Ran Li
ABSTRACT
In the Internet-of-Things (IoT) cloud platform, optimizing resource scheduling is the main way to achieve the maximum benefit of the system. However, the current researches lack an effective solutions to manage the steady and the abnormal state changes of batch tasks as a whole. To solve the problem of cloud resource scheduling for batch tasks under different scenarios and achieve the maximum benefit of the power IoT cloud platform, this paper proposes a Multi-Objective Optimization Model (MOOM) for dynamic resource scheduling. Firstly, we analyze the task execution performance parameters under the steady state, and proposes a performance analysis model based on queuing theory. Based on the analysis model, we can calculate the approximate solution of task performance parameters under a certain configuration. Then, considering different operation scenarios of the power IoT, a dynamic scheduling mechanism for cloud resources is constructed based on the performance parameters, which can guide the cloud platform to determine the optimal resource scheduling scheme under a given scenario. In addition, MOOM also contains the optimization objective of cost minimization, and proposes a method to quantify the cost. Finally, extensive experimental evaluations demonstrate the efficiency and effectiveness of our proposed model.
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Index Terms
- Multi-Objective Optimization of Dynamic Resource Scheduling in IoT Cloud Platform
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