Service-Oriented Computing 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14-17, 2020, Proceedings
This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full, 16 short, and 3 industry papers...
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Main Authors | , , , , , |
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Format | eBook Conference Proceeding |
Language | English |
Published |
Cham
Springer Nature
2020
Springer International Publishing AG Springer International Publishing |
Edition | 1 |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3030653102 9783030653101 3030653099 9783030653095 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-030-65310-1 |
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Abstract | This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full, 16 short, and 3 industry papers included in this volume were carefully reviewed and selected from 137 submissions. They were organized in topical sections named: microservices; Internet of Things; services at the edge; machine learning for service oriented computing; smart data and smart services; service oriented technology trends; industry papers. |
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AbstractList | This book constitutes the proceedings of the 18th International Conference on Service-Oriented Computing, ICSOC 2020, which was planned to take place in Dubai, UAE, during December 14-17, 2020. Due to the COVID-19 pandemic the conference was held online. The 23 full, 16 short, and 3 industry papers included in this volume were carefully reviewed and selected from 137 submissions. They were organized in topical sections named: microservices; Internet of Things; services at the edge; machine learning for service oriented computing; smart data and smart services; service oriented technology trends; industry papers. |
Author | Hacid, Hakim Benatallah, Boualem Kafeza, Eleanna Martinelli, Fabio Motahari, Hamid Bouguettaya, Athman |
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Subtitle | 18th International Conference, ICSOC 2020, Dubai, United Arab Emirates, December 14-17, 2020, Proceedings |
TableOfContents | 2 Background and Motivation -- 3 Fast Replica for Fog Computing -- 3.1 Implementation Details -- 4 Evaluation -- 5 Related Work -- 6 Conclusion -- References -- On Discovering Data Preparation Modules Using Examples -- 1 Introduction -- 2 Background -- 2.1 Data Example Generation -- 3 Module Discovery -- 3.1 Feedback-Based Discovery of Scientific Modules -- 3.2 Incremental Ranking of Candidate Modules -- 4 Concluding Remarks -- References -- Internet of Things -- Accelerate Personalized IoT Service Provision by Cloud-Aided Edge Reinforcement Learning: A Case Study on Smart Lighting -- 1 Introduction -- 2 Background and Related Work -- 2.1 Edge-Cloud Collaboration -- 2.2 Schemes for Edge-Cloud Collaborated Model Training -- 2.3 Fast Personalization by Federated Reinforcement Learning -- 3 System Model for Cloud-Aided Edge Reinforcement Learning -- 3.1 Basic Ideas of the Cloud-Aided Edge Reinforcement Learning -- 3.2 Knowledge Fusion Strategies on Edge Nodes -- 4 Reinforcement Learning for Providing Personalized Illuminance in Smart Lighting -- 5 Experiments -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Experimental Results -- 5.4 Quantitative Comparison of the Performance of Different Algorithms -- 6 Conclusion -- References -- A Game-Based Secure Trading of Big Data and IoT Services: Blockchain as a Two-Sided Market -- 1 Introduction -- 1.1 Motivating Example -- 1.2 Related Work and Problem Statement -- 1.3 Contribution -- 2 Proposed Model for Secure Trading of Data -- 2.1 Model Description: A Double Two-Sided Game Formulation -- 2.2 Players Demands and Utility Functions -- 2.3 Game Equilibrium -- 3 Simulation and Empirical Analysis -- 3.1 Simulation Setup -- 3.2 Shared Revenues and Computational Costs over Externalities -- 3.3 Data Consumers' Demand and Computational Unit Supply -- 3.4 Data Providers and Blockchain Payoffs -- 4 Conclusion Intro -- Preface -- Organization -- A Tribute to Florian Daniel's Contributions to Service-Oriented Computing -- Contents -- Microservices -- Optimal Evolution Planning and Execution for Multi-version Coexisting Microservice Systems -- 1 Introduction -- 2 Related Work -- 3 Version Dependency Model -- 4 Optimization Algorithm with Version Dependency -- 4.1 Problem Definition -- 4.2 Optimal Evolution Algorithm -- 5 Infrastructure and Programming Framework with Version Dependency -- 5.1 Overview -- 5.2 Version Dependency Model Generation -- 5.3 Version Dependency Based Requesting -- 5.4 Version Coexistence Evolution Plan Executing -- 6 Experiments -- 6.1 Experiment Setup -- 6.2 Scenario 1: Service Upgrade -- 6.3 Scenario 2: New User Requirements -- 7 Conclusion -- References -- Topology-Aware Continuous Experimentation in Microservice-Based Applications -- 1 Introduction -- 2 Related Work -- 3 Characterizing Change Types -- 3.1 Microservice-Based Application -- 3.2 Interaction Graph -- 3.3 Topological Change Types -- 4 Ranking Identified Changes -- 4.1 Constructing the Topological Difference -- 4.2 Traversing the Topological Difference -- 4.3 Subtree Complexity Heuristic -- 4.4 Response Time Analysis Heuristic -- 4.5 Hybrid Heuristic -- 5 Ranking Quality Evaluation -- 5.1 Setup -- 5.2 Scenario 1: Revisiting the Sample Application -- 5.3 Scenario 2: Breaking Changes -- 5.4 Discussion -- 6 Limitations -- 7 Conclusion -- References -- Charting Microservices to Support Services' Developers: The Anaximander Approach -- 1 Introduction -- 2 Related Work -- 3 The Anaximander approach -- 3.1 Modelling the Map as a Graph -- 3.2 Modelling Probes as Functions -- 4 Exploring a Reference Architecture -- 5 Conclusions and Perspectives -- References -- Fast Replica of Polyglot Persistence in Microservice Architectures for Fog Computing -- 1 Introduction 4.3 Comparing CES-RS Against the State-of-the-Art -- 5 Related Work -- 6 Conclusion -- References -- Impact of Service- and Cloud-Based Dynamic Routing Architectures on System Reliability -- 1 Introduction -- 2 Related Work and Background -- 2.1 Related Work -- 2.2 Background: Dynamic Routing Architecture Patterns -- 3 Model of Request Loss During Crashes -- 3.1 Definition of Internal and External Loss -- 3.2 Bernoulli Process to Model Request Loss -- 4 Empirical Validation -- 4.1 Experimental Planning -- 4.2 Results -- 5 Discussion and Conclusions -- References -- Pricing in the Competing Auction-Based Cloud Market: A Multi-agent Deep Deterministic Policy Gradient Approach -- 1 Introduction -- 2 Related Work -- 3 Basic Settings -- 3.1 Cloud Users -- 3.2 Cloud Providers -- 4 MADDPG Algorithm -- 4.1 Partially Observable Markov Game -- 4.2 Multi-Agent Deep Deterministic Policy Gradient -- 5 Experiments -- 5.1 Parameter Settings -- 5.2 Training -- 5.3 Strategy Evaluation -- 6 Conclusion -- References -- Dynamic Edge User Allocation with User Specified QoS Preferences -- 1 Introduction -- 2 A Motivating Example -- 3 System Model and ILP Formulation -- 4 Heuristic Solution -- 5 Experiments and Analysis of Results -- 6 Conclusion and Future Work -- References -- Fault-Tolerating Edge Computing with Server Redundancy Based on a Variant of Group Degree Centrality -- 1 Introduction -- 2 Problem Statement -- 2.1 Definitions -- 2.2 ESR Model -- 2.3 Problem Hardness -- 3 Approach Design -- 3.1 Optimal Approach -- 3.2 Heuristic Approach -- 4 Experimental Evaluation -- 4.1 Baseline Approaches -- 4.2 Experimental Setup -- 4.3 Experimental Results -- 5 Related Work -- 6 Conclusion -- References -- TD-EUA: Task-Decomposable Edge User Allocation with QoE Optimization -- 1 Introduction -- 2 Motivating Example -- 3 Problem Formulation -- 4 Approaches References -- A Dynamic Cost Model to Minimize Energy Consumption and Processing Time for IoT Tasks in a Mobile Edge Computing Environment -- 1 Introduction -- 2 Dynamic Cost Minimization Model -- 2.1 Local Computing in the IoT Device -- 2.2 Local Computing in the MEC Server -- 2.3 Remote Computing in the Cloud -- 2.4 System Dynamic Cost Minimization Equation -- 3 Time and Energy Minimization Scheduler (TEMS) -- 4 Evaluation -- 5 Conclusion -- References -- Automated SLA Negotiation in a Dynamic IoT Environment - A Metaheuristic Approach -- 1 Introduction -- 2 Related Work -- 3 Negotiation Strategy -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Result -- 5 Conclusion and Future Work -- References -- PATRIoT: A Data Sharing Platform for IoT Using a Service-Oriented Approach Based on Blockchain -- 1 Introduction -- 2 Related Work -- 3 System Model Overview -- 3.1 Core Components Description -- 3.2 Blockchain-Based IoT Data Sharing Process -- 4 Experiments and Result Analysis -- 4.1 Computation Time Cost -- 4.2 Cost Overhead -- 4.3 PATRIoT Platform in Legislation Context: GDPR Compliance -- 5 Conclusion -- References -- Services at the Edge -- Energy Minimization for Cloud Services with Stochastic Requests -- 1 Introduction -- 2 Related Work -- 3 Cloud Service System Model and Problem Description -- 3.1 Cloud Service System Model -- 3.2 Problem Description -- 4 Energy Minimization Algorithm -- 5 Experiments -- 5.1 Parameter Calibration -- 5.2 Algorithm Comparison -- 6 Conclusion -- References -- Scalable Joint Optimization of Placement and Parallelism of Data Stream Processing Applications on Cloud-Edge Infrastructure -- 1 Introduction -- 2 Proposed Model -- 2.1 System Model -- 2.2 Problem Formulation -- 3 Resource Selection Technique -- 4 Performance Evaluation -- 4.1 Experimental Setup -- 4.2 Resolution Time Versus Solution Quality 4.1 Optimal Approach -- 4.2 Problem Hardness -- 4.3 Heuristic Approach -- 5 Experiments -- 5.1 Experimental Setup and Dataset -- 5.2 Competing Methods and Evaluation Metrics -- 5.3 Experimental Results and Analysis -- 5.4 Performance Impacts of Parameters -- 6 Related Work -- 7 Conclusion and Future Work -- References -- A Decentralized Reactive Approach to Online Task Offloading in Mobile Edge Computing Environments -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 System Model -- 3.2 Problem Formulation -- 4 Proposed Dynamic-Learning-Based Approach -- 5 Experiments and Analysis -- 5.1 Experiment Settings -- 5.2 Baseline Approaches -- 5.3 Responsiveness Evaluation -- 5.4 Efficiency Evaluation -- 6 Conclusion and Further Work -- References -- Mobility-Aware Service Placement for Vehicular Users in Edge-Cloud Environment -- 1 Introduction -- 2 Problem Formulation -- 3 Detailed Methodology -- 3.1 Optimal Algorithm -- 3.2 Window-Based Optimal Algorithm -- 3.3 Heuristic Using FCFS -- 4 Experimental Results -- 4.1 Dataset Generation -- 4.2 Comparative Methods -- 4.3 Analysis on Real Dataset -- 4.4 Analysis on Synthetically Generated Dataset -- 4.5 Impact of Tunable Parameters -- 5 Related Work -- 6 Conclusion -- References -- Machine Learning for Service Oriented Computing -- Feature Model-Guided Online Reinforcement Learning for Self-Adaptive Services -- 1 Introduction -- 1.1 Problem Statement -- 1.2 Contributions -- 2 Fundamentals -- 3 Feature-Model-Guided Exploration -- 3.1 Integration into Reinforcement Learning -- 3.2 Leveraging the Feature Model Structure for Exploration -- 3.3 Leveraging Feature Model Differences for Exploration -- 4 Experiments -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- References -- FAST: A Fairness Assured Service Recommendation Strategy Considering Service Capacity Constraint -- 1 Introduction 2 Related Work |
Title | Service-Oriented Computing |
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