Cloud Computing and Services Science: 11th International Conference, CLOSER 2021, Virtual Event, April 28–30, 2021, Revised Selected Papers

Preface
Organization
Contents
Tailoring Technology-Agnostic Deployment Models to Production-Ready Deployment Technologies
	1 Introduction
	2 Background and Motivation
		2.1 Deployment Models, Deployment Technologies, and EDMM
		2.2 Motivating Scenario
	3 Extending the EDMM Approach with Tailoring Support
		3.1 Step 1: Technology-Independent Application Modeling
		3.2 Step 2: Technology Decision Support and Tailoring for EDMM Models
		3.3 Step 3: Transform EDMM Model into DTSM
		3.4 Step 4: Technology-specific Deployment Execution
	4 The Extended EDMM Modeling and Transformation Framework
		4.1 Architecture of the Extended EDMM Framework
		4.2 Prototypical Implementation
	5 Case Study and Evaluation
		5.1 First Deployment of PetClinic
		5.2 Migrating the Deployment of PetClinic to Other Technologies
		5.3 Migrating to a PaaS Deployment of PetClinic
		5.4 Migrating to a Technology Not Supporting the Deployment
	6 Related Work
	7 Conclusions
	References
Toward Secure VMs Allocation: Analysis of VMs Allocation Behaviours in the Cloud Computing Environments
	1 Introduction
	2 Related Work
	3 Problem Formulation
		3.1 Threat Model
		3.2 Secure VMs Allocation Model
	4 Secure VMs Allocation Algorithm
		4.1 Secure Stacking Algorithm (SS)
	5 Evaluation
		5.1 Allocation Behaviours Comparison
		5.2 Experimental Setup
		5.3 Results of Malicious Co-residency Respect to VMs Type and Under Limited Resources Availability
		5.4 Results of VMs Migrations
		5.5 Results of PMs Usage
	6 Conclusion
	References
Risk Analysis Automation Process in IT Security for Cloud Applications
	1 Introduction
	2 State of the Art
	3 Methodology
	4 System Modeling
	5 Threat Modelling
	6 Risk Rating
		6.1 Threat Agent Selection and Rating
		6.2 Vulnerability Factors
		6.3 Impact Factors
		6.4 Overall Evaluation and Input Collection
	7 Case Study
		7.1 Modeling
		7.2 Threat Selection
		7.3 Risk Analysis
	8 Conclusion
	References
AI Quality Engineering for Machine Learning Based IoT Data Processing
	1 Introduction
	2 Explainable AI and AI Engineering
	3 Continuous AI Data and Model Quality Management
		3.1 Quality Principles
		3.2 IoT Use Case
	4 DaaS Quality Assessment and Problem Cause Analysis
		4.1 Quality Layers
		4.2 MAPE-K Service Quality Loop
	5 An IoT-Edge Traffic Management Use Case
		5.1 Quality and Symptom Analysis
		5.2 Root Cause Analysis and Remedial Action Recommendation
		5.3 Step 1 Metric Calculation and Step 2 Problem Assessment
		5.4 Step 3 – Root Cause Analysis and Recommendation
		5.5 Transferability
	6 Related Work
	7 Conclusions
	References
Quality of Service Support Through a Self-adaptive System in Edge Computing Environments
	1 Introduction
	2 Related Work
	3 System Architecture
		3.1 Proposed QoS Framework
		3.2 Algorithms
	4 Experimental Design
		4.1 Experiments’ Objective and Scenario
		4.2 Workload Dataset
		4.3 Evaluation Metrics
		4.4 Simulation Setting
	5 Performance Evaluation
		5.1 Objectives’ Evaluation
		5.2 Results Discussion
		5.3 Framework Design
		5.4 Results Summary and Recommendations
	6 Conclusion and Future Work
	References
Author Index