The International Workshop on Analytics for Service and Application Management (AnServApp) provides a forum for researchers and practitioners working on the use of data analytics, machine learning, artificial intelligence, and cognitive techniques for service and application management. The workshop welcomes contributions that address how operational data can be transformed into actionable insights, automated decisions, and trustworthy management processes.
Relevant approaches include predictive analytics, data mining, machine learning, deep learning, graph-based analytics, causal inference, anomaly detection, log and trace analysis, foundation models, large language models, and AI-assisted operations. The workshop is particularly interested in methods that support observability, performance management, fault detection and diagnosis, root-cause analysis, service assurance, intent-based management, resource optimization, security analytics, and closed-loop automation. AnServApp also encourages work that examines the reliability, explainability, robustness, and operational trustworthiness of analytics-driven management solutions.
The main goal of AnServApp is to present mature research results, emerging ideas, and work-in-progress contributions in the area of analytics and AI for service and application management. In addition to regular papers, short papers describing late-breaking advances, practical experiences, position papers, and reports from ongoing research are also welcome.
Topics of interest include but are not limited to:
Analytics, machine learning, and AI for service and application management
MLOps, LLMOps, observability, and automated operations
Anomaly detection, incident prediction, and root-cause analysis
Log, metric, event, and trace analytics
Resource management & orchestration
Multi-Agent Reinforcement Learning (RL)
Distributed RL over communication networks
Performance, reliability, and service assurance
Resource management and optimization in cloud, edge, and hybrid environments
Analytics for microservices, distributed applications, and cloud-native systems
Intent-based, policy-based, and closed-loop service management
Explainable, trustworthy, and robust AI for operational decision support
Security analytics for service and application infrastructures
Privacy-aware and federated analytics for operational data
Foundation models and large language models for service management tasks
Human-in-the-loop analytics and cognitive approaches to operations
Practical experiences, datasets, benchmarks, and reproducibility studies in service and application analytics
Verticals:
- Lower carbon foot print applications / services / systems
- Industry5.0 applications / services / systems
- Smart cities and smart transportation services / systems
- Social media apps / services / systems
- Smart education services / systems
- Edge, fog, cloud services / systems
- Sustainability and resilience of applications / services / systems
- Digital Twins for networks and services
Authors are invited to submit original unpublished papers not under review elsewhere. Papers should be submitted in IEEE 2-column format. Maximum paper length, including title, abstract, all figures, tables, and references, is 6 pages.
Papers have to be submitted electronically in PDF format through the EDAS conference management system, accessible via EDAS: https://anservapp2026.edas.info
For accepted papers, at least one author is expected to register and present the paper in person at the workshop. Accepted papers will be published in the conference proceedings and submitted to IEEE Xplore.
Paper Submission Deadline: August 14, 2026
Notification of Acceptance: September 8, 2026
Submission of Camera-ready Version: September 14, 2026
Conference Date: 30 October 2026
· Pal Varga <pvarga@tmit,bme.hu>, Budapest University of Technology and Economics, Hungary
· Tokunbo Makanju <makanju@cs.dal.ca>, Dalhousie University, Canada