Compared with the traditional two-level or h-level voltage source converter, modular multilevel converter has many incomparable advantages, such as long-distance high-capacity transmission, asynchronous grid interconn...
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ISBN:
(纸本)9781665435253
Compared with the traditional two-level or h-level voltage source converter, modular multilevel converter has many incomparable advantages, such as long-distance high-capacity transmission, asynchronous grid interconnection, distributed generation connected to the grid, wind power generation connected to the grid, passive Island, drilling platform and so on There are broad application prospects in many fields, such as power supply in partial suitable areas, capacity expansion and reconstruction of urban power grid, etc. This paper studies the control strategy of flexible HVDC system based on MMC in cloud computing environment.
In the Bitcoin system, transaction history of an address can be useful in many scenarios, such as the balance calculation and behavior analysis. However, it is non-trivial for a common user who runs a light node to fe...
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ISBN:
(纸本)9781728170022
In the Bitcoin system, transaction history of an address can be useful in many scenarios, such as the balance calculation and behavior analysis. However, it is non-trivial for a common user who runs a light node to fetch historical transactions, since it only stores headers without any transaction details. It usually has to request a full node who stores the complete data. Validation of these query results is critical and mainly involves two aspects: correctness and completeness. The former can be implemented via Merkle branch easily, while the latter is quite difficult in the Bitcoin protocol. To enable the completeness validation, a strawman design is proposed, which simply includes the BF (Bloom filter) in the headers. However, since the size of BF is about KB, light nodes in the strawman will suffer from the incremental storage burden. What's worse, an integrated block must be transmitted when BF cannot work, resulting in large network overhead. In this paper, we propose LVQ, the first lightweight verifiable query approach that reduces the storage requirement and network overhead at the same time. To be specific, by only storing the hash of BF in headers, LVQ keeps data stored by light nodes being little. Besides, LVQ introduces a novel BMT (BF integrated Merkle Tree) structure for lightweight query, which can eliminate the communication costs of query results by merging the multiple successive BFs. Furthermore, when BF cannot work, a lightweight proof by SMT (Sorted Merkle Tree) is exploited to further reduce the network overhead. The security analysis confirms LVQ's ability to enable both correctness and completeness validation. In addition, the experimental results demonstrate its lightweight.
Although cloud storage technology can provide users with convenient storage services, a large amount of duplicate data in the cloud brings a huge storage burden and the risk of privacy leakage. To improve the utilizat...
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Although cloud storage technology can provide users with convenient storage services, a large amount of duplicate data in the cloud brings a huge storage burden and the risk of privacy leakage. To improve the utilization of cloud storage resources and protect data confidentiality, random message lock encryption technology (R-MLE) can be used to delete redundant data in the cloud. But the theoretical basis of the deduplication scheme based on R-MLE is bilinear mapping, so the computational cost of finding duplicate fingerprint-tags is relatively large. To improve the deduplication efficiency, we proposed a secure deduplication scheme based on the autoencoder model in our previous research, using the model to generate the abstract-tags of the data, and using the similarity of the abstract-tags to quickly filter out the fingerprint-tags with high repeatability, which greatly reduces the number of fingerprint-tag comparisons. On this basis, this paper further proposes a secure deduplication method based on k-means clustering. First, the abstract-tags in cloud storage are clustered, and then the distance between the abstract-tags uploaded by users and the centroid is calculated. Then, the abstract-tags of the category with the closest distance are selected. Finally, duplicate data detection is performed only on the fingerprint-tags corresponding to these abstract-tags. In this way, the filtering speed of fingerprint-tags can be further accelerated. Experiments show that our method has higher performance than the secure deduplication method based on the autoencoder model.
distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the Compu...
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ISBN:
(数字)9781728166773
ISBN:
(纸本)9781728166773
distributed digital infrastructures for computation and analytics are now evolving towards an interconnected ecosystem allowing complex applications to be executed from IoT Edge devices to the HPC Cloud (aka the computing Continuum, the Digital Continuum, or the Transcontinuum). Understanding end-to-end performance in such a complex continuum is challenging. This breaks down to reconciling many, typically contradicting application requirements and constraints with low-level infrastructure design choices. One important challenge is to accurately reproduce relevant behaviors of a given application workflow and representative settings of the physical infrastructure underlying this complex continuum. In this paper we introduce a rigorous methodology for such a process and validate it through E2Clab. It is the first platform to support the complete analysis cycle of an application on the computing Continuum: (i) the configuration of the experimental environment, libraries and frameworks;(ii) the mapping between the application parts and machines on the Edge, Fog and Cloud;(iii) the deployment of the application on the infrastructure;(iv) the automated execution;and (v) the gathering of experiment metrics. We illustrate its usage with a real-life application deployed on the grid'5000 testbed, showing that our framework allows one to understand and improve performance, by correlating it to the parameter settings, the resource usage and the specifics of the underlying infrastructure.
Current scientific workflows are large and complex. They normally perform thousands of simulations whose results combined with searching and data analytics algorithms, in order to infer new knowledge, generate a very ...
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ISBN:
(纸本)9783030576752;9783030576745
Current scientific workflows are large and complex. They normally perform thousands of simulations whose results combined with searching and data analytics algorithms, in order to infer new knowledge, generate a very large amount of data. To this end, workflows comprise many tasks and some of them may fail. Most of the work done about failure management in workflow managers and runtimes focuses on recovering from failures caused by resources (retrying or resubmitting the failed computation in other resources, etc.) However, some of these failures can be caused by the application itself (corrupted data, algorithms which are not converging for certain conditions, etc.), and these fault tolerance mechanisms are not sufficient to perform a successful workflow execution. In these cases, developers have to add some code in their applications to prevent and manage the possible failures. In this paper, we propose a simple interface and a set of transparent runtime mechanisms to simplify how scientists deal with application-based failures in task-based parallel workflows. We have validated our proposal with use-cases from e-science and machine learning to show the benefits of the proposed interface and mechanisms in terms of programming productivity and performance.
An essential ingredient of the smart grid is software-based services. Increasingly, software is used to support control strategies and services that are critical to the grid's operation. Therefore, its correct ope...
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ISBN:
(纸本)9781728161273
An essential ingredient of the smart grid is software-based services. Increasingly, software is used to support control strategies and services that are critical to the grid's operation. Therefore, its correct operation is essential. For various reasons, software and its configuration needs to be updated. This update process represents a significant overhead for smart grid operators and failures can result in financial losses and grid instabilities. In this paper, we present a framework for determining the root causes of software rollout failures in the smart grid. It uses distributed sensors that indicate potential issues, such as anomalous grid states and cyber-attacks, and a causal inference engine based on a formalism called evidential networks. The aim of the framework is to support an adaptive approach to software rollouts, ensuring that a campaign completes in a timely and secure manner. The framework is evaluated for a software rollout use-case in a low voltage distribution grid. Experimental results indicate it can successfully discriminate between different root causes of failure, supporting an adaptive rollout strategy.
Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted d...
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ISBN:
(纸本)9781450397612
Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance computing platforms. The effective analysis of a DCGAN in Graphic Processing Unit and Tensor Processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central Processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic Processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.
The proceedings contain 67 papers. The topics discussed include: windsurfing with APPA: automating computational fluid dynamics simulations of wind flow using cloud computing;parallel comparison of huge DNA sequences ...
ISBN:
(纸本)9781728165820
The proceedings contain 67 papers. The topics discussed include: windsurfing with APPA: automating computational fluid dynamics simulations of wind flow using cloud computing;parallel comparison of huge DNA sequences in multiple GPUs with block pruning;accelerating deep learning using multiple GPUs and FPGA-based 10GbE switch;adaptive load balancing based on machine learning for iterative parallel applications;switching at flit level: a congestion efficient flow control strategy for network-on-chip;robustness and energy-elasticity of crown schedules for sets of parallelizable tasks on many-core systems with DVFS;and scalable parallel genetic algorithm for solving large integer linear programming models derived from behavioral synthesis.
distributed generators are typically interfaced to the grid via power electronic converters that are usually operated in pulse width modulation mode. This results in undesirable higher-order harmonics in currents and ...
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The increased usage of IoT, containerization, and multiple clouds not only changed the way IT works but also the way IT Operations, i.e., the monitoring and management of IT assets, works. Monitoring a complex IT envi...
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ISBN:
(纸本)9781728147161
The increased usage of IoT, containerization, and multiple clouds not only changed the way IT works but also the way IT Operations, i.e., the monitoring and management of IT assets, works. Monitoring a complex IT environment leads to massive amounts of heterogeneous context data, usually spread across multiple data silos, which needs to be analyzed and acted upon autonomously. However, for a holistic overview of the IT environment, context data needs to be consolidated which leads to several problems. For scalable and automated processes, it is essential to know what context is required for a given monitored resource, where the context data are originating from, and how to access them across the data silos. Therefore, we introduce the Monitoring Resource Model for the holistic management of context data. We show what context is essential for the management of monitored resources and how it can be used for context reasoning. Furthermore, we propose a multi-layered framework for IT Operations with which we present the benefits of the Monitoring Resource Model.
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