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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

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Flexible Load Collaborative Scheduling Method of Distribution Network Based on Particle Swarm Algorithm

Flexible Load Collaborative Scheduling Method of Distributio...

引用

2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Shang, Jiayi Zhao, Tianyu Li, Fei Zheng, Yamin Lu, Haibo Zhang, Zhipeng State Grid Hangzhou Power Supply Company Zhejiang Hangzhou China

ISBN: (纸本)9798350383348

With the change of energy structure and the continuous development of power system, new loads such as distributed energy and energy storage equipment are gradually connected to distribution network. These devices have certain adjustment ability, and can realize optimal control of distribution network through their cooperative scheduling. Flexible load cooperative dispatching method is a method to realize optimal operation of distribution system by dispatching flexible loads in distribution network. This paper will introduce the flexible load cooperative scheduling method in detail. © 2024 IEEE.

关键词： Collaborative scheduling distribution network Flexible loads particle swarm algorithm social behavior simulation theory

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distributed SMT Solving Based on Dynamic Variable-Level Partitioning 36th

Distributed SMT Solving Based on Dynamic Variable-Level Part...

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36th international conference on Computer-Aided Verification (CAV)

作者： Zhao, Mengyu Cai, Shaowei Qian, Yuhang Chinese Acad Sci Inst Software Key Lab Syst Software Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China

ISBN: (纸本)9783031656262;9783031656279

Satisfiability Modulo Theories on arithmetic theories have significant applications in many important domains. Previous efforts have been mainly devoted to improving the techniques and heuristics in sequential SMT solvers. With the development of computing resources, a promising direction to boost performance is parallel and even distributed SMT solving. We explore this potential in a divide-and-conquer view and propose a novel dynamic parallel framework with variable-level partitioning. To the best of our knowledge, this is the first attempt to perform variable-level partitioning for arithmetic theories. Moreover, we enhance the interval constraint propagation algorithm, coordinate it with Boolean propagation, and integrate it into our variable-level partitioning strategy. Our partitioning algorithm effectively capitalizes on propagation information, enabling efficient formula simplification and search space pruning. We apply our method to three state-of-the-art SMT solvers, namely CVC5, OpenSMT2, and Z3, resulting in efficient parallel SMT solvers. Experiments are carried out on benchmarks of linear and nonlinear arithmetic over both real and integer variables, and our variable-level partitioning method shows substantial improvements over previous partitioning strategies and is particularly good at non-linear theories.

关键词： Satisfiability Modulo Theories parallel computing Partitioning Strategy Interval Constraint Propagation

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Improved Efficiency Of Fog And Cloud computing Paradigms 2

Improved Efficiency Of Fog And Cloud Computing Paradigms

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2nd IEEE international conference on Advancements and Key Challenges in Green Energy and computing, AKGEC 2024

作者： Wakankar, Ronit Simhachalam, Sumanyu Ganesan, Raghavendran Velmurugan, T. Vellore Institute of Technology School of Electronics Engineering Sense Vellore India

ISBN: (纸本)9798350390179

For effective energy management in smart grids, this research suggests a FOG computing architecture that bridges the gap between the edge and CLOUD. Facing data congestion in both traditional edge and CLOUD computing, the system utilizes a two-tiered approach. By preprocessing and filtering sensor data from buildings, edge nodes reduce latency and network traffic. The FOG layer in turn effectively filters data for potential CLOUD processing while doing real time analysis and decision-making depending on immediate needs. With latency reduction of up to 98.7%, the simulation demonstrates significant benefits over CLOUD only approaches and enables real time energy adjustments that optimize resource utilization. Overall, scalability and efficient data handling overcome CLOUD limitations, while enhanced security minimizes reliance on a central CLOUD. This distributed brain for the grid paves the way for a smarter, more responsive energy landscape, powered by intelligence at the edge. © 2024 IEEE.

关键词： Fog computing

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Latency-Aware Placement of Stream Processing Operators

Latency-Aware Placement of Stream Processing Operators

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29th international conference on parallel and distributed computing (Euro-Par)

作者： Ecker, Raphael Karagiannis, Vasileios Sober, Michael Ebrahimi, Elmira Schulte, Stefan TU Wien Vienna Austria Austrian Inst Technol Ctr Digital Safety & Secur Vienna Austria TU Hamburg Christian Doppler Lab Blockchain Technol Internet Hamburg Germany

ISBN: (纸本)9783031506833;9783031506840

The rise of the Internet of Things and Fog computing has increased substantially the number of interconnected devices at the edge of the network. As a result, a large amount of computations is now performed in the fog generating vast amounts of data. To process this data in near real time, stream processing is typically employed due to its efficiency in handling continuous streams of information in a scalable manner. However, most stream processing approaches do not consider the underlying network devices as candidate resources for processing data. Moreover, many existing works do not take into account the incurred network latency of performing computations on multiple devices in a distributed way. Consequently, the fog computing resources may not be fully exploited by existing stream processing approaches. To avoid this, we formulate an optimization problem for utilizing the existing fog resources, and we design heuristics for solving this problem efficiently. Furthermore, we integrate our heuristics into Apache Storm, and we perform experiments that show latency-related benefits compared to alternatives.

关键词： Stream Processing Fog computing Edge computing Internet of Things Apache Storm

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Towards Efficient and Privacy-Preserving Hierarchical Federated Learning for distributed Edge Network 5th

Towards Efficient and Privacy-Preserving Hierarchical Federa...

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5th international conference on Blockchain and Trustworthy Systems, BlockSys 2023

作者： An, Ningyu Liang, Xiao Zhou, Fei Wang, Xiaohui Li, Zihan Feng, Jia Guan, Zhitao State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co.Ltd Beijing100209 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China State Grid Zhejiang Electric Power Company.Ltd Hangzhou310007 China

ISBN: (纸本)9789819981007

Federated learning is a promising paradigm that utilizes widely distributed devices to jointly train a machine learning model while maintaining privacy. However, when oriented to distributed resource-constrained edge devices, existing federated learning schemes still suffer from heterogeneity challenge. Hierarchical federated learning divides devices into clusters based on their resources to enhance training efficiency. In addition, there are also security concerns such as the risk of privacy leakage during the interactions intra and cross the layers. In this paper, we propose PHFL, a Privacy-Preserving Hierarchical Federated Learning Scheme, which includes a personalized task assignment protocol and a heterogeneous-friendly aggregation scheme, so as to achieve an efficient and low-cost training for resource-constrained edge devices. To address the heterogeneity challenge, we combine synchronous and asynchronous aggregation, and design different aggregation weights for device differences. On top of that, a privacy-preserving scheme based on threshold homomorphic encryption is designed to accommodate the privacy requirements of the proposed hierarchical federated learning scenario. The security analysis and performance evaluation demonstrate that this approach improves model generalizability and learning efficiency with acceptable overhead, while also providing privacy protection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Edge computing

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Optimizing a Super-fast Eigensolver for Hierarchically Semiseparable Matrices 24

Optimizing a Super-fast Eigensolver for Hierarchically Semis...

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53rd international conference on parallel Processing (ICPP)

作者： Josyula, Abhishek Verma, Pritesh Gaonkar, Amar Barua, Amlan Hegde, Nikhil Indian Inst Technol Dharwad Belur Karnataka India Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9798400717932

In this paper, we consider the efficient computation of all eigenvalues and eigenvectors of Symmetric Hierarchically Semiseparable (HSS) matrices, which have an inherent structure: the off-diagonal blocks have hierarchical bases and have low ranks. State-of-the-art is a divide-conquer algorithm, SuperDC, to compute eigenvectors and eigenvalues in an order of magnitude faster than popular and commercial solvers. We improve on the state-of-the-art and present novel shared- and distributed-memory parallel algorithms for computing eigenvalues of HSS matrices. We take advantage of the recursive divide-conquer approach employed in SuperDC to parallelize the eigenvalue computation, present a span and available parallelism analysis, and optimize the original SuperDC algorithm to reduce the storage requirement from O(N-2) to O(N) in the case of banded matrices. We do a systematic evaluation with different parallel programming paradigms, scheduling policies, and scalability configurations. We observe that in the shared-memory parallel implementations, OpenMP implementations perform better than Cilk versions, work stealing offers no significant performance advantage, and in the distributed-memory implementations, asynchronous communication yields better performance than implementation with barrier-based communication. We find the optimal input decomposition at which the parallel implementations provide the best speedup. For input symmetric matrices of different sparsity structures and sizes ranging from 4096 to 256k rows, on up to 512 cores, the implementations scale well and show a significant speedup of up to 147x compared to the available SuperDC implementation.

关键词： Symmetric eigenvalue problems hierarchically semi-separable matrix divide-and-conquer SuperDC parallel

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Research on Fine-Grained Task Offloading in Mobile Edge computing 4

Research on Fine-Grained Task Offloading in Mobile Edge Comp...

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4th international conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Wang, Yaping Zhao, Shuxu School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730000 China

ISBN: (纸本)9798350337181

Edge computing is considered a promising architecture for handling latency-sensitive and computationally intensive tasks. The lack of consideration for the timing of jobs and their unique topology in the existing research on task scheduling in mobile edge computing settings results in performance deterioration and underutilization of edge servers. In this paper, we provide a two-tier, lightweight offloading mechanism. by carefully taking into account: 1) the job topology, 2) the task urgency, and 3) the rivalry for server resources. Simulation findings show that, as compared to baseline techniques, our proposed approach considerably increases application completion rates while reducing average system completion delay by 66.7% and 37.6% under various user and edge server counts. © 2023 IEEE.

关键词： Mobile edge computing

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

20th IFIP WG 10.3 International Conference on Network and Pa...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

ISBN: (纸本)9789819628636

The proceedings contain 76 papers. The special focus in this conference is on Network and parallel computing. The topics include: AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism;DaCP: Accelerating Synchronization-Free SpTRSV via GPU-Friendly Data Communication and parallelism Strategies;Diagnosability of the Lexicographic Product of Paths and Complete Bipartite Graphs Under PMC Model;DTuner: A Construction-Based Optimization Method for Dynamic Tensor Operators Accelerating;Efficient Implementation of the LOBPCG Algorithm on a CPU-GPU Cluster;HP-CSF: An GPU Optimization Method for CP Decomposition of Incomplete Tensors;JediGAN: A Fully Decentralized Training of GAN with Adaptive Discriminator Averaging and Generator Selection;optimizing Vo-Viso: A Modified Methodology to parallel computing with Isolating Data in Memristor Arrays;parallel Computation of the Combination of Two Point Operations in Conic Curves Cryptosystem over GF(2n) Using Tile Self-assembly;parallel Construction of Independent Spanning Trees on 3-ary n-cube Networks;SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling;swDarknet: A Heterogeneous parallel Deep Learning Framework Suitable for SW26010 Pro Processor;VConv: Autotiling Convolution Algorithm Based on MLIR for Multi-core Vector accelerators;ACH-Code: An Efficient Erasure Code to Reduce Average Repair Cost in Cloud Storage Systems of Multiple Availability Zones;CMS: A Computility Resource Status Management and Storage Framework;fast Memory Disaggregation with SwiftSwap;HASLB: Huge Page Allocation Strategy Optimized for Load-Balance in parallel computing Programs;lightFinder: Finding Persistent Items with Small Memory;miDedup: A Restore-Friendly Deduplication Method on Docker Image Storage Systems;SPLR: A Selective Packet Loss Recovery for Improved RDMA Performance;a Cluster-Based Platoon Formation Scheme for Realistic Automated Vehicle Platooning;AnaNET: Anatomical Network fo

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Optimization of Economic Load Dispatch for distributed Energy System Using Metaheuristic Method

Optimization of Economic Load Dispatch for Distributed Energ...

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2024 international conference on Engineering and computing, ICECT 2024

作者： Khan, Usama Tariq Zia, Muhammad Fahad Lahore University of Management Sciences Department of Electrical Engineering Lahore Pakistan American University in Dubai Department of Electrical and Computer Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350349719

The increasing demand for electricity has led to the use of multiple energy generation sources. Among these sources, renewable energy is favored due to its environmentally friendly nature. The integration of distributed generation sources with the conventional grid makes the system highly dense and complex. Different heuristic methods are commonly used to determine the optimal or near-optimal operational strategy of a distributed system while maintaining system constraints. To address this issue, a meta-heuristic method based on genetic algorithms (GA) is proposed in this paper to solve the economic load dispatch problem for a distributed energy system that incorporates both thermal and photovoltaic (PV) systems. In addition, the prohibited-operating zones of the thermal generators are considered. The power output of the PV system for each subinterval is calculated by considering the irradiance and temperature values in that interval. The algorithm is tested for a daily load of 700 MW and the optimal share of each generator for different scheduling hours is determined. To validate the proposed algorithm, it is compared with benchmarking methods such as particle swarm optimization, quadratic programming, and lambda iteration. The results indicate that the proposed algorithm is effective in reducing operating costs and has a faster convergence rate than benchmark methods. © 2024 IEEE.

关键词： Genetic algorithms

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Scheduling Big Machine Learning Tasks on Clusters of Heterogeneous Edge Devices 17

Scheduling Big Machine Learning Tasks on Clusters of Heterog...

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17th international conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Mathkar, Saumya Aiyer, Shreya Bapat, Yashovardhan Pinki, Pinki Paul, Arnab K. Naik, Vinayak BITS Pilani Department of CSIS Goa India

ISBN: (纸本)9798331531195

Edge computing has transformed machine learning by using computing closer to the data sources, thereby reducing latency. The ever-increasing volume of data has necessitated forming clusters of edge devices, possibly with heterogeneous capabilities. Managing heterogeneous resources such as computation and memory remains challenging. Given the capabilities of the edge devices, we need a simple technique suitable for an Edge computing *** introduce a scheduling mechanism that leverages Integer Linear Programming (ILP) to optimize the overall computation time of ML-based tasks. We implement our scheduling mechanism that efficiently allocates resources, ensuring tasks are executed parallel across the cores of edge devices in a cluster by minimizing computation time. For tasks large enough to fit on any core, we leverage distributed learning to train the model in pieces and later combine them. We employ our ILP-based scheduler for efficient task allocation and compare its performance with a Greedy, simple approach based on best-fit technique. We evaluate our approach on three cases of sets of tasks on the spectrum of uniformity and size. Our results demonstrate two times the speed gain for our ILP-based approach over Greedy for the category with the least uniformity and large size. © 2025 IEEE.

关键词： distributed computing Edge computing Optimization

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