1,721,002 research outputs found
ALATO: An efficient intelligent algorithm for time optimization in an economic grid based on adaptive stochastic Petri net
No full textCost and execution time are important issues in economic grids, which are widely used for parallel computing. This paper proposes ALATO, an intelligent algorithm based on learning automata and adaptive stochastic Petri nets (ASPNs) that optimizes the execution time for tasks in economic grids. ASPNs are based on learning automata that predict their next state based on current information and the previous state and use feedback from the environment to update their state. The environmental reactions are extremely helpful for teaching Petri nets in dynamic environments. We use SPNP software to model ASPNs and evaluate execution time and costs for 200 tasks with different parameters based on World Wide Grid standard resources. ALATO performs better than all other heuristic methods in reducing execution time for these tasks
Improving Channel Assignment in Multi-radio Wireless Mesh Networks with Learning Automata
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PGSW-OS: a novel approach for resource management in a semantic web operating system based on a P2P grid architecture
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A Dynamic Cloud with Data Privacy Preservation
Full text linkThe emerging field of Cloud Computing provides elastic on-demand services over the Internet or over a network. According to the International Data Corporation (IDC), cloud computing has two major issues: i) architecture issues, such as a lack of standardization, a lack of customization; and ii) users’ data privacy. In this study we focus on these issues. We are facing an increasing demand for migration of varieties of traditional databases and computation services to cloud computing environments, i.e., database-as-a-service. Although each service offers a new feature, it escalates standardization and customization issues due to the lack of standardization between cloud vendors and service customization because each cloud-based service has its own features, requirements and outputs. In the first part of this study, we propose a cloud architecture based on a Service-Oriented Architecture (DCCSOA) that enhances our ability to do standardization and customization in the cloud. The proposed architecture uses a single layer, which is called Dynamic Template Service Layer (DTSL), that provides the following operations and advantages: i) enables a single service layer to interact with all native cloud services (e.g., IaaS, PaaS, SaaS and any cloud-based services); ii) provides a standardization for existing services and future services in the cloud; iii) customizes native cloud services based on users’ group requests. The second part of this study focuses on users’ data privacy preservation on the proposed architecture. Users’ data privacy can be violated by the cloud vendor, the vendor’s authorized users, other cloud users, unauthorized users, or external malicious entities. Encryption of data on client side is one of the solutions to preserve data privacy in the cloud; however, encryption methods are complex and expensive for mobile devices to encrypt and decrypt each file, such as smart phones. We propose a novel light-weight data privacy method (DPM) by using a chaos system for mobile cloud users to store data on multiple clouds. The proposed method enables mobile users to store data in the clouds while it preserves users’ data privacy. We consider different technologies to deploy our proposed data privacy preservation method on DCCSOA, including the mobile devices, the Internet-of-things (IoT), and Graphic Processing Unit (GPU)-based computing. We also consider different use case scenarios for the proof of concept, including data privacy preservation for users’ photos in smart phones, sensitive electronic health records protection in the cloud, and data privacy preservation for cloud-based databases. We evaluate both the proposed dynamic architecture and the proposed data privacy preservation method. Our experimental results show that on the one hand DCCSOA enhances standardization by offering a flexible cloud architecture and minimizing the modification on the native cloud services; on the other hand, DPM achieves a superior performance over regular encryption methods in regard to computation time
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Scheduling, Characterization and Prediction of HPC Workloads for Distributed Computing Environments
Full text linkAs High Performance Computing (HPC) has grown considerably and is expected to grow even more, effective resource management for distributed computing sys- tems is motivated more than ever. As the computational workloads grow in quantity, it is becoming more crucial to apply efficient resource management and workload scheduling to use resources efficiently while keeping the computational performance reasonably good. The problem of efficiently scheduling workloads on resources while meeting performance standards is hard. Additionally, non-clairvoyance of job dimen- sions makes resource management even harder in real-world scenarios. Our research methodology investigates the scheduling problem compliant for HPC and researches the challenges for deploying the scheduling in real world-scenarios using state of the art machine learning and data science techniques.To this end, this Ph.D. dissertation makes the following core contributions: a) We perform a theoretical analysis of space-sharing, non-preemptive scheduling: we studied this scheduling problem and proposed scheduling algorithms with polyno- mial computation time. We also proved constant upper-bounds for the performance of these algorithms. b) We studied the sensitivity of scheduling algorithms to the accuracy of runtime and devised a meta-learning approach to estimate prediction accuracy for newly submitted jobs to the HPC system. c) We studied the runtime prediction problem for HPC applications. For this purpose, we studied the distri- bution of available public workloads and proposed two different solutions that can predict multi-modal distributions: switching state-space models and Mixture Density Networks. d) We studied the effectiveness of recent recurrent neural network models for CPU usage trace prediction for individual VM traces as well as aggregate CPU usage traces. In this dissertation, we explore solutions to improve the performance of scheduling workloads on distributed systems.We begin by looking at the problem from the theoretical perspective. Modeling the problem mathematically, we first propose a scheduling algorithm that finds a constant approximation of the optimal solution for the problem in polynomial time. We prove that the performance of the algorithm (average completion time is the constant approximation of the performance of the optimal scheduling. We next look at the problem in real-world scenarios. Considering High-Performance Computing (HPC) workload computing environments as the most similar real-world equivalent of our mathematical model, we explore the problem of predicting application runtime. We propose an algorithm to handle the existing uncertainties in the real world and show-case our algorithm with demonstrative effectiveness in terms of response time and resource utilization. After looking at the uncertainty problem, we focus on trying to improve the accuracy of existing prediction approaches for HPC application runtime. We propose two solutions, one based on Kalman filters and one based on deep density mixture networks. We showcase the effectiveness of our prediction approaches by comparing with previous prediction approaches in terms of prediction accuracy and impact on improving scheduling performance. In the end, we focus on predicting resource usage for individual applications during their execution. We explore the application of recurrent neural networks for predicting resource usage of applications deployed on individual virtual machines. To validate our proposed models and solutions, we performed extensive trace-driven simulation and measured the effectiveness of our approaches
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Personalizing Autonomous Driving with Rich Human Guidance
Full text linkWith progress in enabling autonomous cars to drive safely on the road, it is time to ask how should they be driving. This dissertation focuses on learning the desired objective function for autonomous cars with the goal of personalizing autonomous driving: drive following the passenger’s preferences across diverse environments. Traditionally autonomous cars have been trained using expert demonstrations, with an implicit assumption that the demonstrations are truly representative of optimal driving. Personalizing autonomous driving under this assumption would mean using Inverse Reinforcement Learning (IRL) to learn the objective function latent in the user’s own demonstration and then adopt the user’s own driving style. In this thesis, we question this assumption and propose algorithmic solutions for personalizing driving styles without demonstration data. Through user studies in a simulated driving environment, we first show that people do not want their autonomous cars to drive like them: they want a significantly more defensive car. Next we formalize driving preference as reward functions and propose several algorithms to learn them interactively from an alternative form of human guidance: Preference-based Learning. In Preference-based reward learning we show users several trajectory pairs sequentially and ask them to indicate their preference in each pair. This has been shown to be effective for learning reward functions in absence of demonstrations. Simple preference is, however, far less informative than all the demonstration data. The key contribution of this thesis is an algorithmic framework that leverages computational models of human behavior to enable learning from richer preference queries where response to each query contains more information than just a comparison. We propose different forms of rich preference queries. We ask people not only what they prefer, but also why they prefer. We design new queries to learn more complex reward functions that can potentially represent preferences in non-stationary environments. We introduce reward dynamics as a mixture of reward functions and parameters that govern how preferences change in response to the dynamics of the environment. We develop a unified formalism for treating all forms of human guidance as observations about the true preferences and use this formalism to derive objective functions for actively generating rich queries. We show empirically through simulations and also with user studies that richer preference queries can learn driving preference more accurately than comparison-alone queries. We also discover that richer queries not only speed up preference learning in practice but also offer more transparency into the decision-making algorithms of the autonomous car, thus enhancing people’s trust in the system. Although the human- robot system of choice in this thesis is autonomous car, our algorithmic solutions apply to personalizing other human-robot systems where the robot is a dynamical system that should match human preference and where demonstrations are unavailable due to complexity of robot operation or disparity between preferences and demonstrations
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Defense Frameworks Against Adversarial Attacks on Deep Learning Models
Full text linkDeep learning has made remarkable progress over the past decade across various fields, such as Computer Vision, Natural Language Processing (NLP), and Speech Recognition, driving innovation and advancements of various applications. One key challenge is to improve deep learning models' generalization, which refers to the capability of a model to handle unseen data. Several factors contribute to the challenge of generalization in deep learning models, including limited data availability, overfitting tendencies, and the inherent complexity of the models themselves.The phenomenon of adversarial samples is symptomatic of the limitation in the generalization capability of deep learning models. Adversarial samples refer to data instances manipulated from their originals, often appearing visually similar and imperceptible to human senses, yet causing incorrect predictions from deep learning models. This phenomenon presents a substantial concern for security-sensitive domains like medical diagnosis, autonomous driving, and anomaly detection, where model reliability is crucial.The development of various adversarial defense methods in recent years, such as adversarial training, noise reduction, and gradient masking, emphasizes the considerable efforts to enhance the robustness and reliability of deep learning models. Meanwhile, as innovative adversarial attacks continue to evolve, they effectively expose the vulnerabilities inherent in deep learning models, thereby raising challenges for existing defense methodologies.Although adversarial defense research has made advancements, the root cause of the vulnerability in deep learning models is still not fully understood. Additionally, there is a pressing need for defense mechanisms that offer comprehensive protection and high resilience against a wide range of adversarial attacks. The research presented in this dissertation aims to contribute to the enrichment of knowledge within the research community by providing deeper insights into adversarial attacks and defense mechanisms. It endeavors to develop novel defense methods that are robust and reliable when protecting deep learning models against adversarial threats. Through this work, we seek to advance the field of adversarial defense and contribute to the development of more effective defense strategies.In this dissertation, we introduced three novel defense mechanisms aimed at enhancing the robustness of deep learning models against adversarial attacks. In the first work, we tackled the issue of image blurring in traditional Variational Autoencoder (VAE)-based generative networks by focusing on improving high-fidelity data reconstruction. Additionally, this work optimized the model's decision-making strategy through a Bayesian update, allowing a model to incorporate multiple sources of supporting evidence for the final decision. The second study proposed a new generative network structure coupled with a new two-step noise reduction approach designed to effectively filter out adversarial noise. The third method introduced a new noise reduction mechanism called VQUNet. This method features a learnable quantization of latent features and a hierarchical network structure for high-fidelity data reconstruction. VQUNet's unique design significantly enhances the data reconstruction quality after the filtering process, while effectively regularizing adversarial perturbation within the network, thereby improving its resilience against adversarial attacks.Extensive experimental investigations demonstrated that the proposed methods provided superior robustness to the targeted deep learning models. They exhibited superior performance over other state-of-the-art noise-reduction-based defense methods, achieving prediction improvement with a notable margin over existing methods under adversarial attacks across both Fashion-MNIST and CIFAR10 datasets. The experimental analysis underscored the effectiveness, resilience, and robustness of the proposed methods against adversarial attacks. These findings offered valuable insights into the development of effective defense strategies, shedding light on the mechanisms and principles for future research
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