A Novel Reinforcement-Learning-Based Approach to Workflow Scheduling Upon Infrastructure-as-a-Service Clouds

Chen, Peng; Xia, Yunni; Yu, Chun

doi:10.4018/ijwsr.2021010102

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…After years of development, business intelligence covers a wider range of contents, including nancial intelligence, sales intelligence, procurement intelligence, and production intelligence. We need to think deeply and study the new intelligent three-dimensional dynamic accounting information platform [3]. rough this system, we can realize various functions including shortening the response time of complex problems, tentatively sending out new views and insights, trying the e ects of di erent strategies, enhancing management control, reducing costs, and making objective decisions, so as to meet the needs of accounting data mining [4].…”

Section: Introductionmentioning

confidence: 99%

Financial Accounting Information Data Analysis System Based on Internet of Things

2022

Mathematical Problems in Engineering

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In order to meet the intelligent demand of modern financial data analysis, this paper proposes a financial accounting information data analysis system based on the Internet of things. Based on the central reinforcement learning architecture, the model uses multiple execution modules to enhance the computing and generalization ability of the single-agent reinforcement learning algorithm. In the selection of reinforcement learning algorithm, the instantaneous time difference algorithm is introduced. The algorithm can synchronize the experience of the previous iteration state in the learning process and does not depend on the final prediction value, which greatly saves the storage cost. In the establishment of the financial data analysis index system, the paper comprehensively considers the enterprise’s operation, development, debt repayment, and other capabilities, ensuring the integrity and rationality of the index system. In order to evaluate the performance of the algorithm, this paper takes the real financial data as the sample and uses BP neural network to conduct a comparative experiment. The experimental results show that the recognition accuracy of the model is better than that of the BP neural network in each experimental scenario, and the recognition accuracy of Experiment 3 is improved by 4.6%. Conclusion. The performance of the distributed reinforcement learning algorithm is better than that of the common back-propagation neural network in the real data set scenario.

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Section: Introductionmentioning

confidence: 99%

Financial Accounting Information Data Analysis System Based on Internet of Things

2022

Mathematical Problems in Engineering

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“…In addition, anomaly diagnosis is not the final step. As shown in Figure 6, anomaly handling would be more practical, e.g., we could consider anomaly-aware adaptation based on deep-reinforcement-learning-based dynamic scheduling approaches [52,53,54] with effective anomaly detection and diagnosis, which would also contribute to QoS improvement [55].…”

Section: Discussionmentioning

confidence: 99%

Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model

Chen

Liu

Xin

et al. 2022

The Computer Journal

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Quality of data services is crucial for operational large-scale internet-of-things (IoT) research data infrastructure, in particular when serving large amounts of distributed users. Effectively detecting runtime anomalies and diagnosing their root cause helps to defend against adversarial attacks, thereby essentially boosting system security and robustness of the IoT infrastructure services. However, conventional anomaly detection methods are inadequate when facing the dynamic complexities of these systems. In contrast, supervised machine learning methods are unable to exploit large amounts of data due to the unavailability of labeled data. This paper leverages popular GAN-based generative models and end-to-end one-class classification to improve unsupervised anomaly detection. A novel heterogeneous BiGAN-based anomaly detection model Heterogeneous Temporal Anomaly-reconstruction GAN (HTA-GAN) is proposed to make better use of a one-class classifier and a novel anomaly scoring function. The Generator-Encoder-Discriminator BiGAN structure can lead to practical anomaly score computation and temporal feature capturing. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. The results show that HTA-GAN outperforms its competitors and demonstrates better robustness.

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Evaluating BERT on cloud-edge time series forecasting and sentiment analysis via prompt learning

Song

et al. 2022

2022 IEEE 24th Int Conf on High Performance Computing &Amp; Communications; 8th Int Conf on Data Science &Amp; Systems; 20th In

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A Novel Reinforcement-Learning-Based Approach to Workflow Scheduling Upon Infrastructure-as-a-Service Clouds

Cited by 4 publications

References 29 publications

Financial Accounting Information Data Analysis System Based on Internet of Things

Financial Accounting Information Data Analysis System Based on Internet of Things

Effectively Detecting Operational Anomalies In Large-Scale IoT Data Infrastructures By Using A GAN-Based Predictive Model

Evaluating BERT on cloud-edge time series forecasting and sentiment analysis via prompt learning

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