Context-aware online IoT service scheduling approach in cloud environments

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.