How to design efficient scheduling strategy for different environments is a hot topic in cloud computing. In the private cloud of computer science labs in universities, there are several kinds of tasks with different resource requirements, constraints, and lifecycles such as IT infrastructure tasks, course design tasks submitted by undergraduate students, deep learning tasks and and so forth. Taking the actual needs of our laboratory as an instance, these tasks are analyzed, and scheduled respectively by different scheduling strategies. The Batch Scheduler is designed to process tasks in rush time to improve system throughput. Dynamic scheduling algorithm is proposed to tackle long-term lifecycle tasks such as deep learning tasks which are hungry for GPU resources and have dynamically changing priorities. Experiments show that the scheduling strategies proposed in this paper improve resource utilization and efficiency.

This paper proposes a deep learning model based on a recurrent neural network (RNN) to solve the problem of text normalization for speech synthesis. Traditional rule-based models cannot take advantage of contextual information and do not handle text outside of rules well, while deep learning-based models can handle these problems better. Based on the seq2seq neural network, we construct a new text-normalized deep learning model that considers the context of words in sentences by using the gated recurrent unit (GRU) and local attention mechanism. In recent years, seq2seq has made many achievements in different fields of natural language processing. Our research proves that well-constructed small network models in specific application fields can also achieve meaningful results. In our small network, the local attention mechanism is used to reduce the computational complexity without decreasing accuracy. In the experiments, we compared our model with the attention-based model proposed by Sproat, the without attention model and the attention model. Experimental results show that our method reduces the network scale while gathering the main context information, overcomes the defect of high complexity of the traditional attention mechanism model, and achieves higher accuracy. It can also be seen from experiments that the most important related words are relatively close to the target word in text normalization.