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Research on Real-Time Processing and Efficient Sharing Program of Power Measurement Data

Research on Real-Time Processing and Efficient Sharing Progr...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Cuicui Zhang Jing Zhang Jiali Sun Qi Sun Information and Telecommunication Branch State Grid Anhui Electric Power Co. Ltd Hefei Anhui China

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

In modern power systems, power measurement data is a key basis for monitoring, analyzing, and making decisions regarding operational status. This paper proposes a solution to improve the performance of power measurement data processing and sharing in response to the high requirements of the power system. By integrating a real-time database, the real-time data processing module preprocesses the measurement data based on key information. parallel computing architecture and ADMM algorithm are used to realize information sharing and collaboration between data streams. The accuracy of state variable estimation is improved by introducing a weighted M-P generalized inverse matrix using iterative methods and linearization. In terms of data processing speed, the model in this paper, in terms of data sharing efficiency, the average response time of the optimized sharing scheme is shortened to 0.5 seconds, and the data transfer rate is increased to 1000 Mbps. the average response time of the optimized sharing scheme is shortened from 5 seconds to 0.5 seconds, and the data transfer rate is increased from 100 Mbps to 1000 Mbps. in terms of user experience, the optimized power measurement In terms of user experience, the optimized Power Measurement solution has an average satisfaction score of more than 90, and the user operation steps have been simplified to three. It not only improves the data processing capability of the power measurement system, but also enhances the system sharing level.

关键词： Analytical models Power measurement Accuracy Computational modeling Computer architecture Real-time systems Data models User experience Power systems Time factors

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

引用

Annual conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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Ponder: Online Prediction of Task Memory Requirements for Scientific Workflows

Ponder: Online Prediction of Task Memory Requirements for Sc...

引用

IEEE international conference on e-Science and grid computing

作者： Fabian Lehmann Jonathan Bader Ninon De Mecquenem Xing Wang Vasilis Bountris Florian Friederici Ulf Leser Lauritz Thamsen Humboldt-Universität zu Berlin Germany Technische Universität Berlin Germany University of Glasgow United Kingdom

ISBN: (数字)9798350365610

ISBN: (纸本)9798350365627

Scientific workflows are used to analyze large amounts of data. These workflows comprise numerous tasks, many of which are executed repeatedly, running the same custom program on different inputs. Users specify resource allocations for each task, which must be sufficient for all inputs to prevent task failures. As a result, task memory allocations tend to be overly conservative, wasting precious cluster resources, limiting overall parallelism, and increasing workflow *** this paper, we first benchmark a state-of-the-art method on four real-life workflows from the nf-core workflow repository. This analysis reveals that certain assumptions underlying current prediction methods, which typically were evaluated only on simulated workflows, cannot generally be confirmed for real workflows and executions. We then present Ponder, a new online task-sizing strategy that considers and chooses between different methods to cater to different memory demand patterns. We implemented Ponder for Nextflow and made the code publicly available. In an experimental evaluation that also considers the impact of memory predictions on scheduling, Ponder improves Memory Allocation Quality on average by 71.0% and makespan by 21.8% in comparison to a state-of-the-art method. Moreover, Ponder produces 93.8% fewer task failures.

关键词： Limiting Codes Memory management Predictive models parallel processing Benchmark testing Size measurement

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An Optimized Scheduling Algorithm for the Multi-channel Hyperledger Fabric

An Optimized Scheduling Algorithm for the Multi-channel Hype...

引用

IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Ou Wu Shanshan Li He Zhang Liwen Liu Yanze Wang Haoming Li State Key Laboratory of Novel Software Technology Nanjing University China Software Institute Nanjing University China

Hyperledger Fabric (Fabric for short), a typical consortium blockchain platform, has released many versions that support different consensus protocols to ensure that the system can withstand faults (such as crashes, network partitions, or network shutdowns). However, if there is a malicious node in the system, Fabric cannot guarantee any behavior of the malicious node. Therefore, when a certain number of nodes are attacked simultaneously, it directly affects the security and efficiency of the entire system. In the face of multi-channel Fabric network architecture, the parallel processing of transactions by node groups linked on different channels significantly improves the throughput of the Fabric network. However, it may also cause system congestion and safety problems. To solve this problem, this paper designs a Lyapunov-based scheduling algorithm for the performance optimization of Fabric. When considering the system's security probability and queue accumulation, we give the maximum consensus verification rate under the minimum security probability guarantee. Finally, we installed the latest stable version v2.0 of Fabric on the cloud server to verify the algorithm's effectiveness.

关键词： Scheduling algorithms distributed ledger Network architecture Throughput Fabrics Scheduling Peer-to-peer computing

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Peer-to-Peer Energy Trading with Mid-Market Rate among Virtue Power Plants by Multi-Agent Deep Reinforcement Learning

Peer-to-Peer Energy Trading with Mid-Market Rate among Virtu...

引用

Electrical Engineering and Control Technologies (CEECT), conference on

作者： Tong Ye Weijia Yang Shihao Liu Yuping Huang Yuyao Yang Feng Pan School of Energy Science and Engineering University of Science and Technology of China Hefei China Chinese Academy of Sciences Guangzhou Institute of Energy Conversion Guangzhou China Metrology Center of Guangdong Power Grid Co. Ltd Qingyuan China

ISBN: (数字)9798331528096

ISBN: (纸本)9798331528102

Efficient energy allocation and utilization are critical challenges in the evolving landscape of decentralized power systems integrated with renewable energy sources. Peer-toPeer (P2P) energy trading offers a novel framework for direct energy transactions among prosumers, enhancing system flexibility and market responsiveness. This paper addresses the complex decision-making processes inherent in P2P energy markets by proposing a Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm. The proposed model leverages Virtual Power Plants (VPPs) to aggregate distributed energy resources, enabling autonomous and optimized energy trading within the P2P framework. VPPs act as intermediaries, enhancing market liquidity, optimizing energy distribution, and ensuring supply reliability. The MATD3-based approach effectively manages high-dimensional continuous action spaces and adapts to the dynamic nature of P2P energy trading. Simulation results indicate that the proposed MADRL model reduces overall energy costs and enhances the performance of VPPs in decentralized energy markets. This research contributes to the advancement of intelligent P2P energy trading mechanisms, emphasizing the pivotal role of reinforcement learning in developing sustainable and resilient energy infrastructures

关键词： Adaptation models Costs Aggregates Simulation Pricing Virtual power plants Deep reinforcement learning Peer-to-peer computing Power system reliability Reliability

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Simultaneously Learning Syntactic Dependency and Semantics Reasonability for Relation Extraction

Simultaneously Learning Syntactic Dependency and Semantics R...

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international conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Wang, Xin Yin, Nan Zhang, Xiang Bai, Xinyi Luo, Zhigang College of Computer National University of Defense Technology Changsha China Institute for Quantum and State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

ISBN: (纸本)9789811669620

Relation extraction as an important Natural Language Processing (NLP) task is to identify relations between named entities in text. Recently, graph convolutional networks over dependency trees have been widely used to capture syntactic features and achieved attractive performance. However, most existing dependency-based approaches ignore the positive influence of the words outside the dependency trees, sometimes conveying rich and useful information on relation extraction. In this paper, we propose a novel model, Entity-aware Self-attention Contextualized GCN (ESC-GCN), which efficiently incorporates syntactic structure of input sentences and semantic context of sequences. To be specific, relative position self-attention obtains the overall semantic pairwise correlation related to word position, and contextualized graph convolutional networks capture rich intra-sentence dependencies between words by adequately pruning operations. In this way, our proposed model not only reduces the noisy impact from dependency trees but also obtains easily-ignored entity-related semantic representation. Extensive experiments demonstrate that our model achieves encouraging performance. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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HCL: Distributing parallel Data Structures in Extreme Scales 22

HCL: Distributing Parallel Data Structures in Extreme Scales

引用

IEEE international conference on Cluster computing

作者： Devarajan, Hariharan Kougkas, Anthony Bateman, Keith Sun, Xian-He Illinois Inst Technol Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781728166773

Most parallel programs use irregular control flow and data structures, which are perfect for one-sided communication paradigms such as MPI or PGAS programming languages. However, these environments lack the presence of efficient function-based application libraries that can utilize popular communication fabrics such as TCP, Infinity Band (IB), and RDMA over Converged Ethernet (RoCE). Additionally, there is a lack of high-performance data structure interfaces. We present Hermes Container Library (HCL), a high-performance distributed data structures library that offers high-level abstractions including hash-maps, sets, and queues. HCL uses a RPC over RDMA technology that implements a novel procedural programming paradigm. In this paper, we argue a RPC over RDMA technology can serve as a high-performance, flexible, and co-ordination free backend for implementing complex data structures. Evaluation results from testing real workloads shows that HCL programs are 2x to 12x faster compared to BCL, a state-of-the-art distributed data structure library.

关键词： distributed Data Structure RPC over RDMA Hybrid Data Access Model HPC Data Containers

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Fast and Scalable Gate-Level Simulation in Massively parallel Systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE international conference on Computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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Research and Implementation of Secure Storage Technology of Private Cloud Based on Hadoop

Research and Implementation of Secure Storage Technology of ...

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Networking, Communications and Information Technology (NetCIT), international conference on

作者： Meirong Lin School of Information Engineering Hainan Vocational University of science and technology Haikou Hainan China

In recent years, with the rapid development of cloud computing industry, big data processing technology is also maturing. At the same time, the scale of domestic mobile Internet market is constantly expanding, the number of users has exceeded 500 million, and it has brought a huge amount of mobile Internet traffic data. Private cloud storage has become the best choice to solve the current enterprise security storage needs because of the unsafe factors such as data leakage, loss and storage instability in public cloud storage. It is an implementation of private cloud computing. Compared with public cloud, it has higher security and lower investment cost. From the current situation of mobile Internet, on the one hand, the surge of mobile data traffic has brought huge operating pressure to operators, which requires them to invest more funds in network construction and upgrading. On the other hand, due to the growth of mobile data services, traditional voice messaging and other services have declined. The cloud storage system can provide massive storage capacity to solve the problem of big data storage in the smart grid. With the development of cloud computing technology, cloud security becomes more and more critical. In the cloud computing environment, users lose control over the data and computing placed in the cloud server, and are uncertain whether the data is protected and whether the computing task is executed correctly. Therefore, in the smart grid cloud storage system, the problem of data security is an urgent problem to be solved.

关键词： Computers Cloud computing distributed databases Surge protection Big Data Smart grids Servers

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Fog computing enabled Smart grid Blockchain Architecture and Performance Optimization with DRL Approach 8

Fog Computing enabled Smart Grid Blockchain Architecture and...

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8th IEEE international conference on Computer Science and Network Technology (ICCSNT)

作者： Zheng, Weijun Wang, Wenhua Wu, Guoqing Xue, Chenzi Wei, Yifei State Grid Zhejiang Elect Power Co Ltd Jiaxing Power Supply Co Jiaxing Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China

ISBN: (纸本)9781728181233

Smart grid is willing to make full advantage of distributed clean energy to alleviate energy crisis and environmental problems. However, distributed renewable energy is usually invisible and uncontrollable for the current power system, and there are intermittent problems in its generation. Therefore, how to achieve the power balance, maintain safe operation, and ensure the reliability and quality of power supply when the distributed energy reaches a high penetration in the grid is a huge challenge. Blockchain as one of the research hotspots brings about new solution approach to the dilemma. The two fields have many commons on decentralization, autonomy, marketization and intelligence. In this paper, we discuss the feasible scheme of the integrated system and add fog computing to reduce costs. Considering the realization of system, we choose Hyper Fabric as the basic structure and add verifiable random function to the consensus aimed to improve randomness and security in the encrypted election. Meanwhile, in order to satisfy the business requirements, a flexible adjustment method based on Deep Q Learning algorithm is designed to realize the joint optimization of throughput, latency and storage cost. The proposed scheme provides the advantages including privacy, flexibility, extensibility and implantation simplicity.

关键词： Smart grid Blockchain Consensus mechanism Fog computing Deep reinforcement learning

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