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

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

15th International Conference on Advances in Computing, Cont...

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

ISBN: (纸本)9798331300579

the proceedings contain 1065 papers. the topics discussed include: Morse Code to text converter using Arduino;blink-an intelligent personal assistant for enhancing accessibility for differently-abled people;a novel clustering and optimization strategy for network lifetime enhancement in wireless sensor network;an application of voice mail: email services for visually impaired;enhancing spam detection accuracy using genetic algorithm;design and implementation of modified bidirectional converter;farming tool leverage system and expert chat;smart mobility assistive device for paretic people;brain stroke prediction through MRI using deep learning techniques;effective hyper-parameter tuning of machine learning model for analysis drinking water quality;touchless hand sanitizer dispenser - San Master;cyber hygiene in higher educational institutes;and submodule level configuration for PV System Performance Improvement under Partial Shading.

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the Seventh Workshop on Hot Topics in Cloud computing Performance (HotCloudPerf-2024) 15

The Seventh Workshop on Hot Topics in Cloud Computing Perfor...

引用

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

作者： Kimovski, Dragi Tocze, Klervie Herbst, Nikolas De Matteis, Tiziano Univ Klagenfurt Klagenfurt Austria Linkoping Univ Linkoping Sweden Univ Wurzburg Wurzburg Germany Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9798400704451

It gives us immense pleasure to extend a warm welcome to you for the 2024 edition of the Workshop on Hot Topics in Cloud computing Performance - HotCloudPerf 2024. Cloud computing represents one of the most significant transformations in the realm of IT infrastructure and usage. the adoption of global services within public clouds is on the rise, and the immensely lucrative global cloud market already sustains over 1 million IT-related jobs. However, optimizing the performance and efficiency of the IT services provided by both public and private clouds remains a considerable challenge. Emerging architectures, techniques, and real-world systems entail interactions with the computing continuum, serverless operation, everything as a service, complex workflows, auto-scaling and -tiering, etc. the extent to which traditional performance engineering, software engineering, and system design and analysis tools can contribute to understanding and engineering these emerging technologies is uncertain. the community requires practical tools and robust methodologies to address the hot topics in cloud computing performance effectively.

关键词： cloud performance performance modelling

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Quantization in distributed Learning for Privacy Preserving: A Systematic Literature Review 15

Quantization in Distributed Learning for Privacy Preserving:...

引用

15th IEEE international conference on Cloud computing Technology and Science, CloudCom 2024

作者： Al Qassem, Lamees M. Colombo, Maurizio Damiani, Ernesto Asal, Rasool Almemari, Al Anoud Alhammadi, Yousof Ebtic Khalifa University Abu Dhabi United Arab Emirates Khalifa University Abu Dhabi United Arab Emirates Khalifa University Department of Computer Science Abu Dhabi United Arab Emirates Universita degli Studi di Milano Computer Science Department Milan Italy College of Engineering and It University of Dubai Dubai United Arab Emirates

ISBN: (纸本)9798331507589

A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. this paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks. © 2024 IEEE.

关键词： Federated learning

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2024 15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

2024 15th International Conference on Computing Communicatio...

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

ISBN: (纸本)9798350370249

the proceedings contain 2463 papers. the topics discussed include: gender-based diagnosis of frontotemporal dementia using deep learning;heart attack risk prediction using advanced machine learning techniques;a computer-aided risk assessment and major factors analysis system for cervical cancer;enhancing oil spill detection using synthetic aperture radar with dual attention U-Net model;a regulatory framework for improving e-banking security;advancements in graph-based machine learning for electronic health record analysis;application of advanced AI algorithms for Fintech crime detection;robust color classification for autonomous robotic boats;unlocking the viability of blockchain adoption for healthcare: a feasibility study;sign guard: banking signature authentication;a bibliometric study on the interplay of virtual reality and cinematic innovation;and a roadmap of byte-sized morality in traversing with ethical landscape of artificial intelligence.

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Against the Current: Introducing Reversibility to Superscalar Processors via Reversible Branch Predictors 15

Against the Current: Introducing Reversibility to Superscala...

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15th international Green and Sustainable computing conference

作者： Gregg, Byron Teuscher, Christof Portland State Univ Dept Elect & Comp Engn Portland OR 97207 USA

ISBN: (纸本)9798331507879;9798331507862

Although highly energy efficient, adiabatic and reversible systems suffer from performance drawbacks inherent to the physical operations that make them so efficient. Superscalar processors provide high performance through out-of-order speculative work of which an effective branch predictor is a key component in those performance gains. In the context of reversibility, a branch predictor is a design focal point because any fully reversible system must also be able to predict branch outcomes when in reverse mode. Taking advantage of Temporal Streaming techniques, this paper introduces several reversible branch predictor implementations which enable reversible and out-of-order instruction execution. these first-of-their-kind designs allow for a superscalar architecture that would maintain both a high level of performance and a high level of energy efficiency with the ability to un-compute obsolete data stored in memory. Testing our designs using the SimpleScalar out-of-order simulator, we estimate possible additional savings of 24 fJ per MB of data recovered at room temperature and at reverse prediction rates 2.27% higher than the forward. this work opens new avenues for designing and developing what we are calling Fully Adiabatic, Reversible, and Superscalar (FARS) Processor Architectures and is the first of many adaptations of conventional superscalar components to a reversible system.

关键词： adiabatic computing branch predictor efficient computing fars fully adiabatic and reversible fully adiabatic reversible and superscalar hyper-efficient computing reversible and superscalar reversible branch predictor reversible computing superscalar

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

15th International Conference on Advances in Computing, Cont...

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

ISBN: (纸本)9798331300579

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An Efficient Task Scheduling for Latency Sensitive Tasks in Edge - Cloud computing Environment 15

An Efficient Task Scheduling for Latency Sensitive Tasks in ...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： thotakura, Yaswanth Chowdary Gollapalli, Geetha Siva Srinivas Yarramsetty, Dinesh Manikanta Kasim, Shalom Raja Doppalapudi, Kalyan Kumar Swain, Chinmaya Kumar Srm University - Ap Dept. of Cse Andhra Pradesh India

ISBN: (纸本)9798350370249

With an emphasis on fulfilling deadlines, this study presents an efficient method for workload scheduling in edge-cloud collaborative computing settings. It optimizes task distribution taking into account variables like task arrival rate and resource availability. A genetic algorithm-based approach is suggested to improve the procedure of scheduling further. It makes use of task prioritization, mutation, and solution encoding. To achieve deadline fulfillment and resource optimization in edge-cloud collaborative computing systems, this method successfully tackles the problems of fluctuating user demands and resource constraints. It also provides an effective solution for dynamic task scheduling. © 2024 IEEE.

关键词： Earliest Deadline First (EDF) Edge computing First-Come-First-Serve (FCFS) Genetic Algorithm (GA) Solution Encoding

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Towards FAIR Data Stream Processing Ecosystems 18

Towards FAIR Data Stream Processing Ecosystems

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18th international conference on distributed and Event-Based systems (DEBS)

作者： Tirpitz, Liam Rhein Westfal TH Aachen Aachen Germany

ISBN: (纸本)9798400704437

With the rapid growth of data from heterogeneous, distributed sources, data streams need to be increasingly processed in the cloud-edge continuum. Processing is distributed between diverse edge environments and homogeneous, but powerful data centers, to optimally utilize available resources, alleviate infrastructure bottlenecks and follow the principle of data locality. Compared to cloud infrastructure, compute and data resources on the edge are distributed across geographical regions, infrastructures and organizational units with independent data processing systems. However, existing data stream processing frameworks provide integrated systems, which require matching software components to be used across the whole, distributed infrastructure or even assume centralized control over all resources. We argue, that this cloud-like, centralized approach does not fit to the decentralized nature of the edge environment. Focusing on fully integrated systems, which are either limited to single organizational units or require a certain degree of homogeneity limits data sovereignty and the overall potential of distributed data stream processing on the edge. Instead, we propose to develop data stream processing as part of data ecosystems, and connect locally independent and sovereign systems through a lightweight set of common standards, protocols, and semantic descriptions.

关键词： edge computing distributed data stream processing data ecosystems interoperability FAIR

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Agriculture 4.0 - Integrated Smart Irrigation System 15

Agriculture 4.0 - Integrated Smart Irrigation System

引用

15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Poonia, Ajay Garg, Tanmay Mishra, Ojaswini Batra, Eashan Ganeshan, R. Vit Bhopal University School of Computing Science and Engineering Bhopal-Indore Highway Bhopal Sehore466114 India

ISBN: (纸本)9798350370249

To combat water inefficiencies and subpar agricultural yields, Agriculture 4.0 incorporates Industry 4.0 technologies into farming methods. Examples of these solutions include smart irrigation systems. Agriculture 4.0 - Integrated Smart Irrigation system optimize water usage in real-time, encouraging conservation and soil health, by utilizing sensors and machine learning models. Farmers may monitor and control irrigation operations remotely, increasing efficiency, with technologies like web interfaces, Augmented Reality and IoT connectivity. these systems are flexible and scalable, accommodating a range of crops and local conditions. they hold great potential to transform agricultural water management and promote sustainability. © 2024 IEEE.

关键词： Agriculture 4.0 Augmented Reality Cloud Internet of things Machine Learning SARIMA Sensor Smart Irrigation System Time Series Forecasting

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LLaMPS: Large Language Models Placement System 15

LLaMPS: Large Language Models Placement System

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

作者： Bandamudi, Likhith Singh, Ravi Kumar Kunde, Shruti Mishra, Mayank Singhal, Rekha TCS Res Mumbai Maharashtra India

ISBN: (纸本)9798400704451

the rapid expansion of Large Language Models (LLMs) presents significant challenges in efficient deployment for inference tasks, primarily due to their substantial memory and computational resource requirements. Many enterprises possess a variety of computing resources-servers, VMs, PCs, laptops-that cannot individually host a complete LLM. Collectively, however, these resources may be adequate for even the most demanding LLMs. We introduce LLaMPS, a novel tool, designed to optimally distribute blocks 1 of LLMs across available computing resources within an enterprise. LLaMPS leverages the unused capacities of these machines, allowing for the decentralized hosting of LLMs. this tool enables users to contribute their machine's resources to a shared pool, facilitating others within the network to access and utilize these resources for inference tasks. At its core, LLaMPS employs a sophisticated distributed framework to allocate transformer blocks of LLMs across various servers. In cases where a model is pre-deployed, users can directly access inference results (GUI and API). Our tool has undergone extensive testing with several open-source LLMs, including BLOOM-560m, BLOOM-3b, BLOOM-7b1, Falcon40b, and LLaMA-70b. It is currently implemented in a real-world enterprise network setting, demonstrating its practical applicability and effectiveness.

关键词： LLMs distributed Inference Optimal block placement

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