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Blockchain-Based distributed Resource Sharing Model Design 3

Blockchain-Based Distributed Resource Sharing Model Design

引用

3rd international conference on Computer Science and Blockchain, CCSB 2023

作者： Zheng, Shanqi Huang, Xiaobo Wei, Ming Qu, Yingshi Wang, Huiying State Grid Liaoning Information and Communication Company Shenyang China State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China

ISBN: (纸本)9798350359077

Computer resource sharing provides an effective solution to the problems of imbalance between the distribution of computer resources and computing power demand in the network and the waste of computer resources. In the power system, using computers for resource sharing can dispatch power resources and effectively solve various problems caused by power consumption peaks, thus achieving the purpose of saving resources and improving users' experience. However, the traditional computerized resource sharing system generally adopts the task allocation method, which makes it difficult for users to directly access the shared resources and participate in the whole scheduling process when they use electricity. In addition, the assignment and scheduling of tasks are managed by the central node, and once the central node is damaged, the normal operation of the resource sharing system will be unsustainable. To address the above problems, this paper proposes a distributed resource sharing model by introducing a block chain private chain to build the underlying architecture of the resource sharing model and provide shared resources directly to users in a P2P manner. This paper analyzes the key implementation mechanism of the resource sharing model and builds a distributed computing network to truly realize computer resource sharing. After verifying and analyzing the CPU and memory usage during the work of the resource sharing model, and combining the electricity consumption data of users in various regions and time nodes, it is verified that the model can effectively improve the efficiency of computer resources usage, enhance the overall computing capacity, and finally achieve the effective utilization of electricity resources. © 2023 IEEE.

关键词： computing power

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Teaching parallel Algorithms Using the Binary-Forking Model

Teaching Parallel Algorithms Using the Binary-Forking Model

引用

1st international conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Blelloch, Guy E. Gu, Yan Sun, Yihan Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Calif Riverside Riverside CA 92521 USA

ISBN: (纸本)9798350364613;9798350364606

In this paper, we share our experience in teaching parallel algorithms with the binary-forking model. With hardware advances, multicore computers are now ubiquitous. This has created a substantial demand in both research and industry to harness the capabilities of parallel computing. It is thus important to incorporate parallelism in computer science education, especially in the early stages of the curriculum. However, it is commonly believed that understanding and using parallelism requires a deep understanding of computer systems and architecture, which complicates introducing parallelism to young students and non-experts. We propose to use the binary-forking model in teaching parallel algorithms, proposed by our previous research work. This model is meant to capture the performance of algorithms on modern multicore shared-memory machines, which is a simple abstraction to isolate algorithm design ideas with system-level details. The abstraction allows for simple analysis based on the work-span model in theory, and can be directly implemented as parallel programs in practice. In this paper, we briefly overview some basic primitives in this model, and provide a list of algorithms that we believe are well-suited in parallel algorithm courses.

关键词： binary-forking model computer science education fork-join parallel algorithms parallel programming

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Assessing the Performance of Docker in Docker Containers for Microservice-based Architectures 32

Assessing the Performance of Docker in Docker Containers for...

引用

32nd Euromicro international conference on parallel, distributed and Network-Based Processing (PDP)

作者： Bedinotto Fava, Felipe Laviola Leite, Luiz Felipe Alves da Silva, Luis Fernando da Silva Amalfi Costa, Pedro Ramires Diniz Nogueira, Angelo Gaspar Gobus Lopes, Amanda Fagundes Schepke, Claudio Luis Kreutz, Diego Brandao Mansilha, Rodrigo Fed Univ Pampa UNIPAMPA Lab Adv Studies Computat LEA Grad Software Engn PPGES Alegrete Brazil

ISBN: (纸本)9798350363074;9798350363081

We provide a comprehensive and updated assessment of Docker versus Docker in Docker (DinD), evaluating its impact on CPU, memory, disk, and network. Using different workloads, we evaluate DinD's performance across distinct hardware platforms and GNU/Linux distributions on cloud Infrastructure as a Service (IaaS) platforms like Google Compute Engine (GCE) and traditional server-based environments. We developed an automated tools suite to achieve our goal. We execute four well-known benchmarks on Docker and its nested-container variant. Our findings indicate that nested-containers require up to 7 seconds for startup, while the Docker standard containers require less than 0.5 seconds for Debian and Alpine operating systems. Our results suggest that Docker containers based on Debian consistently outperform their Alpine counterparts, showing lower CPU latency. A key distinction among these Docker images lies in the varying number of installed libraries (e.g., stretching from 13 to 119) across different Linux distributions for the same system (e.g., MySQL). Furthermore, the number of events and CPU latency indicates that the influence of DinD over Docker proves that it is insignificant for both operating systems. In terms of memory, running containers of Debian-based images consume 20% more size of memory than those based on Alpine. No significant differences are between nested-containers and Dockers for disk and network IO. It is worth emphasizing that some of the disparities, such as a bigger memory footprint, appear to be a direct result of the software stack in use, including different kernel versions, libraries, and other essential packages.

关键词： Docker in Docker DinD Docker Containers Nested-container Performance Evaluation Benchmarks Cloud computing

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Research on new power system research and development enterprise contribution evaluation model based on FUCOM-BWM and improved TOPSIS 24

Research on new power system research and development enterp...

引用

2024 international Academic conference on Edge computing, parallel and distributed computing, ECPDC 2024

作者： Zhang, Liang Ge, Zhicheng Yin, Tieqiang Jin, Gang Jiang, Nannan Cai, Yunlong Li, Bingkang State Grid Jilin Electric Power Co Changchun China Department of Economic Management North China Electric Power University Beijing China

ISBN: (纸本)9798400718168

Enterprises are the main body of new power system research and development, and scientific evaluation of enterprise contribution can help stimulate the innovation vitality and enthusiasm of enterprises, and ensure the efficiency and sustainability of multi-party joint research and development of new power system. In this paper, an evaluation model based on FUCOM-BWM and improved TOPSIS is proposed for evaluating the contribution of enterprises in research and development of new electric power system. Firstly, the evaluation index system is constructed from the dimensions of industry status, achievement output, resource input, and business level;secondly, the FUCOM-BWM method is utilized to assign weights to the evaluation indexes, which overcomes the problems of inconsistent expert preference and chance in the traditional method. Finally, combined with the improved TOPSIS method, the contributions of the eight participating enterprises in the field of large-tonnage insulators are ranked, thus providing a more scientific reference basis for decision makers. The results show that continuing to strengthen the construction of enterprise technological innovation capacity, increasing professional and technical inputs, and continuously improving the quantity and quality of enterprise scientific research outputs is an effective way for enterprises to enhance their own contribution in the scientific research and development activities of new power systems. This paper verifies through examples that the proposed evaluation model has good adaptability and high accuracy, and provides new ideas for the evaluation of the contribution degree of enterprises in the scientific research and development of new electric power system. © 2024 ACM.

关键词： contribution evaluation improved TOPSIS new power system portfolio assignment research and development participating enterprises

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FedGB: Federated Gradient Boosting for Robust Autism Detection 15

FedGB: Federated Gradient Boosting for Robust Autism Detecti...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Dhar, Akanksha Gupta, Ishita Anuradha, J. Vellore Institute of Technology Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

Autism Spectrum Disorder (ASD) presents substantial problems in cognitive, language, and social skills. Early detection is critical for effective intervention, spurring research into Artificial Intelligence (AI) approaches for ASD detection. Federated Learning (FL) is a promising way to improve accuracy and privacy by training models jointly across distributed datasets. This study presents 'FedGB,' a Federated Gradient Boosting (FedGB) strategy that addresses the drawbacks of existing methods and improves ASD identification. Using FL and Gradient Boosting methods, 'FedGB' combines gridSearchCV for hyperparameter tweaking to ensure optimal performance and secrecy. Extensive experimentation on varied ASD datasets demonstrates higher performance compared to existing approaches, demonstrating 'FedGB''s effectiveness and dependability in improving ASD detection while maintaining privacy. © 2024 IEEE.

关键词： Autism Spectrum Disorder Epsilon Federated Learning Gradient Boost Classifier grid Search CV Machine Learning

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Space and Move-optimal Arbitrary Pattern Formation on a Rectangular grid by Robot Swarms 24

Space and Move-optimal Arbitrary Pattern Formation on a Rect...

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25th international conference on distributed computing and Networking, ICDCN 2024

作者： Sharma, Avisek Ghosh, Satakshi Goswami, Pritam Sau, Buddhadeb Department of Mathematics Jadavpur University West Bengal Kolkata India Jadavpur University West Bengal Kolkata India

ISBN: (纸本)9798400716737

Arbitrary pattern formation (Apf) is a well-studied problem in swarm robotics. To the best of our knowledge, the problem has been considered in two different settings: one in a euclidean plane and another in an infinite grid. This work deals with the problem in an infinite rectangular grid setting. The previous works in literature dealing with the Apf problem in an infinite grid had a fundamental issue. These deterministic algorithms use a lot of space in the grid to solve the problem, mainly to maintain the asymmetry of the configuration or to avoid a collision. These solution techniques cannot be useful if there is a space constraint in the application field. In this work, we consider luminous robots (with one light that can take three colors) to avoid symmetry, but we carefully designed a deterministic algorithm that solves the Apf problem using the minimal required space in the grid. The robots are autonomous, identical, and anonymous, and they operate in Look-Compute-Move cycles under a fully asynchronous scheduler. The Apf algorithm proposed in [2] by Bose et al. can be modified using luminous robots so that it uses minimal space, but that algorithm is not move-optimal. The algorithm proposed in this paper not only uses minimal space but is also asymptotically move-optimal. The algorithm proposed in this work is designed for an infinite rectangular grid, but it can be easily modified to work on a finite grid as well. © 2024 ACM.

关键词： Robots

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PTQ-SO: A Scale Optimization-based Approach for Post-training Quantization of Edge computing 27

PTQ-SO: A Scale Optimization-based Approach for Post-trainin...

引用

27th international conference on Computer Supported Cooperative Work in Design (CSCWD)

作者： Liu, Kangkang Chen, Ningjiang Guangxi Univ Coll Comp & Elect Informat Nanning Peoples R China Guangxi Univ Educ Dept Guangxi Zhuang Autonomous Reg Key Lab Parallel Distributed & Intelligent Comp Nanning Peoples R China

ISBN: (纸本)9798350349184;9798350349191

With the increasing performance of deep convolutional neural networks, they have been widely used in many computer vision tasks. However, a huge convolutional neural network model requires a lot of memory and computing resources, which makes it difficult to meet the requirements of low latency and reliability of edge computing when the model is deployed locally on resource-limited devices in edge environments. Quantization is a kind of model compression technology, which can effectively reduce model size, calculation cost and inference delay, but the quantization noise will cause the accuracy of the quantization model to decrease. Aiming at the problem of precision loss caused by model quantization, this paper proposes a post-training quantization method based on scale optimization. By reducing the influence of redundant parameters in the model on the quantization parameters in the process of model quantization, the scale factor optimization is realized to reduce the quantization error and thus improve the accuracy of the quantized model, reduce the inference delay and improve the reliability of edge applications. The experimental results show that under different quantization strategies and different quantization bit widths, the proposed method can improve the accuracy of the quantized model, and the absolute accuracy of the optimal quantization model is improved by 1.36%. The improvement effect is obvious, which is conducive to the application of deep neural network in edge environment.

关键词： deep neural networks edge computing scale optimization post-training quantization edge application

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distributed PV Management Based on Edge-cloud computing Model of Smart Distribution Transformer Terminal Unit 7

Distributed PV Management Based on Edge-cloud Computing Mode...

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2022 7th international Seminar on Computer Technology, Mechanical and Electrical Engineering, ISCME 2022

作者： Shao, Yinchi Zong, Jin Lin, Jiayin Chen, Can State Grid Jibei Electric Power Research Institute Beijing100045 China State Grid Jibei Electric Power Company Limited Beijing100045 China China Electric Power Research Institute Beijing China

With the growing installation of distributed PV at a low voltage level and the resulting concerns about good power quality, power facilities see a greater need for energy management programs, especially for flexible distributed PV management. In the dawn of a new generation of ICT technologies, an edge-cloud computing model widely applied to smart distribution transformer terminal units (SD-TTU) is proposed to achieve lifecycle management of power facilities. Firstly, existing distribution management systems' current practices and limitations are presented. According to the development trend, the reconstructed computing model involves three layers, and data interaction among the cloud layer, edge layer, and device layer is described. To support the huge scale of sensed data integration and transmission, the architecture of the edge node is established. Then the technical details for the distributed PV management are presented, including equipment configuration, main monitoring information, and specific procedures. © Published under licence by IOP Publishing Ltd.

关键词： Cloud computing

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Stochastic Large-scale Machine Learning Algorithms with distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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2024 IEEE international conference on Big Data, BigData 2024

作者： Fang, Biyi Klabjan, Diego Vo, Truong Northwestern University Dept. of Engineering and Applied Mathematics EvanstonIL60208 United States Northwestern University Dept. of Industrial Engineering and Management Sciences EvanstonIL60208 United States Northwestern University Dept. of Machine Learning and Data Science EvanstonIL60208 United States

ISBN: (纸本)9798350362480

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm. © 2024 IEEE.

关键词： Contrastive Learning

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Design of distributed Power Planning Decision Algorithm for Transmission and Distribution Networks Based on Neural Network Model

Design of Distributed Power Planning Decision Algorithm for ...

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2023 international conference on Internet of Things, Robotics and distributed computing, ICIRDC 2023

作者： Zhang, Yu Wang, Rong Luo, Chen Li, Qingsheng Power Grid Planning Research Center of Guizhou Power Grid Guizhou Guiyang550000 China

ISBN: (纸本)9798350375275

DG (distributed Generation) planning is a key technology in the construction of intelligent distribution networks. The large-scale integration of DGs into the distribution network will cause changes in its power flow and affect the normal operation of the network. The location and capacity are two important factors that determine the degree of impact of DG integration on the power system. In the continuous adjustment of the power system, voltage regulation, reactive power balance, and relay protection devices all affect the operational stability of the power system. This article proposes a decision-making algorithm for DG planning in transmission and distribution networks based on the NN (Neural Network) model. The research results show that when training the fitting model, the training time of GA (Genetic Algorithm) and ACA (Ant Colony Algorithm) is longer than that of the algorithm in this paper, which is 22.81S and 18.31S, respectively. Comparing the results, it can be seen that the regression fitting speed of the algorithm in this paper is better than that of GA and ACA., Taking the economic indicators, power quality indicators, and environmental protection indicators of DG as the objective functions, and considering the temporal characteristics of DG in the planning process, this algorithm is used to obtain a non inferior solution set and the ideal point method is used to obtain typical planning schemes. © 2023 IEEE.

关键词： Neural network models

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