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Online adaptive Prediction Method for Cloud computing Workloads with Bi-LSTM 3

Online Adaptive Prediction Method for Cloud Computing Worklo...

3rd International conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Liu, Junyu University of California School of Physical Science IrvineCA United States

ISBN: (纸本)9798350372052

With the increasing maturity and popularity of cloud computing technology, cloud applications in enterprises and individuals have become a significant trend in information technology. The efficient, stable, and economical operation of cloud computing environment poses a challenge to the industry. The accuracy of workload prediction is particularly critical, as it is directly related to rational deployment of resources, cost optimization, and system stability. Due to the unique characteristics of cloud computing environments, including its huge scale, resource-sharing mechanism, multi-tenant architecture, and diversity of application types, it is difficult for traditional prediction models to adapt to its dynamic and complex load change patterns. This study proposes an innovative deep-learning model to address the above challenges to predict resource loads in cloud computing environments. The model first performs a deep feature extraction on historical load data. Then, Bidirectional Long Short-Term Memory Networks (Bi-LSTM) is introduced to enhance the model's prediction capability on time series data. In addition, we utilize an optimized version of LSTM to enhance the prediction accuracy of the model further when facing different load scenarios. Experimental results show that the deep learning model proposed in this study improves prediction accuracies compared to traditional load prediction methods, and prediction errors are significantly reduced. This confirms the model's theoretical validity and provides practical technical support for cloud computing resource management and scheduling decisions in practical applications. © 2023 IEEE.

关键词： Cloud computing

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Towards Robust physical-world Backdoor Attacks on Lane Detection 24

Towards Robust Physical-world Backdoor Attacks on Lane Detec...

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32nd ACM International conference on Multimedia, MM 2024

作者： Zhang, Xinwei Liu, Aishan Zhang, Tianyuan Liang, Siyuan Liu, Xianglong SKLCCSE Beihang University Beijing China Institute of Dataspace China SKLCCSE Beihang University Shen Yuan Honors College Beijing China School of Computing National University of Singapore Singapore Singapore Zhongguancun Laboratory China

ISBN: (纸本)9798400706868

Deep learning-based lane detection (LD) plays a critical role in autonomous driving systems, such as adaptive cruise control. However, it is vulnerable to backdoor attacks. Existing backdoor attack methods on LD exhibit limited effectiveness in dynamic real-world scenarios, primarily because they fail to consider dynamic scene factors, including changes in driving perspectives (e.g., viewpoint transformations) and environmental conditions (e.g., weather or lighting changes). To tackle this issue, this paper introduces BadLANE, a dynamic scene adaptation backdoor attack for LD designed to withstand changes in real-world dynamic scene factors. To address the challenges posed by changing driving perspectives, we propose an amorphous trigger pattern composed of shapeless pixels. This trigger design allows the backdoor to be activated by various forms or shapes of mud spots or pollution on the road or lens, enabling adaptation to changes in vehicle observation viewpoints during driving. To mitigate the effects of environmental changes, we design a meta-learning framework to train meta-generators tailored to different environmental conditions. These generators produce meta-triggers that incorporate diverse environmental information, such as weather or lighting conditions, as the initialization of the trigger patterns for backdoor implantation, thus enabling adaptation to dynamic environments. Extensive experiments on various commonly used LD models in both digital and physical domains validate the effectiveness of our attacks, outperforming other baselines significantly (+25.15% on average in Attack Success Rate). Our codes can be found in https://***/Veee9/BadLANE. © 2024 ACM.

关键词： adaptive cruise control

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DDDAS-Based Learning for Edge computing at 5G and Beyond 5G 4th

DDDAS-Based Learning for Edge Computing at 5G and Beyond 5G

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4th International conference on Dynamic Data Driven Applications Systems, DDDAS 2022

作者： Runsewe, Temitope Yavuz, Abdurrahman Celik, Nurcin Saad, Walid Department of Industrial and Systems Engineering University of Miami MiamiFL33146 United States Department of Electrical and Computer Engineering Virginia Tech ArlingtonVA22203 United States

ISBN: (纸本)9783031526695

The emerging and foreseen advancements in 5G and Beyond 5G (B5G) networking infrastructures enhance both communications capabilities and the flexibility of executing computational tasks in a distributed manner, including those encountered in edge computing (EC). Both, 5G & B5G and EC environments present complexities and dynamicity in their multi-level and multimodal infrastructures. DDDAS-based design and adaptive and optimized management of the respective 5G, B5G, and EC infrastructures are needed, to tackle the stochasticity inherent in these complex and dynamic systems, and to provide quality solutions for respective requirements. In fact, both emerging communication and computational technologies and infrastructure systems can benefit from their symbiotic relationship in optimizing their corresponding adaptive and optimized management. EC enabled by 5G (and future B5G) allows efficient distributed execution of computational tasks. DDDAS-based methods can support the adaptivity in bandwidth and energy efficiencies in 5G and B5G communications. On the other hand, EC has become a very attractive feature for critical infrastructure such as energy grids as it allows for secure and efficient real-time data processing. In order to fully exploit the advantages of EC, the communication network should be able to tackle the changing requirements related to the task management within edge servers. Thus, leveraging the DDDAS paradigm, we jointly optimize the scheduling and offloading of computational tasks in an EC-enabled microgrid considering both physical constraints of microgrid and network requirements. The results showcase the superiority of the proposed DDDAS-based approaches in terms of network utilization and operational efficiencies achieved, with microgrids as case example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Energy efficiency

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A bio-inspired design of real-time control learning algorithm in model-free drone manipulation

A bio-inspired design of real-time control learning algorith...

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2024 International conference on Optics, Electronics, and Communication Engineering, OECE 2024

作者： Wang, Jiaguo Li, Wenheng Pei, Yang Northwestern Polytechnical University Shaanxi Xi’an710072 China AVIC Xi’an Aeronautics Computing Technique Research Institute Shaanxi Xi’an710068 China

ISBN: (纸本)9781510685437

In recent years, neural-network-based adaptive dynamic models are commonly used to estimate and control flight dynamics for drones and multi-copters. However, most of them simply use networks to optimize few parameters in control policy. There are still large improvements on the structural layout for robust model-free control applications. Therefore, to achieve a similar intelligence of human is still challenging due to their difference in basic mechanisms and difficulty in network modeling. In this paper, we design a control learning algorithm which combines reinforcement learning with neural networks simplified from human cerebellar motor learning model. The algorithm learns parameters by statistically measuring the performance and analyzing the input-output relationship on real-time episodes. In local linear systems, parameters are learned with respect to a spatial function of environment state and subjective expectation. Compared with other methods using static models, the most obvious advantage of this algorithm is that it can learn complex dynamics of alternative degrees of freedom while the dynamics are difficult to be formulated by equation set. Besides, the algorithm is suitable for individual systems, without prior knowledge about system geometry, centre of gravity as well as installation error, since it learns the dynamic effects directly relevant to the optimal guidance and control behavior in unknown or partially known environments instead. Experiment verifies the algorithm in a practical way. In the experiment, the algorithm is implemented to a quad-copter and it can learn the flight control policy from zero-state without any prior knowledge. The flight quality is tested to be equal to accurate control model at outdoor flight experiences. By repeated experiment, the algorithm is demonstrated to have good robustness to control different physical models and the potential to explore alternative dimensionality. © 2024 SPIE.

关键词： Drones

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Online adaptive Prediction Method for Cloud computing Workloads with Bi-LSTM

Online Adaptive Prediction Method for Cloud Computing Worklo...

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Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI), IEEE International conference on

作者： Junyu Liu University of California Irvine School of Physical Science Irvine California United States of America

ISBN: (数字)9798350372052

ISBN: (纸本)9798350372069

关键词： Deep learning Adaptation models Cloud computing Computational modeling Biological system modeling Time series analysis Predictive models

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Hierarchical Intelligent-Geometric Control Architecture for Unmanned Aerial Vehicles Operating in Uncertain environments 19th

Hierarchical Intelligent-Geometric Control Architecture for ...

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19th International conference on Artificial Intelligence and Soft computing, ICAISC 2020

作者： Khachumov, Mikhail Federal Research Center "Computer Science and Control" of RAS 44/2 Vavilova St Moscow119333 Russia 6 Miklukho-Maklaya St Moscow117198 Russia

ISBN: (纸本)9783030615338

This paper considers the cutting-edge scientific problem of controlling unmanned aerial vehicles (UAVs) in unstable conditions based on intelligent-geometric theory that combines geometric control methods (methods of optimal control, complex motion control and stabilization, trajectory tracking, differential pursuit-evasion games, etc.) with intelligent control methods using tools of artificial intelligence (productions, semantic networks, fuzzy logic, frame-based behavioral microprograms and operations, methods of knowledge acquisition, etc.). Such integration provides reliable and high-performance control techniques for operating in uncertain environments under wind disturbances. Hierarchical architecture of intelligent-geometric control system is proposed, designed for joint application of precise geometric and adaptive intelligent control methods as parts of a single robotic system. In accordance with the proposed architecture, the solution to the problem of controlling a UAV group taking into account mathematical models of an aircraft and wind loads was simulated in MATLAB Simulink system. © 2020, Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

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Large language model-driven probabilistic trajectory prediction in the Internet of Things using spatio-temporal encoding and normalizing flows

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Digital Communications and Networks 2025年

作者： Xiaoliang Wang Liming Xu Lian Zhou Yuzhen Liu Neal Xiong Kuan-Ching Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan 411201 China Sanya Institute of Hunan University of Science and Technology Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China Department of Computer Mathematical and Physical Sciences Sul Ross State University Alpine TX 79830 USA Dept of Computer Science and Information Engineering Providence University Taichung 43301 Taiwan Republic of China

The convergence of the Internet of Things (IoT) and foundation models, particularly Large Language Models (LLMs), heralds a new era of intelligent and adaptive systems. This investigation explores the integration of LLMs and IoT for probabilistic vehicle trajectory prediction, a critical component in autonomous driving. Traditional deterministic models often fail to capture the complexity and uncertainty of real-world driving environments. To address these limitations, we propose an innovative approach that leverages LLM-driven spatio-temporal encoding and normalizing flows. The proposed method extracts latent motion patterns from historical trajectory data using LLMs, providing a nuanced understanding of vehicle dynamics. Advanced spatio-temporal encoders are employed for comprehensive feature extraction, capturing temporal dependencies and spatial relationships. Additionally, scaled dot-product attention is utilized for effective multimodal feature fusion, enhancing the integration of diverse data sources. The normalizing flows framework enhances the model's ability to capture complex, multimodal probability distributions of future trajectories by constructing an invertible transformation network. The network systematically maps simple base distributions to intricate target distributions through reversible transformations, significantly improving prediction accuracy. Evaluation of the nuScenes dataset demonstrates substantial improvements over existing methods. The results underscore the model's capability to generate diverse and realistic trajectory distributions, paving the way for safer and more efficient autonomous driving systems.

关键词： Internet of things Large language models Normalizing flows Spatio-temporal encoding Trajectory prediction

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Modelling Utility of Networked Services in Military environments

Modelling Utility of Networked Services in Military Environm...

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International conference on Military Communications and Information Systems (ICMCIS)

作者： Kuikka Vesa Matti Syrjänen Information Technology Division The Finnish Defence Research Agency Riihimäki Finland

Utility of services and applications in the complex structure of a communication network are modelled using probabilistic methods. Quantitative utility measures are computed by combining two different views of utility: connectivity on the physical network level and usability from a user's point of view in performing military tasks or operations. This paper introduces a novel method of computing reliability measures by using adjacency matrices, and then combines this resulting information with usefulness information of services. Services are evaluated individually for all of the connections between all pairs of nodes in the networked services regardless of the number of physical links in the communication network between any two given nodes. This method enables describing various uses of different services and operational situations. Practical examples with numerical results are presented in the text. Different methods for computing probability of connectivity are available but adjacency matrices are illustrative and computationally efficient for small and medium-sized network structures in the military context.

关键词： Reliability Military computing Communication networks mathematical model Military communication Computational modeling Information systems

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Predictive Modeling for Infrastructure System Engineering

Predictive Modeling for Infrastructure System Engineering

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American-Society-of-Civil-Engineers (ASCE) India conference on Urbanization Challenges in Emerging Economies - Resilience and Sustainability of Infrastructure

作者： Vora, Ojas Vora, Pankaj Vora, Urjaswala Univ Mumbai Rizvi Coll Engn Mumbai 400050 Maharashtra India WTESL Mumbai 400055 Maharashtra India C DAC Mumbai 400049 Maharashtra India

ISBN: (数字)9780784482032

ISBN: (纸本)9780784482032

Civil infrastructure systems emphasize on co-existence of the different structures together as a system that is highly dynamic and with behavior that varies on wide temporal and spatial scales. Furthermore, urban environments can be viewed as a "system of systems" with complex interactions and interdependencies existing among its individual systems. The focus has been comparatively more on the resilient design of the individual infrastructure components rather than how infrastructure components interrelate and interact to make up a dynamic system. To model complex infrastructures systems with time-varying behaviors, system theory is a powerful tool that allows analytical and computational methods to fully describe the complex dynamics of independent and interdependent infrastructure systems. Cyber-physical systems are a new class of sensor-rich engineered systems that entail not only sensors but computing and actuation cohesively integrated for monitoring and controlling of the applications. The vast historical as well as real-time data obtained from such systems and its semantics result in a knowledge repository. Predictive analytics is one of the best practices of extracting information from such a repository in order to determine patterns and predict future outcomes and system behavior. Our earlier work proposed a decision support framework comprising a geologic prediction model, and a construction strategy decision model for tunnel design. We further extend and generalize the framework by including a predictive analytics model for effective engineering of infrastructure component(s) within a civil infrastructure system. The predictive analytics framework is sustainable due to its self-adaptive properties.

关键词： INFRASTRUCTURE Prediction models New classes WAREHOUSE decision making support Predictive real-time data system behavior Dynamic systems Frameworks computational methods Best Practices

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On the Performance of Selective Adaptation in State Lattices for Mobile Robot Motion Planning in Cluttered environments

On the Performance of Selective Adaptation in State Lattices...

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IEEE/RSJ International conference on Intelligent Robots and Systems (IROS) / Workshop on Machine Learning methods for High-Level Cognitive Capabilities in Robotics

作者： Napoli, Michael E. Biggie, Harel Howard, Thomas M. Univ Rochester Dept Elect & Comptuer Engn 601 Elmwood Ave Rochester NY 14627 USA

ISBN: (纸本)9781538626825

Autonomous mobile robots require motion planning algorithms that match limitations of on-board computing resources to safely navigate complex environments. In situations where near-optimality is preferential to runtime performance, search spaces that optimize their local connectivity to improve the global optimality of generated solutions are desirable. However, not all nodes in the search space benefit equally from optimization which can result in an inefficient use of computational resources and unnecessary increase in runtime. To address this limitation, we propose an approach called the Selectively adaptive State Lattice which uses a heuristic based on the local environment to selectively perform optimization and obtain a balance between runtime performance and relative optimality. We present a statistical evaluation of local connectivity optimization and global search with the State Lattice, adaptive State Lattice, and Selectively adaptive State Lattice algorithms in randomly generated obstacle fields. We further highlight the performance of each method on a Clearpath Robotics TurtleBot2 in a qualitative physical experiment.

关键词： Optimization Lattices Planning Runtime Trajectory Mobile robots

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