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

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