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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Jain, Anant Gupta, Gauranshi Johari, Rahul Vidyarthi, Deo Prakash Guru Gobind Singh Indraprastha University Delhi India School of Computer and Systems Sciences Parallel and Distributed System Lab JNU Delhi India

ISBN: (纸本)9789819795062

Historically, the health insurance domain has adhered to uniform premium pricing structures, often sidelining the distinct health nuances of each policyholder. Such broad-brush approaches can result in misaligned premiums, not truly reflective of individual health dynamics. Venturing into uncharted territories, this study proposes an innovative fusion of Federated Machine Learning (FML) and BlockChain methodologies to recalibrate health insurance pricing. FML, a cutting-edge evolution in the machine learning sphere, emphasizes decentralized data processing. By harnessing FML, timely insights can be gathered that mirror an individual’s health trajectory. This continuous insight generation facilitates agile adjustments to insurance premiums, ensuring they resonate with real-time health conditions. BlockChain’s decentralized ledger system acts as the bedrock of this model. Each premium modification, steered by FML-derived insights, is indelibly etched onto the BlockChain. This architecture not only masters transparency but also instills a heightened sense of trust, given the unalterable nature of BlockChain entries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Energy-Efficient Scheduling Algorithms for real-time parallel Applications on Heterogeneous distributed Embedded systems

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IEEE TRANSACTIONS ON parallel AND distributed systems 2017年第12期28卷 3426-3442页

作者： Xie, Guoqi Zeng, Gang Xiao, Xiongren Li, Renfa Li, Keqin Hunan Univ Coll Comp Sci & Elect Engn Key Lab Embedded & Network Comp Hunan Prov Changsha 410082 Hunan Peoples R China Nagoya Univ Grad Sch Engn Nagoya Aichi 4648603 Japan SUNY Coll New Paltz Dept Comp Sci New Paltz NY 12561 USA

Energy consumption minimization is one of the primary design requirements for heterogeneous distributed systems. State-of-the-art algorithms are used to study the problem of minimizing the energy consumption of a real-time parallel application with precedence constrained tasks on a heterogeneous distributed system by introducing the concept of latest finish time (LFT) to reclaim the slack time based on the dynamic voltage and frequency scaling (DVFS) energy-efficient design optimization technique. However, the use of DVFS technique alone is insufficient, and the energy consumption reduction is limited because scaling down the frequency is restricted in practice. Furthermore, these studies merely minimize energy consumption through a local energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on the fixed processor, rather than a global energy-efficient scheduling algorithm, such as reducing the energy consumption for each task on different processors. This study solves the problem of minimizing the energy consumption of a real-time parallel application on heterogeneous distributed systems by using the combined non-DVFS and global DVFS-enabled energy-efficient scheduling algorithms. The non-DVFS energy-efficient scheduling (NDES) algorithm is solved by introducing the concept of deadline slacks to reduce the energy consumption while satisfying the deadline constraint. The global DVFS-enabled energy-efficient scheduling (GDES) algorithm is presented by moving the tasks to the processor slacks that generate minimum dynamic energy consumptions. Results of the experiments show that the combined NDES&GDES algorithm can save up to 36.25-55.65 percent of energy compared with state-of-the-art counterparts under different scales, parallelism, and heterogeneity degrees of parallel applications.

关键词： Directed acyclic graph (DAG) dynamic voltage and frequency scaling (DVFS) energy-efficient scheduling heterogeneous distributed embedded systems real-time

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Towards Smarter Maritime Security: Apriori Algorithm Optimization in Serverless Environments

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Procedia Computer Science 2025年 257卷 452-459页

作者： Mahtab Shahin Saeed Rahimpour Tara Ghasempouri Sanja Bauk Thomas Fahringer Dirk Draheim Estonian Maritime Academy Tallinn University of Technology Tallinn Estonia Department of Computer Systems Tallinn University of Technology Tallinn Estonia Distributed and Parallel Systems Group University of Innsbruck Tirol Austria Information Systems Group Tallinn University of Technology Tallinn Estonia

This paper presents an optimized implementation of the Apriori algorithm tailored for large-scale data mining in cloud-native, serverless environments, utilizing real-world fuel datasets. Our approach achieves a 28% reduction in execution time and a 22% decrease in memory consumption compared to traditional distributed Apriori methods. The study leverages high-dimensional fuel datasets, spanning from 2020 to 2050, to evaluate scalability and efficiency in processing energy-related data. By employing advanced synchronization and deferred partitioning strategies, communication overhead is significantly reduced, improving performance while effectively balancing computational loads across distributed nodes. Security measures, including AES-256 encryption and role-based access control (RBAC), are incorporated to safeguard data confidentiality and ensure compliance with regulatory frameworks. The proposed solution scales efficiently for datasets up to 1 million records, demonstrating applicability across domains such as transportation and logistics. Future work will explore adaptive partitioning techniques, hybrid cloud architectures, and AI-driven predictive analytics to further enhance scalability and operational efficiency in serverless multi-cloud systems.

关键词： Cloud-Native Architectures Serverless Computing Data Mining Apriori Algorithm distributed Computing Big Data Maritime

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Fast and scalable in-network lock management using lock fission 24

Fast and scalable in-network lock management using lock fiss...

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Proceedings of the 18th USENIX Conference on Operating systems Design and Implementation

作者： Hanze Zhang Ke Cheng Rong Chen Haibo Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Shanghai AI Laboratory and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University and Engineering Research Center for Domain-specific Operating Systems Ministry of Education China and Key Laboratory of System Software (Chinese Academy of Sciences)

ISBN: (纸本)9781939133403

distributed lock services are extensively utilized in distributed systems to serialize concurrent accesses to shared resources. The need for fast and scalable lock services has become more pronounced with decreasing task execution times and expanding dataset scales. However, traditional lock managers, reliant on server CPUs to handle lock requests, experience significant queuing delays in lock grant latency. Advanced network hardware (e.g. programmable switches) presents an avenue to manage locks without queuing delays due to their high packet processing power. Nevertheless, their constrained memory capacity restricts the manageable lock scale, thereby limiting their effect in large-scale *** paper presents FISSLOCK, a fast and scalable distributed lock service that exploits the programmable switch to improve (tail) latency and peak throughput for millions of locks. The key idea behind FISSLOCK is the concept of lock fission, which decouples lock management into grant decision and participant maintenance. FISSLOCK leverages the programmable switch to decide lock grants synchronously and relies on servers to maintain participants (i.e., holders and waiters) asynchronously. By using the programmable switch for routing, FISSLOCK enables on-demand fine-grained lock migration, thereby reducing the lock grant and release delays. FISSLOCK carefully designs and implements grant decision procedure on the programmable switch, supporting over one million locks. Evaluation using various benchmarks and a real-world application shows the efficiency of FISSLOCK. Compared to the state-of-the-art switch-based approach (NetLock), FISSLOCK cuts up to 79.1% (from 43.0%) of median lock grant time in the microbenchmark and improves transaction throughput for TATP and TPC-C by 1.76× and 2.28×, respectively.

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Fault-Tolerant Architectures for distributed Big Data Analytics

Fault-Tolerant Architectures for Distributed Big Data Analyt...

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Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Sunil Sudhakaran Samarth Shah Manas Kumar Mishra Vignesh Natarajan Jay Bhatt Om Goel Mahatma Gandhi University Kottayam Kerala India University at Albany Albany NY USA Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India Arizona State University AZ United States Northeastem University Boston MA USA ABES Engineering College Ghaziabad U.P. India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

It has created challenges in the processing of data and analysis, which need to go beyond the traditional centralized architecture to solve the current data volume, speed, and types. To address these challenges, fault-tolerant architectures have become one of the keys to meeting the needs of distributed big data analytics. Failure is a normal part of systems; these architectures are meant to accommodate failures of individual components so that the entire system does not need to stop working or, worse, give false results. They make use of several nodes to operate and retain data in a distributed fashion, which supplies excessive scalability and fault tolerance. This means that even if some nodes fail, a fault-tolerant architecture can still provide redundancy by replicating data across many nodes, giving the system redundancy. Fault-tolerant architectures also utilize efficient data processing techniques like partitioning and data parallel processing, to achieve high performance. This allows real-time and near-real-time analysis of streaming data.

关键词： Scalability Fault tolerant systems Redundancy distributed databases Computer architecture Big Data Data processing real-time systems Complexity theory Physical design

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FRAME 2024 - Proceedings of the 2024 on workshop on Flexible Resource and Application Management on the Edge, Part of: HPDC 2024 - 3rd International Symposium on High-Performance parallel and distributed Computing

FRAME 2024 - Proceedings of the 2024 on Workshop on Flexible...

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4th workshop on Flexible Resource and Application Management on the Edge, FRAME 2024

ISBN: (纸本)9798400706417

The proceedings contain 9 papers. The topics discussed include: towards sustainable deployment of microservices over the Cloud-IoT continuum, with FREEDA;towards enabling observability of energy demand, with NEST;urgent edge computing;optimizing resource allocation in the edge: a minimum weighted vertex cover approach;towards energy-aware execution and offloading of serverless functions;reactive autoscaling of Kubernetes nodes;OUTFIT: crowdsourced data feeding noise maps in digital twins;encoding consistency: optimizing self-driving reliability with real-time speed data;and striking trade-off between high performance and energy efficiency in an edge computing application for detecting floating plastic debris.

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Lightweight Edge Stream Processing Framework and Task Scheduling Algorithm for CNN-Based distributed PV Output Prediction

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IET Generation, Transmission and Distribution 2025年第1期19卷

作者： Zhu, Bin Liu, Tianyuan Weng, Jiaming Liu, Dong Shanghai China

An increasing number of distributed photovoltaic systems utilize convolutional neural network (CNN)-based models for power prediction, yet face computational bottlenecks when deploying these models on resource-constrained photovoltaic edge computing terminals (PECT). To address this challenge, this paper proposes a lightweight edge stream processing framework integrated with a dynamic task scheduling mechanism, comprising three core components: a data receiving module (DRM) implements real-time task preprocessing with validity screening, a data computing module (DCM) splits and processes sub-tasks of CNNs in parallel, and realizes distributed node collaboration. and a data summarizing module (DSM) for data aggregation. The scheduling mechanism combines a modified least laxity first (MLLF) algorithm with dynamic priority adjustment and a self-monitoring allocation (SMA) algorithm enabling local resource-aware load balancing. Deployed on the iPACS-5612C1 IoT terminal, experiments show that the proposed framework achieves a 97% average CPU utilization (85% in baseline methods), a 25% reduction in computing time, and a 90% task completion rate, with the best real efficiency. The framework achieves a real efficiency improvement of 40% over cloud batch processing while maintaining prediction accuracy above 90% under dynamic conditions. Experiments also demonstrate that this framework has the potential to be deployed on larger photovoltaic clusters. These results demonstrate the effectiveness and scalability of the edge stream processing framework. © 2025 The Author(s). IET Generation, Transmission & Distribution published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： Convolutional neural networks

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4th International AMAST workshop on real-time systems and Concurrent and distributed Software, ARTS 1997

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4th International AMAST workshop on real-time systems and Concurrent and distributed Software, ARTS 1997

ISBN: (纸本)3540630104

The proceedings contain 28 papers. The special focus in this conference is on A Pioneer of Computer Science. The topics include: Deductive verification of real-time systems using step;refinement of time;representing time efficiently with BDDs;refining interval temporal logic specifications;integrating temporal logics and model checking algorithms;a new class of implementable real-time automata;communication concepts for statecharts;regular processes and timed automata;a visual formalism for real-time requirement specifications;formal specification and verification method of concurrent and distributed systems by restricted timed automata;transformational formal development of real-time systems;a transformation of monitor into communication synchronized parallel processes;contracts for ODP;affine transformations in SIGNAL and their application in the specification and validation of real-time systems;action-based concurrency and synchronization for objects;communication extended abstract types in the refinement of parallel communicating processes;verification and refinement of distributed programs in a fair framework;formalizing real-time scheduling as program refinement;specification and refinement of continuous real-time systems;high-level execution time analysis;a sound and complete proof system for probabilistic processes;testing semantics for a probabilistic-timed process algebra;denotational semantics for timed testing;a true concurrency perspective and introduction of a suspend/resume operator in ET-LOTOS.

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High Performance Visualization for Astrophysics and Cosmology

High Performance Visualization for Astrophysics and Cosmolog...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Nicola Tuccari Eva Sciacca Fabio Vitello Iacopo Colonnelli Yolanda Becerra Enric Sosa Cintero Guillermo Marin Milan Jaros Lubomir Riha Petr Strakos Sebastian Trujillo-Gomez Emiliano Tramontana Robert Wissing Istituto Nazionale di Astrofisica Catania Italy Università di Torino Torino Italy Barcelona Supercomputing Center Barcelona Spain IT4Innovations National Supercomputing Center Ostrava Czech Republic Heidelberg Institute for Theoretical Studies Heidelberg Germany Università degli Studi di Catania Italy University of Oslo Institute of Theoretical Astrophysics Oslo Norway

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Modern Astrophysics and Cosmology (A&C) projects produce immense data volumes, necessitating advanced software tools for data access, storage, and analysis. Visualization Interface for the Virtual Observatory (VisIVO) is one such tool enabling multi-dimensional data analysis and knowledge discovery across complex astrophysical datasets. Leveraging containerization and virtualization, VisIVO has been deployed on various distributed computing platforms. Additionally, Blender, an open-source 3D suite, provides robust tools for rendering and processing volumetric data, making it suitable for visualizing complex datasets. At the SPACE Center of Excellence these tools are being adapted for high-performance visualization of cosmological simulations performed with GADGET and ChaNGa on pre-exascale systems. However, implementing high-performance visualization on diverse HPC platforms presents several challenges, including hardware and software compatibility, data management, scalability, performance portability, and efficient resource allocation. This paper outlines strategies to integrate VisIVO with workflow frameworks and streaming platforms to address these challenges. Workflow frameworks enhance portability, scheduling, and reproducibility of visualization workflows on pre-exascale systems used in A&C simulations. We also discuss the use of streaming platforms to enable concurrent (i.e. in-situ) analysis and visualization of simulations, reducing the need to store full simulation data by leveraging distributed databases that stream the output data in real time. Lastly, we present an adaptation of Blender to handle large-scale particle-based astrophysical data, offering high-quality visualization with interactive exploration capabilities.

关键词： Adaptation models Analytical models Astrophysics Computational modeling Scalability Data visualization distributed databases Data models Reproducibility of results real-time systems

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