No abstract

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

In this paper, we propose a unified framework for an abstractive summarization method which uses the prompt language model and a pointer mechanism. The abstractive summarization problem usually includes a text encoder and a text decoder. Current methods usually employ an encoder-decoder architecture to condense and paraphrase a document. To better paraphrase a document, we propose a unified framework for an abstractive summarization model that only uses a topic-sensitive decoder. Our model has a prompt input module, a text decoder and a pointer mechanism. We apply our model to Xsum, Gigaword, and CNN/DailyMail summarization datasets, and experimental results demonstrate that our model has achieved state-of-the-art results on the Xsum dataset and comparable results on the other two datasets.

Constructing energy-efficient database systems to reduce economic costs and environmental impact has been studied for ten years. With the emergence of the big data age, along with the data-centric and -intensive computing trend, the great amount of energy consumed by database systems has become a major concern in a society that pursues green information technology. However, to the best of our knowledge, despite the importance of this matter in Green IT, there have been few comprehensive or systematic studies conducted in this field. Therefore, the objective of this article is to present a literature survey with breadth and depth on existing energy management techniques for database systems. The existing literature are organized hierarchically with two major branches focusing separately on energy consumption models and energy-saving techniques. Under each branch, we first introduce some basic knowledge, and then we classify, discuss, and compare existing research according to their core ideas, basic approaches, and main characteristics. Finally, based on these observations through our study, we identify multiple open issues and challenges, and provide insights for future research. We hope that our outcome of this work will help researchers to develop more energy-efficient database systems.