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

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Multi-Objective Optimization Task Scheduling Method Based on Dynamic Programming for Multi-Cloud Environment 4

Multi-Objective Optimization Task Scheduling Method Based on...

引用

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

作者： Chen, Xueyang College of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350337181

Cloud computing is one of the most successfull technologies for providing services on request via the Internet, bringing extreme convenience to customers and businesses with its advantages of hyperscale, modernisation, reliability, universality, high scaleability and payment on request. However, with the growth of services for managing content distribution and interactive computing, such as social networking and scientific processes online, the capacity of cloud data centers is limited and cannot meet the business needs during peak hours. To handle this huge data volume, multiple cloud systems that has been introduced to pool multiple clouds together to provide a single set of services in a collaborative manner. Aiming at the resource allocation problem in cloudy environments, this paper proposes a method of multi-objective task optimization scheduling based on dynamic programming, constructs a scheduling model of time and cost, and designs an efficient multiobjective optimisation approach is used to address the module. The method first describes the resource allocation process in a multi-cloud environment, then establishes a progress model for multi-user tasks in a cloud environment, then formalises the multi-user task optimisation problem and designs a solution algorithm based on a dynamic programming approach, and finally conducts simulation experiments to evaluate the effectiveness of the method. The experimental results show that the method can achieve time and cost optimisation in the scheduling of multi-user tasks. © 2023 IEEE.

关键词： Cloud computing

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RIMR: Reverse Influence Maximization Rank

RIMR: Reverse Influence Maximization Rank

引用

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

作者： Vap, Jay Kogge, Peter Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9798350364613;9798350364606

The ubiquitous usage of social media and other internet platforms has led to the widespread proliferation of naturally occurring social networks. This has caused a revolution in marketing and information dissemination due to the rise of influencers, and their innate ability to broadcast information to large audiences. Influence maximization, a key problem in identifying optimal influential individuals, can be used to identify influencers in a social network, or can be superimposed on other settings which exhibit relational information. Popular methods, such as greedy algorithms, face scalability issues, performing poorly on larger networks, and incurring a large memory overhead. To address these challenges, this paper introduces a novel methodology, RIMR, which leverages statistical modeling and parallel programming to circumvent the need for storing large collections of data, achieving scalability and efficiency. Experimental results demonstrate the effectiveness and scalability of the proposed approach compared to state-of-the-art methods, showcasing significant speedups. This paper provides insights into the development of the proposed statistical process, the heterogeneous algorithm implementation, and a prototype demonstrating its efficacy.

关键词： GPUs Heterogeneous Architectures Influence Maximization parallel Algorithms

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Optimizing Fog computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering 1

Optimizing Fog Computing Performance: A Machine Learning-Bas...

引用

1st international conference on advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Dbritto, Ceres Lopes, Joel Oli, Sanjay Joshi, Maulik S Singh, Aniket Rayal, Ashish St Louis United States Redmond United States Dayananda Sagar College of Engineering Department of Mathematics Bangalore560078 India Gujarat Power Engineering and Research Institute GTU Department of Humanity and Science Gujarat India Veer Madho Singh Bhandari Uttarakhand Technical University Department of Computer Science and Engineering Pithoragarh Campus India School of Applied and Life Sciences Uttaranchal University Department of Mathematics Dehradun248007 India

ISBN: (纸本)9798331518578

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework. © 2025 IEEE.

关键词： Fog computing

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OLM²: Automatic Optimal Strategy Generating for Large-Scale Model Training with Limited-Memory 14

OLM<SUP>2</SUP>: Automatic Optimal Strategy Generating for L...

引用

IEEE 14th international conference on Joint Cloud computing (JCC)

作者： Yang, Zhilin Tang, Yu Qiao, Linbo Yang, Xi Huang, Zhen Natl Univ Def Technol Coll Comp Sci Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (纸本)9798350328554

The scale of model parameters and the amount of training data is exponentially increasing. It requires more GPU memory with the exponential increasement of model parameters. Recomputation and swapping are two main memory optimization methods that have been extensively studied, and there are also optimization strategies that combine the two methods. However, most of them are based on heuristic search strategies, which do not explore the complete solution space and can't guarantee the optimality of the solution results. An optimal search strategy with tensor-level recomputation and swapping is expected in large-scale model training. In this paper, we propose an optimal strategy searching algorithm combining tensor-based recomputation and swapping. Specifically, the memory swapping strategy is reformulated as an optimization problem, which converts the memory constraints into mixed integer programming, to find the optimal memory optimization strategy. By leveraging the advantages of both recomputation and swapping, this approach minimizes computation consumption without exceeding the available memory limitation. Experimental results show that our method exhibits about 60% reduction in memory requirements during the training process. Furthermore, our method can reduce the overall training time beyond the existing algorithms. Compared to Checkmate, our approach achieves about 0.3-0.9% reduction in computation cost per iteration.

关键词： memory optimization recomputation swapping mixed integer linear program

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FERI: Feature Enhancement and Relational Interaction for Image-text Matching 30

FERI: Feature Enhancement and Relational Interaction for Ima...

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30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Zhang, Yu Zhang, Jianqiang Song, Gongpeng Lu, Qin Zhao, Shuo Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (纸本)9798331515966

Image-text matching is an important problem at the intersection of computer vision and natural language processing. It aims to establish the semantic link between image and text to achieve high-quality semantic alignment between the two modalities. However, the existing methods have the problem that the meaning expressed in the image or the complex narrative in the text cannot be fully understood due to insufficient feature extraction. Moreover, due to the essential modal differences between images and texts, how to effectively and accurately align the semantic contents in images and texts has become the key of research. In order to solve the above problems, this paper proposes a method based on feature enhancement and relationship interaction. When processing images, the proposed method fuses labeled features, region features and location features to represent images. When processing text, a combination of Bi-GRU and self-attention mechanism is used to represent the text. In order to further align the semantic content in images and texts accurately, this paper improves two relational interaction mechanisms by identifying connection relationships and learning association relationships. Thus, the relation enhanced embedding is obtained. Finally, it calculated the similarity of the enhanced embedding to judge the matching degree of the image and text. Extensive experiments on the public datasets Flickr30K and MSCOCO demonstrate the effectiveness of our method. © 2024 IEEE.

关键词： cross-modal retrieval feature enhancement image-text matching relationship interaction

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PIOD: An Efficient parallel Iterative Algorithm for Solving Over-determined Equations 29

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

引用

29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Tian, Min Wei, Zhenguo Li, Yanlong Du, Wei Xiao, Lei Xu, Chaoshuai China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks China Jinan Institute of Supercomputing Technology Jinan Key Laboratory of High Performance Computing China

ISBN: (纸本)9798350330717

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%. © 2023 IEEE.

关键词： conjugate gradient method MPI over-determined equations parallel algorithms parallel iterative methods

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Accelerating Hierarchical Federated Learning with Model Splitting in Edge computing 30

Accelerating Hierarchical Federated Learning with Model Spli...

引用

30th IEEE international conference on parallel and distributed Systems, ICPADS 2024

作者： Wang, Xiangnan Xu, Yang Xu, Hongli Sun, Zhipeng Liao, Yunming Qi, Ji University of Science and Technology of China School of Computer Science and Technology Hefei China University of Science and Technology of China Suzhou Institute for Advanced Research Suzhou China Software Technology Co. Ltd China

ISBN: (纸本)9798331515966

Recently, Hierarchical Federated Learning (HFL) stands out as a cutting-edge approach to efficiently learn knowledge from massive data on edge devices or clients. To alleviate the computation/communication burden of training large-scale models on resource-constrained clients, Hierarchical Split Federated Learning (HSFL), which splits an entire model into a top and bottom (sub-)model and offloads the training of the top model to the edge server, has been proposed. Nonetheless, there are two key issues, i.e., system heterogeneity and dynamic contexts, hindering the application of effective HSFL. In response, we present an efficient HSFL method, termed AdaHSFL, which introduces intra-cluster gradient feedback regulation and inter-cluster updating frequencies optimization to enhance training efficiency. Concretely, intra-cluster gradient feedback regulation enables edge servers to instantly process incoming smashed data and calculate gradients using top model copies on background threads, while inter-cluster updating frequencies optimization adjusts edge cluster updating frequencies to align the training duration across all clusters with that of the fastest cluster. Furthermore, AdaHSFL explores to jointly implement these two strategies to eliminate the idle waiting time both intra- and intercluster incurred by synchronization barriers. Rigorous performance evaluation demonstrates that AdaHSFL can improve the accuracy by 3.9%-25.5% within given time budgets, compared with the baselines. © 2024 IEEE.

关键词： Federated learning

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Optimal Placement of distributed Generation for Power Quality Improvement using Hybrid ABC-CS Algorithm 14

Optimal Placement of Distributed Generation for Power Qualit...

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14th international conference on advances in computing, Control, and Telecommunication Technologies, ACT 2023

作者： Reddy, Gopal K. Parathasarathy, L. Shakunthala, C. Sowmyashree, K.S. Vidyavardhaka College of Engineering Karnataka Mysuru India ATME College of Engineering Karnataka Mysuru India

Distribution generation (DG) is a term that refers to the production of electricity near the consumption place. On a local basis, there are opportunities for electric utilities to use DG to reduce peak loads, provide auxiliary services such as reactive power and voltage support, and improve power quality. In many cases, distributed generators can provide lower-cost electricity and higher power reliability and security with fewer environmental consequences. Hence, the Dg is placed in the power system to reduce the power loss and increase the power quality. This paper presents a new hybrid optimization algorithm to determine the optimal DG location and size to minimize the total power loss, total harmonic distortion (THD), and voltage sag index improvement. The proposed hybrid optimization algorithm combines the behavior of the Artificial Bee Colony and Cuckoo Search (ABC-CS) algorithm. The DG units are optimally placed by considering the objectives like voltage profile and power loss. The implementation results suggest that the proposed system for the DG placement produces better performance, so that power system quality can be achieved. © Grenze Scientific Society, 2023.

关键词： Power quality

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Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance computing Workloads Under Interference

Characterizing the Performance of Emerging Deep Learning, Gr...

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

作者： Xu, Hao Song, Shualig Mao, Ze Univ Texas Austin Austin TX 78712 USA Univ Southern Calif Los Angeles CA USA

ISBN: (纸本)9798350364613;9798350364606

Throughput-oriented computing via co-running multiple applications in the same machine has been widely adopted to achieve high hardware utilization and energy saving on modern supercomputers and data centers. However, efficiently co-running applications raises new design challenges, mainly because applications with diverse requirements can stress out shared hardware resources (IO, Network and Cache) at various levels. The disparities in resource usage can result in interference, which in turn can lead to unpredictable co-running behaviors. To better understand application interference, prior work provided detailed execution characterization. However, these characterization approaches either emphasize on traditional benchmarks or fall into a single application domain. To address this issue, we study 25 up-to-date applications and benchmarks from various application domains and form 625 consolidation pairs to thoroughly analyze the execution interference caused by application co-running. Moreover, we leverage mini-benchmarks and real applications to pinpoint the provenance of co-running interference in both hardware and software aspects.

关键词： Co-running Deep Learning Graph High Performance computing

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Efficient Storage and Retrieval of Similar Data in Edge computing Systems 29

Efficient Storage and Retrieval of Similar Data in Edge Comp...

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29th IEEE international conference on parallel and distributed Systems, ICPADS 2023

作者： Liu, Yuanfeng Xie, Junjie Liao, Hanlong Chen, Sheng Liu, Xiulong Guo, Deke Tianjin University College of Intelligence and Computing Tianjin300350 China AMS Institute of Systems Engineering Beijing100141 China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha410073 China

ISBN: (纸本)9798350330717

Edge computing is migrating services from remote clouds to the network edge, where a vast amount of data is also flowing into edge nodes. In this context, the Edge Data-Sharing System (EDSS) enhances service quality by enabling edge nodes to cooperate. However, the EDSS is suitable for precise search and faces the existing high overhead when many users retrieve similar data. To solve the obstacle, this paper proposes a similarity-based edge storage system, SESS, which leverages the software-defined edge network to realize efficient storage and retrieval of similarity data. We first design RealminHash, a core module of SESS, for efficient signature and and index for each data. Then, SESS calculates the storage strategy based on the similarity between data. Importantly, SESS adjusts this strategy using periodic network information to ensure load balancing. Experimental results demonstrate that SESS realizes the nearest-neighbor storage while maintaining load balancing. SESS outperforms the well-known k-means and spectral clustering methods in terms of accuracy and latency and supports millisecond similar queries. © 2023 IEEE.

关键词： Edge computing LSH minHash similarity-based storage software-defined edge network

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