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检索条件"任意字段=8th International Symposium on Algorithmic Aspects of Cloud Computing, ALGOCLOUD 2023"

共 12 条记录，以下是1-10 订阅

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8th international symposium on algorithmic aspects of cloud computing, algocloud 2023

8th International Symposium on Algorithmic Aspects of Cloud ...

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8th international symposium on algorithmic aspects of cloud computing, algocloud 2023

ISBN: (纸本)9783031493607

the proceedings contain 13 papers. the special focus in this conference is on algorithmic aspects of cloud computing. the topics include: Olive Leaf Infection Detection Using the cloud-Edge Continuum;intent-Based Allocation of cloud computing Resources Using Q-Learning;a Double-Decision Reinforcement Learning Based Algorithm for Online Scheduling in Edge and Fog computing;real-Time Leakage Zone Detection in Water Distribution Networks: A Machine Learning-Based Stream Processing Algorithm;multi-agent Reinforcement Learning-Based Energy Orchestrator for Cyber-Physical Systems;clustering-Based Numerosity Reduction for cloud Workload Forecasting;planning Workflow Executions over the Edge-to-cloud Continuum;decentralized Algorithms for Efficient Energy Management over cloud-Edge Infrastructures;i-Deliver P&D Engine: A Decentralized Middleware for a Delivery-as-a-Service System;preface;Application of Federated Learning Techniques for Arrhythmia Classification Using 12-Lead ECG Signals.

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7th international symposium on algorithmic aspects of cloud computing, algocloud 2022

7th International Symposium on Algorithmic Aspects of Cloud ...

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7th international symposium on algorithmic aspects of cloud computing, algocloud 2022

ISBN: (纸本)9783031334368

the proceedings contain 6 papers. the special focus in this conference is on algorithmic aspects of cloud computing. the topics include: MAGMA: Proposing a Massive Historical Graph Management System;new Results in Priority-Based Bin Packing;more Sparking Soundex-Based Privacy-Preserving Record Linkage;privacy Preserving Queries of Shortest Path Distances.

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5th international symposium on algorithmic aspects of cloud computing, algocloud 2019

5th International Symposium on Algorithmic Aspects of Cloud ...

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5th international symposium on algorithmic aspects of cloud computing, algocloud 2019

ISBN: (纸本)9783030586270

the proceedings contain 8 papers. the special focus in this conference is on algorithmic aspects of cloud computing. the topics include: Coordinated Data Flow Control in IoT Networks;a Monitoring System for Distributed Edge Infrastructures with Decentralized Coordination;a Container-Driven Approach for Resource Provisioning in Edge-Fog cloud;self-adaptive Container Deployment in the Fog: A Survey;security-Aware Database Migration Planning;scalable and Hierarchical Distributed Data Structures for Efficient Big Data Management.

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Intent-Based Allocation of cloud computing Resources Using Q-Learning 8th

Intent-Based Allocation of Cloud Computing Resources Using Q...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Kokkinos, Panagiotis Varvarigos, Andreas Konidaris, Dimitrios Tserpes, Konstantinos Univ Peloponnese Dept Digital Syst Sparta Greece Natl Tech Univ Athens Inst Commun & Comp Syst ICCS Athens Greece Harokopio Univ Athens Dept Informat & Telemat Athens Greece

ISBN: (纸本)9783031493607;9783031493614

In cloud computing, resource allocation is a critical operation that is usually performed in an infrastructure-aware manner. In practice, however, users often are not able to specify accurately their requirements, while an infrastructure's specific characteristics are not always known. In our work, we assume that users provide their workload requirements in an infrastructure-agnostic manner, by describing their intentions regarding the way the workload should be served, e.g., with high capacity, with low cost etc. Towards this end, we propose the use of a Q-learning based Reinforcement Learning (RL) methodology that translates the users' intentions to efficient resource allocations in a cloud infrastructure. the proposed mechanism is able to improve continuously the allocation of resources based on the user satisfaction and the infrastructure's efficiency. Simulation results showcase the applicability of this approach, investigating various aspects of it.

关键词： Resource allocation Intents Q-learning cloud computing

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algorithmic aspects of Distributed Hash Tables on cloud, Fog, and Edge computing Applications: A Survey 8th

Algorithmic Aspects of Distributed Hash Tables on Cloud, Fog...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Karras, Aristeidis Karras, Christos Schizas, Nikolaos Sioutas, Spyros Zaroliagis, Christos Univ Patras Comp Engn & Informat Dept Patras 26504 Greece Patras Univ Campus Comp Technol Inst & Press Diophantus Patras 26504 Greece

ISBN: (纸本)9783031493607;9783031493614

In the current era, where data is expanding due to the unforeseen volume, velocity, and variety of data types produced by IoT devices, there is an imperative need to manage such data in remote IoT environments. However, these complexities have been inadequately addressed by conventional data management methods. In such scenarios, Distributed Hash Tables (DHTs) have emerged as an effective solution for efficient data storage and retrieval. Conversely, the dynamizature of IoT data presents its own set of challenges, such as decreased performance, inconsistent data, and increased overhead. To improve the performance of DHTs, we examine their algorithmic properties in cloud, fog, and edge computing environments, taking into account network designs, resource availability, latency requirements, and data proximity. this survey explores the adaptation of algorithmic elements in DHTs for optimal data administration in these cloud computing environments. Moreover, we examine advanced techniques such as effective hashing, adaptive routing, defect tolerance mechanisms, and load balancing. In addition, we address the challenges of managing vast and diverse volumes of IoT data, taking into account the unique features and constraints of cloud, fog, and edge environments. We also conduct contemporary research on security and privacy, focusing on algorithmic and architectural solutions for data integrity, confidentiality, and availability. this work enhances our comprehension of dynamic DHT algorithms and their potential for effective data management across multiple computing paradigms by investigating state-of-the-art research.

关键词： DHTs cloud computing Fog computing Edge computing IoT Systems

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Planning Workflow Executions over the Edge-to-cloud Continuum 8th

Planning Workflow Executions over the Edge-to-Cloud Continuu...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Tzortzi, Marianna Kleitsikas, Charalampos Politis, Agis Niarchos, Sotirios Doka, Katerina Koziris, Nectarios Natl Tech Univ Athens Comp Syst Lab Athens Greece

ISBN: (纸本)9783031493607;9783031493614

In the ever growing field of Data Science, cloud computing is a well established computational paradigm, while Edge computing is an emerging and promising alternative when it comes to the novel challenges introduced in the Internet of things (IoT) landscape. the combination of the two as a unified paradigm forms the Edge-to-cloud continuum, which allows the execution of applications and services to span both edge and cloud resources in a transparent way. In such heterogeneous and volatile environments, the scheduling of data intensive workloads is a difficult task, usually performed manually and requiring careful and educated decisions on the type of devices used to optimally exploit the underlying hardware and achieve any user-defined higher level policy. In this paper we present the EC-Planner, a planning component for Edge-cloud environments, which can make intelligent, automated decisions both on how and where to map arbitrary data analytics tasks to the underlying heterogeneous infrastructure, which may consist of a mix of devices and processing units, including CPUs and hardware accelerators both in the cloud and at the Edge.

关键词： Program processors

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Clustering-Based Numerosity Reduction for cloud Workload Forecasting 8th

Clustering-Based Numerosity Reduction for Cloud Workload For...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Rossi, Andrea Visentin, Andrea Prestwich, Steven Brown, Kenneth N. Univ Coll Cork SFI CRT Artificial Intelligence Cork Ireland Univ Coll Cork Sch Comp Sci Cork Ireland

ISBN: (纸本)9783031493607;9783031493614

Finding smaller versions of large datasets that preserve the same characteristics as the original ones is becoming a central problem in Machine Learning, especially when computational resources are limited, and there is a need to reduce energy consumption. In this paper, we apply clustering techniques for wisely selecting a subset of datasets for training models for time series prediction of future workload in cloud computing. We train Bayesian Neural Networks (BNNs) and state-of-the-art probabilistic models to predict machine-level future resource demand distribution and evaluate them on unseen data from virtual machines in the Google cloud data centre. Experiments show that selecting the training data via clustering approaches such as Self Organising Maps allows the model to achieve the same accuracy in less than half the time, requiring less than half the datasets rather than selecting more data at random. Moreover, BNNs can capture uncertainty aspects that can better inform scheduling decisions, which state-of-the-art time series forecasting methods cannot do. All the considered models achieve prediction time performance suitable for real-world scenarios.

关键词： cloud computing Workload Prediction Clustering Bayesian Neural Network Deep Learning

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Decentralized Algorithms for Efficient Energy Management over cloud-Edge Infrastructures 8th

Decentralized Algorithms for Efficient Energy Management ove...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Karras, Aristeidis Karras, Christos Giannoukou, Ioanna Giotopoulos, Konstantinos C. Tsolis, Dimitrios Karydis, Ioannis Sioutas, Spyros Univ Patras Comp Engn & Informat Dept Patras 26504 Greece Univ Patras Dept Management Sci & Technol Patras 26334 Greece Univ Patras Dept Hist & Archaeol Patras 26504 Greece Ionian Univ Dept Informat Corfu 49100 Greece

ISBN: (纸本)9783031493607;9783031493614

this paper presents an innovative approach to overcoming the limitations of traditional cloud-centric architectures in the evolving Internet of things (IoT) landscape. We introduce a set of novel decentralized algorithms boosting Mobile Edge computing (MEC), a paradigm shift towards placing computational resources near data sources, thus boosting real-time processing and energy efficiency. Our approach addresses the challenges of managing distributed cloud-edge infrastructures in high-mobility environments, such as drone networks. Utilizing the Random Waypoint Model to anticipate device trajectories, our algorithms ensure effective resource allocation, enhanced load balancing, and improved Quality of Service (QoS). An in-depth complexity analysis further improves the scalability and performance of our method, demonstrating their ability to optimize energy efficiency and minimize latency, offering optimized offloading strategies in dynamic IoT environments.

关键词： Decentralized computing cloud computing Edge computing IoT Systems Energy Management High-mobility Environments

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algorithmic aspects of cloud computing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Ioannis Chatzigiannakis Ioannis Karydis

ISBN: (数字)9783031493614

ISBN: (纸本)9783031493607

this book constitutes revised selected papers from the 8th international symposium on algorithmic aspects of cloud computing, algocloud 2023, held in Amsterdam, the Netherlands, on September 5, 2023.;the 13 full papers included in this book were carefully reviewed and selected from 24 submissions. they focus on algorithmic aspects of computing and data management in modern cloud-based systems interpreted broadly so as to include edge- and fog-based systems, cloudlets, cloud micro-services, virtualization environments, decentralized systems, as well as dynamic networks.

关键词： theory of Computation Computer Communication Networks Special Purpose and Application-Based Systems Data Structures and Information theory Computer Applications Computer System Implementation

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Olive Leaf Infection Detection Using the cloud-Edge Continuum 8th

Olive Leaf Infection Detection Using the Cloud-Edge Continuu...

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8th international symposium on algorithmic aspects of cloud computing (algocloud)

作者： Sarantakos, themistoklis Gutierrez, Daniel Mauricio Jimenez Amaxilatis, Dimitrios SparkWorks Ltd Galway Ireland Sapienza Univ Rome Rome Italy

ISBN: (纸本)9783031493607;9783031493614

the use of computer vision, deep learning, and drones has revolutionized agriculture by enabling efficient crop monitoring and disease detection. Still, many challenges need to be overcome due to the vast diversity of plant species and their unique regional characteristics. Olive trees, which have been cultivated for thousands of years, present a particularly complex case for leaf-based disease diagnosis as disease symptoms can vary widely, both between different plant variations and even within individual leaves on the same plant. this complexity, coupled with the susceptibility of olive groves to various pathogens, including bacterial blight, olive knot, aculus olearius, and olive peacock spot, has hindered the development of effective disease detection algorithms. To address this challenge, we have devised a novel approach that combines deep learning techniques, leveraging convolutional neural networks, vision transformers, and cloud computing-based models. Aiming to detect and classify olive tree diseases the experimental results of our study have been highly promising, demonstrating the effectiveness of the combined transformer and cloud-based machine learning models, achieving an impressive accuracy of approximately 99.6% for multiclass classification cases including healthy, aculus olearius, and peacock spot infected leaves. these results highlight the potential of deep learning models in tackling the complexities of olive leaf disease detection and the need for further research in the field.

关键词： Computer Vision Olive Leaf Infection Machine Learning Image Analysis

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