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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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Optimized VM Migration for Energy and Cost Reduction Using TSO Algorithm in Cloud computing 1

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5th international conference on advances in distributed computing and Machine Learning, ICADCML 2024

作者： Nagaswathy, A. Suganya, M. College of Arts and Science Tamil Nadu Sulur India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

Virtual machines (VMs) in cloud computing can be transferred from one physical host to another via VM migration. The placement and migration of virtual machines are a multi-objective optimization problem. An effective VM allocation policy will reduce hotspots, improve QoS preservation, and increase energy efficiency. Additionally, it will lower the data centre’s operating expenses. In this paper, optimized VM migration using Tuna Swarm Optimization (TSO) Algorithm OVM-TSO is proposed for energy and cost reduction in cloud computing. The overloaded and underloaded VMs from any host are selected for migration. For selecting the target VMs for placement, separate fitness function is derived for overloaded and underloaded hosts. Then TSO algorithm is applied for target VM selection for the migrated VMs such that the overall energy consumption, resource wastage, and migration cost are reduced. Simulation results using CloudSim show that the proposed OVM-TSO algorithm minimizes the power consumption, reduces the response delay, and increases the CPU utilization, during migrations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Energy utilization

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Automated Mapping of Task-Based Programs onto distributed and Heterogeneous Machines 23

Automated Mapping of Task-Based Programs onto Distributed an...

引用

2023 international conference for High Performance computing, Networking, Storage and Analysis, SC 2023

作者： Teixeira, Thiago S. F. X. Henzinger, Alexandra Yadav, Rohan Aiken, Alex Stanford University Stanford United States MIT Boston United States

ISBN: (纸本)9798400701092

In a parallel and distributed application, a mapping is a selection of a processor for each computation or task and memories for the data collections that each task accesses. Finding high-performance mappings is challenging, particularly on heterogeneous hardware with multiple choices for processors and memories. We show that fast mappings are sensitive to the machine, application, and input. Porting to a new machine, modifying the application, or using a different input size may necessitate re-tuning the mapping to maintain the best possible *** present AutoMap, a system that automatically tunes the mapping to the hardware used and finds fast mappings without user intervention or code modification. In contrast, hand-written mappings often require days of experimentation. AutoMap utilizes a novel constrained coordinate-wise descent search algorithm that balances the trade-off between running computations quickly and minimizing data movement. AutoMap discovers mappings up to 2.41× faster than custom, hand-written mappers. © 2023 ACM.

关键词： Mapping

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Secure parallel Outsourcing Scheme for Large-scale Matrix Multiplication on distributed Cloud Servers 29

Secure parallel Outsourcing Scheme for Large-scale Matrix Mu...

引用

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

作者： Wang, Yinlong Tao, Yunting Kong, Fanyu Gu, Zhaoquan Yu, Jia Zhang, Hanlin Binzhou Polytechnic College of Information Engineering Shandong Binzhou256603 China Shandong University School of Software Shandong Jinan250101 China Shandong Sansec Information Technology Co. Ltd Jinan250101 China School of Computer Science and Technology Shenzhen China Qingdao University College of Computer Science and Technology Shandong Qingdao266071 China

ISBN: (纸本)9798350330717

Large-scale matrix multiplication is a computational bottleneck in various applications including artificial intelligence and machine learning. Given the time complexity of O(n3) for matrix multiplication, large matrix computation is exceedingly time-consuming for the client-side user. By outsourcing this task to cloud servers with substantial computational resources, we can significantly reduce the client-side computational time. This paper presents a parallel matrix multiplication outsourcing scheme based on Cannon's algorithm. By distributing the matrix across multiple cloud servers for parallel computation, we can get a significant efficiency speedup. Our scheme employs multiple cloud servers to perform parallel matrix computation, reducing the computational load by 89-97% when utilizing 4-16 servers as opposed to using a single server. We provide a comprehensive analysis of the scheme's correctness, security, and verifiability, substantiating the benefits of our approach through the experimental data. © 2023 IEEE.

关键词： Cloud computing Data privacy Matrix multiplication parallel outsourcing

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A distributed Architecture for Particle Swarm Optimization Meta-heuristic Methods Based on Multi-agent Systems 9th

A Distributed Architecture for Particle Swarm Optimization M...

引用

9th international conference on Smart City Applications, SCA 2024

作者： Adbi, Said Mouncif, Hicham Laboratory LIMATI Polydisciplinary Faculty Sultan Moulay Slimane University Beni Mellal Morocco

ISBN: (纸本)9783031886522

This research presents a novel parallel model for Particle Swarm Optimization (PSO), named Multi-agent Agent-based PSO (MAPSO). The model synergizes multi-agent system principles with parallel computing techniques to enhance optimization processes. By transforming particles into autonomous intelligent agents capable of asynchronous execution and advanced learning, the swarm operates as a cohesive multi-agent system. This innovative approach seeks to improve the efficiency and performance of conventional PSO algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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Forest in the Clouds: Navigating Big Data with GRP and RFC 5th

Forest in the Clouds: Navigating Big Data with GRP and RFC

引用

5th international conference on advances in distributed computing and Machine Learning, ICADCML 2024

作者： Mukherjee, Dhritiman Anand, Aman Banik, Rohit Satpati, Ritwik Bhattacharyya, Shakya Paul, Pratyushayan West Bengal Kolkata700135 India School of Computing Queen’s University KingstonONK7L 2N8 Canada

ISBN: (纸本)9789819735228

The burgeoning realm of big data has escalated the importance of efficient processing methodologies. In addressing this challenge, this study delves into the integration of Gaussian Random Projection (GRP) and Random Forest Classifier (RFC) facilitated by the PySpark environment to devise an optimal big data processing model. While PySpark serves as a potent tool for distributed computing, the GRP offers a pragmatic approach for dimensionality reduction, thereby curtailing the computational intricacies of extensive datasets. This reduction positions the data favourably for the application of machine learning techniques, notably the RFC—a versatile algorithm proficient in classification and regression by amalgamating the predictions from an ensemble of decision trees. The confluence of GRP and RFC manifests a model boasting both accuracy and efficiency, catering to diverse applications. This manuscript elucidates the PySpark-based implementation of GRP and RFC, bolstered by a comprehensive performance evaluation using a real-world dataset. The discussion encapsulates the merits and demerits of the adopted methodology, along with prospective avenues for future exploration. Preliminary outcomes validate the proficiency of GRP and RFC as indispensable assets in sculpting efficient big data processing models within the PySpark ecosystem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Advancing Evasion: distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

引用

25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. This paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Content-driven Closeness Centrality based Caching in Softwarized Edge Networks

Content-driven Closeness Centrality based Caching in Softwar...

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IEEE international conference on Communications (IEEE ICC)

作者： Amadeo, Marica Ruggeri, Giuseppe Campolo, Claudia Molinaro, Antonella Univ Mediterranea Reggio Calabria Reggio Di Calabria Italy

ISBN: (纸本)9781538674628

The increasing volume of Internet traffic is pushing the Internet Service Providers to deploy distributed caching services at the network edge, close to the end users, in order to speed up the data retrieval and reduce the bandwidth demands. In parallel, centralized paradigms like Software Defined Networking (SDN) are considered to optimize network management while supporting a variety of network applications like routing, load balancing and caching. In this paper, we extend the SDN control plane to support a novel content caching strategy. We consider a softwarised edge network domain where SDN nodes, augmented with storage capabilities, cache incoming data with the twofold target of limiting the retrieval delay and the inter-domain traffic. The caching decision is taken in a centralized mode by the SDN Controller, according to a newly defined content-driven closeness centrality metric, which identifies the importance of the SDN nodes as cachers based on their proximity to the majority of the clients requesting the most popular contents. Simulation results show the superiority of the solution in terms of higher cache hits and reduced latency, when compared against benchmark caching strategies.

关键词： Caching Edge computing SDN closeness centrality

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21st international conference on advances in Mobile computing and Multimedia Intelligence, MoMM 2023

21st International Conference on Advances in Mobile Computin...

引用

21st international conference on advances in Mobile computing and Multimedia Intelligence, MoMM 2023

ISBN: (纸本)9783031483479

The proceedings contain 15 papers. The special focus in this conference is on advances in Mobile computing and Multimedia Intelligence. The topics include: Face to Face with Efficiency: Real-Time Face Recognition Pipelines on Embedded Devices;multi-camera Live Video Streaming over Wireless Network;effects of Deep Generative AutoEncoder Based Image Compression on Face Attribute Recognition: A Comprehensive Study;Implementation of a Video Game Controlled by Pressing the Upper Arm Using PPG Sensor;immerscape: Supporting the Creation of Immersive Soundscapes by Users in Cultural Heritage Contexts;analysis of Data Obtained from the Mobile Botnet;On the Impact of FFP2 Face Masks on Speaker Verification for Mobile Device Authentication;Blockchain-Enhanced IoHT: A Patient-Centric Internet of Healthcare Things Platform with Smart Contract-Driven Data Management;federated Learning for Collaborative Cybersecurity of distributed Healthcare;does Use of Blink Interface Affect Number of Blinks When Reading Paper Books?;a Method for Stimuli Control of Carbonated Beverages by Estimating and Reducing Carbonation Level;a Knee Injury Prevention System by Continuous Knee Angle Recognition Using Stretch Sensors;Ubiquitous Mobile Application for Conducting Occupational Therapy in Children with ADHD.

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Performance Modeling of Blockchains of BFT-type Consensus 29

Performance Modeling of Blockchains of BFT-type Consensus

引用

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

作者： Li, Bo Jiang, Chenhao Zhang, Hanwen Li, Zhongcheng Sun, Yi Chinese Academy of Sciences Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350330717

As the requirements of different application scenarios vary, numerous blockchains have been developed. Among them, chains using Byzantine Fault Tolerance consensus (BFT chains for short) occupy a dominant position. Performance modeling is the most common method for guiding chain design and configuration. However, many lack specific models due to the large number of BFT chains. Providing a general framework for them is promising and should demonstrate two important features that BFT chains may differ in: different consensus stages and different packaging rules. This paper is the first to propose a general framework applicable to various BFT chains. We use the bulk-service model in queuing theory to reflect the above two features of various BFT chains and provide calculation methods for them. We give an expression for the confirmation delay of transactions (TXs for short) in the chain and verify it through experiments on various BFT chains. We also use the model for a brief analysis to assist designers in configuring and designing BFT chains. © 2023 IEEE.

关键词： BFT-type consensus Blockchain management Delay General framework Performance modeling

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Multi-GPU parallel Pipeline Rendering with Splitting Frame 40th

Multi-GPU Parallel Pipeline Rendering with Splitting Frame

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40th Computer Graphics international conference (CGI)

作者： Zhang, Haitang Ma, Junchao Qiu, Zixia Yao, Junmei Al Sibahee, Mustafa A. Abduljabbar, Zaid Ameen Nyangaresi, Vincent Omollo Shenzhen Technol Univ Coll Big Data & Internet Shenzhen Peoples R China Shenzhen Univ Coll Appl Technol Shenzhen Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen Peoples R China Univ Basrah Coll Educ Pure Sci Dept Comp Sci Basrah Iraq Al Kunooze Univ Coll Comp Engn Dept Basrah Iraq Huazhong Univ Sci & Technol Shenzhen Inst Shenzhen Peoples R China Tom Mboya Univ Fac Biol & Phys Sci Homabay Kenya

ISBN: (纸本)9783031500718;9783031500725

In order to achieve real-time rendering of cloud gaming, a large amount of computing resources of graphics processing units (GPU) are required. In this paper, we propose a multi-GPU parallel pipeline rendering approach that makes full use the computing power of multiple GPUs to accelerate real-time ray tracing rendering effectively. This approach enables heterogeneous GPUs to render the same frame cooperatively through a dynamic splitting frame load balancing scheme, and ensures that each GPU is assigned with the suitable size of splitting frame based on its rendering ability. A fine-grained parallel pipeline method divides the process of rendering into more detailed steps that enable multiple frames to be rendered in parallel, which improves the utilization of each step and speeds up the output of frames. With the experiments on various dynamic scenes, the results show that the number of frames per second (FPS) of the multi-GPU system composed of two GPUs is 2.2 times higher than that of the single GPU system, while the multi-GPU system composed of three GPUs has increased to 3.3 times.

关键词： Multi-GPU Ray tracing Split frame rendering parallel pipeline 3D render

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