This book constitutes the refereed proceedings of the 23rd IFIP WG 6.1 International Conference on Distributed Applications and Interoperable Systems, DAIS 2023, held in Lisbon, Portugal, during June 19-23, 2023, as part of the 18th International Federated Conference on Distributed Computing Techniques, DisCoTec 2023. The 7 full papers presented in this book were carefully reviewed and selected from 13 submissions. The papers cover the following topics: distributed algorithms and systems; data management; and distributed architectures. Foreword Preface DAIS 2023 Organization Contents Distributed Algorithms and Systems TADA: A Toolkit for Approximate Distributed Agreement 1 Introduction 2 Related Work 3 Approximate Distributed Agreement 3.1 Mean-Subsequence-Reduce (MSR) 4 A Template for Approximate Agreement 5 How to Use the Toolkit 5.1 Specifying Generic Primitive Variables 6 Evaluation 7 Conclusion References Studying the Workload of a Fully Decentralized Web3 System: IPFS 1 Introduction 2 IPFS 3 Methodology 3.1 Processing the Requests 3.2 Locating the Content Providers 3.3 Analyzing the Data 3.4 Implementation Details 4 Results 4.1 Requests 4.2 Providers 4.3 Requested Content Vs. Provided Content 5 Related Work 6 Conclusion References Community-Based Gossip Algorithm for Distributed Averaging 1 Introduction 2 Background 2.1 Distributed Averaging 2.2 Gossip Algorithms 3 Previous Work 4 Community-Based Gossip Protocol 5 Experimental Methodology 5.1 Metrics 5.2 Networks 5.3 Simulator 6 Results and Discussion 6.1 Effect of Modularity on Distributed Averaging Performance 6.2 Predictive Value of Modularity Metrics 6.3 Structural and Functional Properties of Boundary Nodes 6.4 Performance of Community-Based Gossip 7 Conclusions References Data Management Transactional Causal Consistent Microservices Simulator 1 Introduction 2 Related Work 3 Running Example 4 Simulator 4.1 Achieving Transactional Causal Consistency 4.2 Functionality Execution 4.3 Event Handling 5 Architecture 6 Evaluation 6.1 Threats to Validity 7 Conclusion References The Impact of Importance-Aware Dataset Partitioning on Data-Parallel Training of Deep Neural Networks 1 Introduction 2 Background and Related Work 2.1 DNN Data-Parallel Training (DPT) 2.2 Prior Work on Example Importance 3 Importance-Aware DPT 3.1 Warmup Training 3.2 Importance Calculation 3.3 Dataset Partitioning Heuristics 3.4 Intervals of Model Training 4 Implementation in PyTorch 4.1 Importance Calculation 4.2 Dataset Partitioning Heuristics 4.3 Modified Training Loop for Importance-Aware Training 5 Evaluation 5.1 Experimental Setup 5.2 Different Dataset Complexities 5.3 Different Models 5.4 Different Partitioning Heuristics 5.5 Different Importance Metrics 5.6 Added Overheads 6 Conclusion References Distributed Architectures Runtime Load-Shifting of Distributed Controllers Across Networked Devices 1 Introduction 2 Problem Statement 2.1 Analogies with Systems in the Literature and in the Industry 3 Proposed Architecture 3.1 Load-Shifting Spectrum: An Example 3.2 Limitations and Technological Constraints 4 Proof of Concept 4.1 Technology Selection 4.2 System to be Controlled: F 4.3 Common Data Model: M 4.4 Monitor/Controller: N 4.5 Renderer: H 4.6 Final Design 5 Evaluation 5.1 Test Environment and Qualitative Assessment 5.2 Performance Evaluation 6 Conclusion and Future Work References EdgeEmu - Emulator for Android Edge Devices 1 Introduction 2 Related Work 2.1 Network Simulators/Emulators 2.2 Fog, Edge and Cloud Simulators 2.3 Test Frameworks 3 Android Virtual Devices and Networking 4 EdgeEmu Emulation 5 Implementation 6 Evaluation 6.1 Testing Environment 6.2 Ping Test 6.3 File Sharing Test 6.4 Number of Emulators 7 Conclusion References Author Index
