Cloud Computing, Big Data & Emerging Topics: 10th Conference, JCC-BD&ET 2022, La Plata, Argentina, June 28–30, 2022, Proceedings (Communications in Computer and Information Science Book 1634)
معرفی کتاب «Cloud Computing, Big Data & Emerging Topics: 10th Conference, JCC-BD&ET 2022, La Plata, Argentina, June 28–30, 2022, Proceedings (Communications in Computer and Information Science Book 1634)» نوشتهٔ Enzo Rucci, Marcelo Naiouf, Franco Chichizola, Laura De Giusti, Armando De Giusti، منتشرشده توسط نشر Springer International Publishing Springer در سال 2022. این کتاب در فرمت pdf، زبان انگلیسی ارائه شده است.
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. 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
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