Event Triggered Optimization Method for Dynamic Task Decomposition Mode in Cloud Fusion

Authors

Wang Yan, College of the Internet of Things, Jiangnan University, Wuxi 214122, China;
Lijun Cheng, College of the Internet of Things, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: Aiming at the characteristics of randomness, dynamics, diversity and uncertainty of arrival time in the cloud fusion mode, two triggered tasks of the new tasks randomly reaching the cloud platform and the non-executable sub-tasks are considered. To improve the sub-tasks correlation degree and the workload balance of each group, the coupling degree of the sub-tasks is decreased, and an event-triggered mechanism (ETM) based optimization method for dynamic task decomposition mode is proposed. On the basis of the unified tasks description, the optimal flow of dynamic task decomposition is designed to determine the sub-tasks information flow. A multi-objective optimal model of the dynamical task decomposition based on groups is established. An improved adaptive genetic algorithm is proposed to solve the problem. The simulation results show that the proposed method can achieve the balance of cloud-fusion task-resource allocation, improve the efficiency and balance of task assignment; the presented optimization algorithm also obtains better precision and convergence performance than traditional methods.

Recommended Citation

Yan, Wang and Cheng, Lijun (2019) "Event Triggered Optimization Method for Dynamic Task Decomposition Mode in Cloud Fusion," Journal of System Simulation: Vol. 30: Iss. 11, Article 1.
DOI: 10.16182/j.issn1004731x.joss.201811001
Available at: https://dc-china-simulation.researchcommons.org/journal/vol30/iss11/1

First Page

4029

Revised Date

2018-10-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.201811001

Last Page

4042

CLC

TP391.9

Recommended Citation

Wang Yan, Cheng Lijun. Event Triggered Optimization Method for Dynamic Task Decomposition Mode in Cloud Fusion[J]. Journal of System Simulation, 2018, 30(11): 4029-4042.

DOI

10.16182/j.issn1004731x.joss.201811001