Binbin Wu’s research while affiliated with Tsinghua University and other places

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Publications (9)


Figure 1. CloudOps implementation process architecture diagram
Enterprise cloud resource optimization and management based on cloud operations
  • Article
  • Full-text available

May 2024

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84 Reads

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20 Citations

Applied and Computational Engineering

Binbin Wu

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Yulu Gong

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[...]

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The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.

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Figure 1. CloudOps implementation process architecture diagram
Enterprise cloud resource optimization and management based on cloud operations

May 2024

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33 Reads

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14 Citations

Applied and Computational Engineering

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.


Figure 2. Ratio before and after critical loss In the process of algorithm optimization, a neural network is configured to output 7 numerical values ranging between 0 and 1, representing the evaluation scores of instructions. The average of these scores determines the quality of an instruction, with higher averages indicating better instructions. Following the application of optimal instructions to the computer network, their effectiveness is evaluated. Instructions are categorized as correct or incorrect based on their outcomes. To calculate critical losses, four values are considered: 1) The target evaluation score for correct instructions is set at 0.7. Scores equal to or greater than 0.7 are associated with non-critical losses; 2) The safety threshold for the average evaluation score of correct instructions is 0.56. An average score equal to or greater than this value is also associated with noncritical losses; 3) The target evaluation score for incorrect instructions is set at 0.3. Scores equal to or
Integration of computer networks and artificial neural networks for an AI-based network operator

May 2024

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196 Reads

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19 Citations

Applied and Computational Engineering

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.


Practical applications of advanced cloud services and generative AI systems in medical image analysis

May 2024

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84 Reads

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18 Citations

Applied and Computational Engineering

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services. The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate synthetic data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.


Implementation of seamless assistance with Google Assistant leveraging cloud computing

May 2024

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112 Reads

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47 Citations

Applied and Computational Engineering

AI and cloud native are mutually reinforcing and inseparable. Due to the huge storage and computing power requirements, most AI applications need cloud support, especially large model applications If cloud native has influenced the software industry to a considerable extent in the past few years, the big model boom means that cloud native has become a standard option for developers.This paper describes the rise of AI model applications and their integration with traditional development workflows, pointing out the challenges that enterprises and developers face when integrating large models. With the rise of cloud-native technologies, the combination of artificial intelligence and cloud computing is becoming increasingly important. Cloud-native technologies provide the infrastructure needed to build and run resilient and scalable applications, while distributed infrastructure supports multi-cloud integration, enabling a unified foundation of "one cloud, multiple computing." As an intelligent voice Assistant, Google Assistant achieves a more intelligent, convenient and efficient user experience through applications in smart home control, enterprise customer service and healthcare. Finally, this paper points out the advantages of combining Google Assistant with cloud computing, providing a more intelligent, convenient, and efficient user experience.


Integration of Computer Networks and Artificial Neural Networks for an AI-based Network Operator

May 2024

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108 Reads

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2 Citations

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Furthermore, a novel algorithm is proposed to emphasize crucial training losses, aiming to enhance the efficiency of operator training. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outlines the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.



Enterprise Cloud Resource Optimization and Management Based on Cloud Operations

May 2024

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30 Reads

The so-called automated operation and maintenance refers to a large number of repetitive tasks in daily IT operations (from simple daily checks, configuration changes and software installation to organizational scheduling of the entire change process) from manual execution in the past to standardized, streamlined and automated operations. This article delves into the realm of enterprise cloud resource optimization and management, leveraging automated operations (autoOps) as a fundamental strategy. As industries like banking witness exponential growth and innovation in IT systems, the complexity of managing resources escalates. Automated operations have emerged as a critical component, transitioning from manual interventions to encompass standardization, workflow optimization, and architectural enhancements. Focusing on the intersection of autoOps and cloud resource management, this research offers insights for practitioners and experts. Through real-world deployments and theoretical frameworks, it elucidates effective strategies for optimizing and governing enterprise cloud resources, thereby enhancing efficiency, security, and resilience in IT operations.


Figure 1. LSTM cloud computing resource optimization model
Virtual machine question-answer dataset
Dynamic resource allocation for virtual machine migration optimization using machine learning

April 2024

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230 Reads

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22 Citations

Applied and Computational Engineering

This article delves into the importance of applying machine learning and deep reinforcement learning techniques in cloud resource management and virtual machine migration optimization, highlighting the role of these advanced technologies in dealing with the dynamic changes and complexities of cloud computing environments. Through environment modeling, policy learning, and adaptive enhancement, machine learning methods, especially deep reinforcement learning, provide effective solutions for dynamic resource allocation and virtual intelligence migration. These technologies can help cloud service providers improve resource utilization, reduce energy consumption, and improve service reliability and performance. Effective strategies include simplifying state space and action space, reward shaping, model lightweight and acceleration, and accelerating the learning process through transfer learning and meta-learning techniques. With the continuous progress of machine learning and deep reinforcement learning technologies, combined with the rapid development of cloud computing technology, it is expected that the application of these technologies in cloud resource management and virtual machine migration optimization will be more extensive and in-depth. Researchers will continue to explore more efficient algorithms and models to further improve the accuracy and efficiency of decision making. In addition, with the integration of edge computing, Internet of Things and other technologies, cloud computing resource management will face more new challenges and opportunities, and the application scope and depth of machine learning and deep reinforcement learning technology will also expand, opening new possibilities for building a more intelligent, efficient and reliable cloud computing service system.

Citations (7)


... We employ Markov Chain Monte Carlo (MCMC) methods for Bayesian inference of model parameters. Specifically, we use a Gibbs sampling algorithm with Metropolis-Hastings steps for non-conjugate full conditionals [29]. The MCMC procedure iterates through the following steps: Sample individual-level parameters (α_ij, β_ij) from their full conditionals Sample market-level parameters (γ_j) from their full conditionals Sample hyperparameters (μ_α_j, μ_β_j, μ_γ, Σ_β, Σ_γ, σ_ε^2) from their full conditionals Sample personalization layer parameters (Θ, Σ_η) using Metropolis-Hastings steps Sample causal inference parameters (τ_i, λ) from their full conditionals Convergence diagnostics, including Gelman-Rubin statistics and trace plots, assess MCMC convergence. ...

Reference:

A Personalized Causal Inference Framework for Media Effectiveness Using Hierarchical Bayesian Market Mix Models
Enterprise cloud resource optimization and management based on cloud operations

Applied and Computational Engineering

... Tsantekidis et al. proposed a deep convolutional neural network approach for high-frequency time series forecasting in limit order books [27] . Their model captured spatial and temporal dependencies in order book data, outperforming traditional time series models in predicting short-term price movements. ...

Enterprise cloud resource optimization and management based on cloud operations

Applied and Computational Engineering

... In a computer network course, network simulation is a technique whereby a software program models the behavior of a network by calculating the interactions between the different network entities such as routers, switches, nodes, access points, and links [14]. Their use in the computer network classroom has the potential to generate higher learning outcomes in ways that were not previously possible. ...

Integration of computer networks and artificial neural networks for an AI-based network operator

Applied and Computational Engineering

... The state space representation implements a sophisticated multi-level encoding scheme that captures temporal and spatial coverage progression aspects. This hierarchical encoding methodology enables efficient processing of complex coverage patterns while maintaining critical temporal relationships [23] . Table 2 presents the comprehensive state vector composition with corresponding dimensionality specifications. ...

Implementation of seamless assistance with Google Assistant leveraging cloud computing

Applied and Computational Engineering

... Transparency in AI-driven recommendations becomes crucial for maintaining user trust. The rapid pace of technological advancement creates a skills gap, with designers needing to update their knowledge of AI technologies continually [8]. Integrating AI tools into existing design workflows requires organizational changes and potential resistance from traditional design teams. ...

Integration of Computer Networks and Artificial Neural Networks for an AI-based Network Operator
  • Citing Preprint
  • May 2024

... It becomes more problematic when you consider that the execution workloads of AI are dynamic, with its resource requirements varying significantly depending on what it is doing. For example, if you are doing image classification, which is a relatively simple task compared to natural language processing tasks, this will likely require fewer resources [11]. It is difficult to determine the number of resources allocated for each task; over-provisioning shortens the host lifespan, while under-provisioning degrades performance. ...

Dynamic resource allocation for virtual machine migration optimization using machine learning

Applied and Computational Engineering