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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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An Improvised Genetic Algorithm Technique on Task Scheduling in Edge and Cloud computing systems 15

An Improvised Genetic Algorithm Technique on Task Scheduling...

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

作者： Pitty, Nagarjuna Hemalatha, N. Venkata Ramana, K. Sajitha, L.P. Ramesh Babu, P. Babu, M.Dinesh Indian Institute of Science Karnataka Bengaluru India Tamilnadu Chennai India QIS College of Engineering and Technology Department of CSE Andrapradesh Ongole India R.M.K Engineering College Department of Computer Science and Business System Tamilnadu Kavaraipettai India Wollega University College of Engineering and Technology Department of Computer Science Oromia Region Nekemte Ethiopia Rajalakshmi Institute of Technology Department of Mechanical Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350370249

In current years, part cloud computing systems have received huge adoption because of their capability to provide low latency and high bandwidth offerings to users. however, one of the primary challenges in these structures is green undertaking scheduling in the presence of dynamic and heterogeneous sources. To deal with this trouble, genetic set of rules (GA) optimization strategies were proposed as a potential solution. this approach utilizes genetic operators, which include mutation and crossover, to conform a populace of potential solutions and choose the best one. by way of considering various constraints and objectives, consisting of strength consumption and make span, the GA-primarily based project scheduling technique can efficaciously optimize useful resource allocation in area cloud computing structures, at the end improving their performance and resource utilization. © 2024 IEEE.

关键词： Scheduling algorithms

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Probabilistic Feamework for Modeling and Analyzing Complex Networks 15

Probabilistic Feamework for Modeling and Analyzing Complex N...

引用

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

作者： Nair, Lekshmi S Cheriyan, Jo Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India SAINTGITS College of Engineering Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350370249

Complex networks, characterised by intricate structures arising from the relationships and interactions among their constituent elements, play a pivotal role in various domains such as social networks, biological systems, and technological infrastructures. the evolution and dynamics of complex networks are analysed for various applications such as link prediction, information diffusion, and recommendation systems. Recent research has focused on the study of structural motifs and the probabilistic analysis of motifs to capture the dynamics of the network. the motifs, considered building blocks of the larger network structure, are recurrent patterns in the network. this study focuses on the different probabilistic models built on triads, which are sets of three interconnected nodes. We investigate six different probability distributions, including Poisson, Gamma, Exponential, Power Law, Gaussian, Lorentzian, and Gaussian Mixture Models (GMMs) that can be applied in the complex network to capture the insight of the connectivity pattern. the applications of this research extend to refining network models, improving simulations, and enhancing our understanding of the likelihood of observing distinct patterns within complex social networks. © 2024 IEEE.

关键词： Poisson distribution

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Leveraging a Custom Multi Modal OCR System for Marksheet Data Extraction 15

Leveraging a Custom Multi Modal OCR System for Marksheet Dat...

引用

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

作者： Kalra, Bhavvuk D. Suresh, Sreedath Sanjay Adithya, M. Shanmuga Priya, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

In the current educational landscape, the transition towards digitalization has become crucial. However, the manual entry of data from traditional physical marksheets into digital systems remains a significant bottleneck in the process. Educators often spend significant amount of time and effort entering data into electronic systems, which could be better utilized for more productive *** this paper, we offer a solution to this problem. We propose an automated system that leverages Optical Character Recognition (OCR) Technology to streamline the extraction of essential information from marksheets. We discuss about the Dataset, preprocessing on the input image, Multi Modal OCR system Structure and other implementation details. We conclude the paper by a comparative study of our Multi Modal OCR system with other Commercially available OCRs like EasyOCR, PaddleOCR and PyTesseract. © 2024 IEEE.

关键词： Character Isolation CNN Data Extraction Digits Handwritten Letters Marksheets Multi Modal Neural Network Optical Character Recognition (OCR) Preprocessing Region of Interest Template Image Text Recognition

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Self-Optimizing distributed Cloud computing with Dynamic Neural Resource Allocation and Fault-Tolerant Multi-Agent systems 4

Self-Optimizing Distributed Cloud Computing with Dynamic Neu...

引用

4th international conference on Ubiquitous computing and Intelligent Information systems, ICUIS 2024

作者： Tripura, N. Divya, P. Chaganti, Koushik Reddy Rao, Kota Venkateswara Rajyalakshmi, P. Naresh, P. Vignan Institute of Technology And Science Dept of Mba Hyderabad India University of the Cumberlands Dept. of Computer and Information Systems KY United States Andhra Loyola Institute of Engineering and Technology Dept of Cse Vijayawada India Tkr College of Engineering and Technology Dept of Cse Hyderabad India Dayananda Sagar University Dept of Cse Bangalore India

ISBN: (纸本)9798331529635

the exponential growth of distributed cloud systems necessitates an intelligent form of workload management that ensures optimum performance, scalability, and reliability. Traditional approaches based on static or heuristic resource allocation and fault-tolerance mechanisms cannot capture the dynamic nature of modern workloads and heterogeneous infrastructures. Drawing from this source of inspiration, this work develops a holistic framework integrating machine learning models that facilitate self-optimizing distributed cloud computing for dynamic workload management. three components are significant in the proposed framework: (1) DNRA;this uses a hybrid GNN and RL architecture for real-time resource allocation, offering up to 25% improvement in resource utilization and a 30% reduction in task latency. (2) Workload-Aware Transfer Learning Scheduler (WATLS), leveraging transfer learning to predict workload dynamics across heterogeneous nodes, achieving 20% higher task throughput and 15% energy savings through proactive scheduling. (3) Multi-Agent Predictive Fault-Tolerant System (MAP-FTS), combining multi-agent systems with predictive fault detection to preemptively redistribute tasks and schedule maintenance, reducing system downtime by 50% and enhancing fault detection accuracy to 90%. the framework integrates novel methodologies towards answering the main challenges of resource allocation, workload prediction, and fault tolerance. the amount of progress achieved through cumulative contributions includes improvement in system reliability and scalability up to 5x their nodes and respective scalable energy efficiency without any degradation of performance. this work paves the pathway for an adaptive and intelligent next generation of distributed cloud systems. © 2024 IEEE.

关键词： distributed cloud

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Green Edge computing Sustainability using Game theoretic Approach 15

Green Edge Computing Sustainability using Game Theoretic App...

引用

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

作者： Swain, Sujata Chakravarty, Krishna Jena, Ajay Kumar Dansana, Jayanti Gourisaria, Mahendra Kumar Bandyopadhyay, Anjan Kalinga Institute of Industrial Technology School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350370249

increasing significance of the edge computing paradigm highlights the need to emphasize how important it is to incorporate sustainable principles into its implementation. this philosophy is best summed up by green edge computing, which outlines a dedication to running systems and services with efficient energy use and low environmental impact. Forward-thinking research projects, like the academic paper mentioned above, analyze the subtleties of green edge computing sustainability through an advanced game-theoretic framework. this groundbreaking work provides stakeholders in edge computing ecosystems with crucial insights by using game theory frameworks to clarify complex stakeholder dynamics and decision-making processes. By carefully examining tactics to improve sustainability, such as resource distribution, energy efficiency optimization, and environmental impact reduction, this academic discussion not only highlights issues but also offers practical ways to encourage sustainable behavior among participants. As a result, the integration of these sophisticated analyses sparks a paradigm change that leads to the achievement of Green computing goals and has a revolutionary effect on the state of technology. © 2024 IEEE.

关键词： Ecological footprint Edge computing Energy efficiency Game theory Green edge computing Sustainable computing

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Exploring the Utility of Graph Methods in HPC thermal Modeling 15

Exploring the Utility of Graph Methods in HPC Thermal Modeli...

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

作者： Guindani, Bruno Molan, Martin Bartolini, Andrea Benini, Luca Politecn Milan Dept Elect Informat & Bioengn Milan Italy Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland

ISBN: (纸本)9798400704451

this work critically examines several approaches to temperature prediction for High-Performance computing (HPC) systems, focusing on component-level and holistic models. In particular, we use publicly available data from the Tier-0 Marconi100 supercomputer and propose models ranging from a room-level Graph Neural Network (GNN) spatial model to node-level models. Our results highlight the importance of correct graph structures and suggest that while graph-based models can enhance predictions in certain scenarios, node-level models remain optimal when data is abundant. these findings contribute to understanding the effectiveness of different modeling approaches in HPC thermal prediction tasks, enabling proactive management of the modeled system.

关键词： High-Performance computing Graph Neural Network thermal Modeling

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Hybrid Cloud Connector: Offloading integration complexities 24

Hybrid Cloud Connector: Offloading integration complexities

引用

17th international systems and Storage conference (SYSTOR)

作者： Kat, Ronen Chen, Doron Factor, Michael Giblin, Chris Ziv, Avi Slominski, Aleksander IBM Res Haifa Israel IBM Res Zurich Switzerland IBM Res New York NY USA

ISBN: (纸本)9798400711817

Regulated enterprises often seek to extend their workloads into the cloud, but are impeded by integration concerns relating to security, governance and compliance. Further, enterprises running mission-critical applications, face throughput and latency challenges due to cloud integration overheads. We present Hybrid Cloud Connector to accelerate on-prem to cloud integration by handling non-functional aspects in lieu of the application, reducing complexity, and centralizing administration via a policy-driven control point.

关键词： distributed computing Hybrid Cloud Application Integration

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distributed computing application for calculation of complex optical fields 16

Distributed computing application for calculation of complex...

引用

16th international conference on Correlation Optics, COR 2023

作者： Gorsky, M. Vatamanitsa, E. Olar, O. Diachenko, L. Galochkin, O. Dovgun, A. Yuriy Fedkovych Chernivtsi National University 2 Kotsjubynskyi Str. Chernivtsi Ukraine

ISBN: (纸本)9781510671829

distributed computing has revolutionized the way complex mathematical calculations are performed. this article explores the application of distributed computing for the calculation of complex integrals which are widely used for numeric simulation of optic fields. Article discusses possible solutions for numeric simulation of optic fields involving distributed computing techniques, and highlights the advantages of this approach. By harnessing the power of distributed computing, researchers can tackle complex optical fields more efficiently than ever before © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： distributed computer systems

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Multicloud Deployment of AI Workflows Using FaaS and Storage Services 15

Multicloud Deployment of AI Workflows Using FaaS and Storage...

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

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

ISBN: (纸本)9781665477062

Multicloud deployments are getting traction for benefits such as agility, avoiding vendor lock-in, resilience, etc. Use of Function-as-a-Service (FaaS) platforms is emerging as a preferred choice for deploying AI workflows. these platforms from different cloud service providers (CSPs) have unique specifications and cost models. the onus is on a user to find a cost-performance optimal mapping of its application workflows to FaaS and its associated services in a multicloud deployment setting. In this work, we have presented an empirical study of multicloud deployment of AI inference-workflows using FaaS and cloud storage services. Our evaluation shows that the cost for executing AI workflow reduces by 83% using an optimal combination of CSPs over a naive deployment. Further, we also propose and evaluate analytical models for estimating the cost of deployment in multicloud using FaaS and storage services.

关键词： Cloud computing Function-as-a-Service Storage Services Performance and Cost Multicloud

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Leveraging distributed Quantum computing for Effective Optimization Solutions 15

Leveraging Distributed Quantum Computing for Effective Optim...

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

作者： Manu, S.E. Ghosh, Rajkumari Seth, Jyoti School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of English Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

Disbursed quantum computing (DQC) is a singular computational paradigm for optimizing complicated optimization problems. It takes advantage of the velocity and c of disbursed computing by combining quantum algorithms with clever use of disbursed computing sources. It affords a novel method for fixing state-of-the-art optimization issues, which are commonly hard to version or remedy with a single laptop. DQC offers the capacity to offer a more efficient and sturdy manner of handling optimization issues in a spread of domain names, which includes finance, logistics, engineering, and machine studying. In DQC, some algorithms exist that incorporate quantum computing techniques for answer discovery (i.e., quantum annealing, variational quantum Eigen solver, and many others.). these algorithms allow the exploration of otherwise intractable answer areas. Quantum computing-based total optimization solves complex problems that are regularly too difficult or too computationally luxurious for an unmarried computer. For example, quantum annealing can solve for global ultimate answers even as parallelizing the hunt efforts to numerous disbursed nodes. At the coronary heart of DQC is its ability to use more than one concurrent resource to find the pleasant answer. By leveraging the allotted computing and parallelizing its efforts, DQC is able to reduce the sources required to clear up complicated optimization problems. © 2024 IEEE.

关键词： Computational Engineering Logistics Paradigm Quantum Scalability

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