Cloud Computing, Big Data & Emerging Topics : 10th Conference, JCC-BD&ET 2022, La Plata, Argentina, June 28–30, 2022, Proceedings / / edited by Enzo Rucci, Marcelo Naiouf, Franco Chichizola, Laura De Giusti, Armando De Giusti

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (146 pages)
• Disciplina: 005.7; 004.6782
• Soggetto topico: Computer engineering; Computer networks; Computers; Application software; Computer Engineering and Networks; Computing Milieux; Computer and Information Systems Applications
• Persona (resp. second.): RucciEnzo
• Nota di contenuto: Intro -- Preface -- Organization -- Contents -- Cloud and High-Performance Computing -- File Access Patterns of Distributed Deep Learning Applications -- 1 Introduction -- 2 Related Work -- 3 Characterizing the I/O Patterns Models of DDL Applications -- 3.1 Software Stack DL -- 3.2 File Access Pattern -- 4 Experimental Data-extraction for File Access Pattern Modelling Characterization -- 4.1 Experimental Environment -- 4.2 Mechanisms Used to Characterize File Access Patterns -- 4.3 Characterization of File Access Patterns to the CIFAR-10 Dataset -- 4.4 Characterization of File Access Patterns to the MNIST Dataset -- 5 Conclusions -- References -- A Survey on Billing Models for Cloud-Native Applications -- 1 Introduction -- 2 Systematic Literature Review -- 3 Main Findings and Discussion -- 4 Conclusions and Research Opportunities -- References -- Performance Analysis of AES on CPU-GPU Heterogeneous Systems -- 1 Introduction -- 2 Background -- 2.1 AES Algorithm -- 2.2 Characterization of Heterogeneous Systems -- 2.3 Related Work -- 3 Previous Implementations of AES -- 3.1 AES for Multicore CPU -- 3.2 AES for Single-GPU and Multi-GPU -- 4 AES for CPU-GPU Heterogeneous Systems -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- Network Traffic Monitor for IDS in IoT -- 1 Introduction -- 2 Network Traffic Monitor Architecture -- 3 Deployment and Testing -- 3.1 Creating Topology Elements. OpenFlow Switch -- 3.2 Creating Links Between Components -- 3.3 Connecting the Monitor -- 3.4 Creating Host 1 and Host 2 -- 3.5 Connecting Host 1 and Host 2 -- 4 Creating SDN Controller and Traffic Sniffer -- 5 Conclusions and Future Work -- References -- Crane: A Local Deployment Tool for Containerized Applications -- 1 Introduction -- 2 Container Management Architecture Precedents -- 2.1 SWITCH -- 2.2 COCOS. ; 2.3 Lightweight Kubernetes Distributions -- 3 Design Evolution of Crane -- 3.1 Instances Load Balancing -- 3.2 Container Automatic Scaling -- 3.3 Detected Implementation Problems -- 4 Conclusions and Future Work -- References -- Machine and Deep Learning -- Multi-class E-mail Classification with a Semi-Supervised Approach Based on Automatic Feature Selection and Information Retrieval -- 1 Introduction -- 2 Background -- 3 Research Methodology -- 3.1 Description of the Dataset -- 3.2 Labeling of Documents -- 3.3 Email Indexing -- 3.4 Feature Selection Strategies -- 3.5 Retrieval of E-mails -- 3.6 Generation of the Classification Models -- 4 Experiments -- 5 Conclusions -- References -- Applying Game-Learning Environments to Power Capping Scenarios via Reinforcement Learning -- 1 Introduction -- 2 The RLlib and Gym Frameworks -- 2.1 RLlib -- 2.2 Gym -- 3 RL for Resource Management -- 4 Casting a Power Capping Scenario with Gym -- 4.1 Defining States -- 4.2 Defining Actions and Rewards -- 5 Experimental Results -- 5.1 Analysis Under Different Power Caps -- 5.2 Impact of the State and Action Definitions -- 5.3 Behaviour Under Different Workloads -- 6 Related Work -- 7 Conclusions -- References -- Solving an Instance of a Routing Problem Through Reinforcement Learning and High Performance Computing -- 1 Introduction -- 2 Previous Concepts -- 2.1 Vehicle Routing Problem -- 2.2 Computational Intelligence -- 2.3 Agents and Their Learning -- 2.4 High Performance Computing in GPU -- 3 Prescriptive Model to RT-CUD-VRP -- 3.1 Environment -- 3.2 Agent Actions -- 3.3 Observations -- 3.4 Rewards -- 3.5 Value Function and Policy -- 4 Experimental Study -- 5 Conclusions and Future Works -- References -- Virtual Reality -- A Cross-Platform Immersive 3D Environment for Algorithm Learning -- 1 Introduction -- 2 Related Works -- 3 Motivation -- 3.1 R-Info. ; 4 3D Mobile Application Development -- 5 Results -- 6 Conclusions -- 7 Future Works -- References -- Author Index.
• Sommario/riassunto: This book constitutes the revised selected papers of the 10th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2022, held in La Plata, Argentina*, in June-July 2022. The 9 full papers were carefully reviewed and selected from a total of 23 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing; Machine and Deep Learning; Cloud and High-Performance Computing, Machine and Deep Learning, and Virtual Reality.
• Titolo autorizzato: Cloud Computing, Big Data & Emerging Topics
• ISBN: 3-031-14599-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910586577603321
• Lo trovi qui: Univ. Federico II
• Serie: Communications in Computer and Information Science, . 1865-0937 ; ; 1634