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A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Li X, Xu J, Yang Y - Comput Intell Neurosci (2015)

Bottom Line: However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost.Adaptive inertia weight factor depends on the estimate value of cost.The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of ICSP, Ministry of Education, Anhui University, Hefei 230039, China ; School of Computer Science and Technology, Anhui University, Hefei 230601, China.

ABSTRACT
Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

No MeSH data available.


Small example of workflow.
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fig1: Small example of workflow.

Mentions: An example of workflow by task dependency graph DAG is given in Figure 1. After task A has executed, tasks B, C are ready to execute. Task D will execute after task C. When tasks B, D have finished, task E is ready. The execution time of task on VM type is shown in Table 1.


A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems.

Li X, Xu J, Yang Y - Comput Intell Neurosci (2015)

Small example of workflow.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4556880&req=5

fig1: Small example of workflow.
Mentions: An example of workflow by task dependency graph DAG is given in Figure 1. After task A has executed, tasks B, C are ready to execute. Task D will execute after task C. When tasks B, D have finished, task E is ready. The execution time of task on VM type is shown in Table 1.

Bottom Line: However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost.Adaptive inertia weight factor depends on the estimate value of cost.The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of ICSP, Ministry of Education, Anhui University, Hefei 230039, China ; School of Computer Science and Technology, Anhui University, Hefei 230601, China.

ABSTRACT
Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.

No MeSH data available.