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检索条件"任意字段=15th International Conference on Distributed Computing Systems"

共 5435 条记录，以下是71-80 订阅

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Security-Aware Allocation of Replicated Data in distributed Storage systems 15

Security-Aware Allocation of Replicated Data in Distributed ...

引用

15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： De Capitani di Vimercati, Sabrina Foresti, Sara Livraga, Giovanni Samarati, Pierangela Tedesco, Mauro Computer Science Department Università degli Studi di Milano via Celoria 18 Milano Italy

ISBN: (纸本)9789897587474

distributed storage systems offer scalable and cost-effective solutions for managing large data collections. A critical factor for the adoption of these systems is the allocation of data (possibly including replicas) to the storage nodes satisfying operational and security requirements, while ensuring economic effectiveness. Appropriate data and replica management provides significant benefits, ranging from enhanced fault tolerance and improved data availability, to reduced latency and optimized workload distribution. A suboptimal placement of data and replicas can instead lead to excessive costs, security risks, and performance bottlenecks. this paper proposes a novel model for permitting data owners to specify in a friendly manner complex data and replica allocation constraints, and an approach for computing optimal (satisfying operational and security requirements and minimizing costs) data allocations in distributed storage environments. Our work aims to improve the reliability, security, and cost-effectiveness of distributed storage systems. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Information management

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Preserving Privacy in Healthcare Analytics: Federated Learning for Breast Cancer Prediction in a Collaborative Learning Framework 15

Preserving Privacy in Healthcare Analytics: Federated Learni...

引用

15th international conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anuvarshini, G.B. Varshaa, D. Sujithra, M. Devi, D. Sudha Department of Computing- Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9798331300579

Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federated learning offers a means to improve privacy and security. this paper addresses the critical need for safeguarding sensitive medical data by proposing a privacy-preserving federated learning algorithm. Leveraging homomorphic encryption, the proposed algorithm ensures that the learning process protects both data and model integrity. By employing a secure multi-party computation protocol, the algorithm defends against potential adversaries seeking to compromise the deep learning model. Evaluation on real-world medical datasets demonstrates the effectiveness of the approach in preserving privacy and security amidst collaborative learning environments. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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A Framework for Real-Time Monitoring of Power Consumption of distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

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15th international conference on Cloud computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

this paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. the framework allows to visualize and analyze the provider results based on the context information about the performed calculation. the first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. the second part is dedicated to the testing of distributed data storage, called Scalable distributed Two-Layered Data Structure (SD2DS). the results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Service Localization and User Satisfaction Enabled Multi-Tier computing IoT System 44

Service Localization and User Satisfaction Enabled Multi-Tie...

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44th IEEE international conference on distributed computing systems (ICDCS)

作者： Gong, Chenyu Ma, Mulei Zeng, Liekang Yang, Yang Wu, Liantao Liu, Zening HKUST Guangzhou IoT Thrust & Res Ctr Digital World Intelligent Th Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China Terminus Grp Beijing Peoples R China East China Normal Univ Software Engn Inst Shanghai Peoples R China Purple Mt Labs Nanjing Peoples R China

ISBN: (纸本)9798350354720;9798350354713

the rapid expansion of the Internet of things (IoT) poses challenges to certain applications, such as Digital Twin (DT). While data from user devices can be filtered by human intelligence, this is not feasible for IoT devices. Owing to the voluminous data generated by IoT devices that require transmission and computing, traditional cloud computing architectures may no longer guarantee the Quality of Experience (QoE), even causing network congestion. To address this issue, we propose a novel Cloud-Network-Edge-Terminal (CNET) model, which includes an intelligent edge layer for filtering IoT data. the computing paradigm shift indicates that the network will provide services at the edge rather than in the cloud, which is so-called service localization. To demonstrate the benefits of service localization, we use integrated user requirement descriptions to measure QoE, specifically the concepts of Service Requirement Zone (SRZ) and User Satisfaction Ratio (USR). Additionally, we conduct extensive numerical simulations to evaluate the model's performance under varying Degrees of Localization (DoL). Our results show that service localization can significantly improve USR even in changing network conditions.

关键词： Service Localization Multi-Tier computing IoT System QoE Service Requirement Zone User Satisfaction Ratio Digital Twin

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Towards Geo-distributed Training of ML Models in a Multi-Cloud Environment 15

Towards Geo-Distributed Training of ML Models in a Multi-Clo...

引用

15th ACM/SPEC international conference on Performance Engineering (ICPE)

作者： Phalak, Chetan Chahal, Dheeraj Ramesh, Manju Singhal, Rekha TCS Res Mumbai Maharashtra India

ISBN: (纸本)9798400704451

Geo-distributed (GD) training is a machine-learning technique that uses geographically distributed data for model training. Like Federated Learning, geo-distributed machine learning can provide data privacy and also benefit from the cloud infrastructure provided by many vendors in multiple geographies. However, GD training suffers from multiple challenges such as performance degradation due to cross-geography low network bandwidth and high cost of deployment. Additionally, all major cloud vendors such as Amazon AWS, Microsoft Azure, and Google Cloud Platform provide services in several geographies. Hence, finding a high-performance as well as cost-effective cloud service provider and service for GD training is a challenge. In this paper, we present our evaluation of the performance and cost associated with training models in multi-cloud and multi-geography. We evaluate multiple deployment architectures using computing and storage services from multiple cloud vendors. the use of serverless instances in conjunction with virtual machines for model training is evaluated in this study. Additionally, we build and evaluate cost models for estimating the cost of distributed training of models in a multi-cloud environment. Our study shows that the judicious selection of cloud services and architecture might result in cost and performance gains.

关键词： Geo-distributed training multi-cloud cost model

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Model Checking with Large Language Models - Initial Experiments and Future Directions 15

Model Checking with Large Language Models - Initial Experime...

引用

15th international conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Batsakis, Sotiris Tachmazidis, Ilias Mantle, Matthew Papadakis, Nikolaos Antoniou, Grigoris Hellenic Mediterranean University Greece University of Huddersfield United Kingdom School of Computing and Engineering University of Huddersfield United Kingdom Leeds Beckett University United Kingdom

ISBN: (纸本)9798350368833

Large Language Models such as ChatGPT have risen in prominence recently leading to the need to analyse their strengths and limitations on various tasks. the objective of this work is to evaluate the performance of Large Language Models on Model Checking which is used extensively in various critical tasks such as software and hardware verification. © 2024 IEEE.

关键词： Modeling languages

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Sizing of DGs for minimization of power losses in distribution system using Particle Swarm Optimization 10

Sizing of DGs for minimization of power losses in distributi...

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10th IEEE international conference on Electronics, computing and Communication Technologies (IEEE CONECCT)

作者： Kumar, K. Vamshi Merugu, Bhanu Devalapalli, Sai Simha Mekala, Madhushalini Lakavath, Jeswitha Shivani VNR Vignana Jyothi Inst Engn & Technol Elect & Elect Engn Hyderabad Telangana India

ISBN: (纸本)9798350385939;9798350385922

With the increasing penetration of renewable sources, distributed Energy Resources (DER) are emerging as a crucial components of modern power systems. In a distribution system integrated with distributed Generation systems (DG's), efficient power management is crucial to minimize energy losses and maximize effective utilization of electrical energy. the power losses in distribution system have been on higher side due to low X/R ratio and high AT&C losses. this paper presents the minimization of power losses in a radial distribution system integrated with DG's. the load flow analysis is carried out using Forward-Backward Sweep (FBS) method. the optimal power levels of DG's for power loss reduction are obtained by using Particle Swarm Optimization (PSO) algorithm. the proposed method is efficient on a standard IEEE 15 bus radial distribution system.

关键词： distributed Generation (DG) Forward Backward sweep Particle Swarm Optimization Fitness value Power Loss analysis Matlab

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Enhancing power grid resilience to cyber-physical attacks using distributed retail electricity markets 15

Enhancing power grid resilience to cyber-physical attacks us...

引用

15th ACM/IEEE international conference on Cyber-Physical systems (ICCPS)

作者： Nair, Vineet Jagadeesan Srivastava, Priyank Annaswamy, Anuradha Massachusetts Inst of Technol Dept Mech Engn Cambridge MA 02139 USA Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

ISBN: (纸本)9798350369274;9798350369281

We propose using a hierarchical retail market structure to alert and dispatch resources to mitigate cyber-physical attacks on a distribution grid. We simulate attacks where a number of generation nodes in a distribution grid are attacked. We show that the market is able to successfully meet the shortfall between demand and supply by utilizing the flexibility of remaining resources while minimizing any extra power that needs to be imported from the main transmission grid. this includes utilizing upward flexibility or reserves of remaining online generators and some curtailment or shifting of flexible loads, which results in higher costs. Using price signals and market-based coordination, the grid operator can achieve its objectives without direct control over distributed energy resources and is able to accurately compensate prosumers for the grid support they provide.

关键词： Resilience smart grid electricity markets optimization cyber attacks

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Secure, Transparent, Supply Chain Supporting Agri-Food Provenance and Sustainability 15th

Secure, Transparent, Supply Chain Supporting Agri-Food Prove...

引用

15th international conference on Ubiquitous computing and Ambient Intelligence

作者： Martin, thomas Cowderoy, Tom Rafferty, Joseph Cadden, Trevor McIvor, Ronan Brennan, Lorraine Ulster Univ Belfast Antrim North Ireland Ulster Coll Dublin Dublin Ireland

ISBN: (纸本)9783031486418;9783031486425

the protein-I project is a cross Ireland initiative that takes a food systems approach to enhancing the sustainability of protein production across the island of Ireland, as part of the protein-I project, this study aims to produce a smart agricultural supply chain solution. there is a need for the requirements of the system to be tailored for the specific use case, agriculture on the island of Ireland is different from agriculture in other parts of the world. the average size of a farm in the Republic of Ireland is 32.4 ha [8], compare this to Australia where the average is 4,331 ha [7], therefore farm practices in Australia will be different. To develop a solution that is catered to the needs of the Agri-food sector on the island of Ireland this study gathered requirements from a series of workshops and interviews with stakeholders. Several use cases were presented by the stakeholders to identify the deficiencies in the current supply chain, and to address these challenges, a custom solution has been designed and implemented.

关键词： Secure supply chains blockchain distributed ledger technology carbon calculation sustainable living smart agriculture Internet of things Industry 5.0 protein-I project

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SIRM: Cost efficient and SLO aware ML prediction on Fog-Cloud Network 15

SIRM: Cost efficient and SLO aware ML prediction on Fog-Clou...

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15th international conference on Communication systems and Networks (COMSNETS)

作者： Phalak, Chetan Chahal, Dheeraj Singhal, Rekha TCS Res Mumbai Maharashtra India

ISBN: (纸本)9781665477062

Cloud and Fog computing are complementary technologies used for complex Internet of things (IoT) based deployment of applications. With an increase in the number of internetconnected devices, the volume of data generated and processed at higher speeds has increased substantially. Serving a large amount of data and workloads for predictive decisions in real-time using fog computing without Service-Level Objective (SLO) violation is a challenge. Integration of multiple cloud services and platforms with fog computing can resolve this issue by providing additional resources. In this work, we present a general-purpose System for Inference Request Management (SIRM) aimed at automatically generating a suitable execution workflow to execute ML/DL inference requests using fog with Function-as-a-Service (FaaS) and Machine Learning-as-a-service (MLaaS) offered by cloud vendors. Generated workflow minimizes the cost of deployment as well as SLO violations. the use of SIRM results in less than 5% violations when tested using health domain and recommender system based applications.

关键词： Machine-Learning-as-a-Service inference serving SLO awareness cost minimization cloud computing

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