execution time, cost and reliability of the workflow.

[0035] Such as figure 1 As shown, the method provided by the invention comprises the following steps:

[0036] Step 10, input the workflow V={v 1 ,v 2 ,v 3 ,v 4 ,v 5 ,v 6 ,v 7 ,v 8 ,v 9 ,v 10 ,v 11 ,v 12} and the user's QoS request {1h, 100﹩, 98%}. The workflow of this embodiment includes 12 tasks, and the input workflow is as follows figure 2 The directed acycli...

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Abstract

The invention discloses a quantum-behaved particle swarm optimization (QPSO) based multi-objective dynamic workflow scheduling method, and belongs to the technical field of cloud computing. The method includes the steps: inputting a workflow and a QoS (quality of service) request; acquiring state information of virtual machines and transmission information among the virtual machines; setting a to-be-executed task set V', and setting objective functions of time, cost and reliability for a task schedule in the V'; allocating optimal resources to the to-be-executed tasks by the aid of QPSO, and judging whether total time, total cost and total reliability of task execution meet the QoS request of a user or not after the tasks are executed; dynamically updating the V', transmission speed among the virtual machines and operating speeds of the virtual machines. By means of dynamically partitioning the workflow and dynamically updating network bandwidth information, the optimal resources are allocated to the workflow tasks accurately, errors between the calculated time and actual execution time and the calculated cost and actual execution cost are reduced, time can be shortened, and cost is reduced while reliability is enhanced.

Description

technical field [0001] The invention belongs to the technical field of cloud computing, and in particular relates to a method for dynamic computing-intensive workflow application processing based on quantum particle swarm optimization algorithm. Background technique [0002] Cloud workflow provides an efficient and feasible solution for the optimization of cloud computing system performance and operating costs. Integrating workflow into cloud computing not only reduces the cost of cloud computing, but also improves the quality of cloud services. The scheduling of cloud workflow is user-centered, selecting the optimal process execution that satisfies the quality of service (QoS) request put forward by the user, which is equivalent to the goal optimization problem. Combining the characteristics of cloud computing, Yan Ge et al. proposed a two-stage task scheduling strategy based on the improved abnormal earliest end time (SHEFT), which realized the optimization of the complet...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
Inventor 马廷淮储雅田伟钟水明
Owner 上海益源农业发展有限公司
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