Cloud Computing, Big Data & Emerging Topics. 10th Conference, JCC-BD&ET 2022 La Plata, Argentina, June 28–30, 2022 Proceedings

Description

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