Ulteriori informazioni
Sommario
.-Service Foundations.-From Natural Language to TOSCA: Leveraging LLMs for Automated Service Composition.-RESTful API Service Discovery via Comprehensive Feature Mining, Deep Neural Networks, and Contrastive Learning.-GCLP: Generative Contrastive Learning with Adaptive Prompt-Guided Diffusion for Temporal Reasoning over Service Knowledge Graphs.-MCPybarra: A Multi-Agent Framework for Low-Cost, High-Quality MCP Service Generation.-DHG-LLM: A Dual-Channel Heuristic Greedy Framework for End-to-End Service Solution Construction under LLM Guidance.-Automating Policy-as-code Generation Pipeline for Data Products: an OpenAPI-driven Rego generator.-Service Reccomendation and Selection.-Optimal Subset Oracle-based Web API Composition Recommendation for Mashup Creation.-TSSGCF: Textual Similarity-Supervised Graph Collaborative Filtering for Web API Recommendation.-Dynamic Reputation Measurement of Online Services for Maximizing User Group Satisfaction.-A Multi-view Heterogeneous Hypergraph Augmented Self-Gating Contrastive Fusion Framework for Service Recommendation.-ARrec: A GitHub Awesome Repository Recommendation Service based on Graph Mining.-Microservice Architectures.-GRACE: A Strategic LLM-Enhanced Graph Reinforcement Learning Framework for Adaptive Fault Recovery in Microservice Systems.-Adaptive Modality Compensation via Bi-Mamba Dual-Stream Learning for Microservice Failure Diagnosis under Incomplete Multimodal Data.-Failure Classification for Microservice Systems Based on Variational Graph Auto-Encoders.-Using Guided Community Detection to Improve Existing Microservice Designs.-A Pattern-Driven and LLM-Assisted Approach for Decomposing Monolithic ML-Based Systems into Microservices.-Service-Level Energy Modeling and Experimentation for Cloud-Native Microservices.-MOTSAD: Multi-Objective Optimization for Time Series Anomaly Detection in Microservice.-Predicting Security Weaknesses in MicroserviceArchitectures using Structural Metrics.-Serverless Computing.-Multi-Agent-Driven Dual-Layer Serverless Adaptive Ensemble Inference Method.-Quantifying Serverless Elasticity: The gumeter Benchmark Suite.-HybridFP: Divide-and-conquer Serverless Function Provision for Mitigating Cold Starts.-Services at the Edge.-Service-Oriented AI Model Compression for Computing Continuum Environments.-Optimizing Containerized Edge Service Migration through File-Level Storage Sharing.-Anticipatory Service Migration in Mobile Edge Computing via Spatio-Behavioral Prediction.-Ahead-of-Time Scheduling for Workflow Applications in Edge Computing.-OctoCross: Workload-Aware Request Offloading Scheduling in Cross-Camera Collaboration.-D3FU: Data-Free Distillation Driven Federated Unlearning for Service-Oriented Computing.-Preference-aware crowdsourcing of IoT energy services.-LEADR: A Lyapunov-based Energy-Aware Decentralized Routing Strategy for Continuous UAV Communication Services.-A Non-Intrusive Framework for Deferred Integration of Cloud Patterns in Energy-Efficient Data-Sharing Pipelines.-SCSTL: Spatial Composite Signal Temporal Logic for IoT Service Monitoring.-SDAD: A Service Deployment Method Based on Association Rule and Reinforcement Learning for Edge Computing.-EdgeInfer-TP: A Collaborative Tensor Parallelism Inference System for Heterogeneous Edge Devices.
Riassunto
The two-volume set LNCS 16320 and16321 constitutes the refereed proceedings of the 23rd International Conference on Service-Oriented Computing, ICSOC 2025, held in Shenzhen, China, during December 1–4, 2025.
The 42 full papers and 21 short papers presented in these proceedings were carefully reviewed and selected from 219 submissions. The papers are organized in the following topical sections:
Part I: Service Foundations; Service Recommendation and Selection; Microservice Architectures; Serverless Computing; and Services at the Edge.
Part II: Software as a Services and Cloud Infrastructure; Privacy, Security and Trust; Services and Business Processes; Quantum Service Computing; and Industrial Papers.
