Context-aware online IoT service scheduling approach in cloud environments

Xinyu Wang; Han Li; Yun Zhao

doi:10.1117/12.2639881

19 October 2022 Context-aware online IoT service scheduling approach in cloud environments

Xinyu Wang, Han Li, Yun Zhao

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943J (2022) https://doi.org/10.1117/12.2639881
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

IoT applications consist of a series of correlated IoT services. Compared with traditional services, IoT services have new features in distribution, dependency, security and dynamism, which pose new challenges to the assurance of performance for cloud-based IoT services. Service scheduling on the cloud is an effective approach to guarantee service performance. Taking into account the complex contextual information of IoT services, this paper proposes a Context-Aware Online IoT Service Scheduling Approach in Cloud Environments. First, based on the characteristics of IoT services, a description of the IoT service scheduling problem is given, including the constraints of transforming the contexts of IoT services into scheduling. Then, the service scheduling problem is mapped to a scheduling plan prediction problem based on a neural network approach. To verify the effectiveness of the method, two baseline algorithms were selected and simulated on the WorkflowSim platform together with the method proposed in this paper. In comparison, our method is found to have similar scheduling costs with the optimization method, but can improve the efficiency when generating the scheduling plan, which can better meet the demands of low time delay for online scheduling under the conditions of dynamic changes in IoT context.

Citation Download Citation

Xinyu Wang, Han Li, and Yun Zhao "Context-aware online IoT service scheduling approach in cloud environments", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943J (19 October 2022); https://doi.org/10.1117/12.2639881

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KEYWORDS

Clouds

Process modeling

Data processing

Computer simulations

Particle swarm optimization

Analytical research

Data modeling

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