The ACM CoNEXT 2025 Student Workshop (CoNEXT-SW'25) provides a focused venue for early-stage and ambitious research in computer networks and systems. The workshop complements the main conference by giving student authors the opportunity to present developing work in a constructive and technically rigorous setting. CoNEXT-SW'25 emphasizes clarity, reproducibility, and open discussion of methods and evaluation plans. This summary outlines the motivation, structure, and highlights of this year's workshop, which features novel contributions on digital twin synchronization, transport innovations in QUIC, hybrid load balancing, secure time synchronization, and machine learning-driven intrusion detection. Keynote: To be announced.
Artifact evaluation places a significant burden on both authors and reviewers: Authors must package bespoke experimental setups, while committees struggle to validate them. Research Infrastructures (RIs) offer tools to support reproducibility, yet interoperability remains limited due to insufficient standardization efforts. To address this, we propose new standards that unify RIs through a formal experiment execution format and topology management for networked experiments. In addition, we advance sustainability through integrated energy analysis and modeling. Together, these contributions improve reproducibility, enable cross-RI collaboration, and foster more sustainable experiments. This makes RIs more FAIR-particularly by strengthening interoperability and reusability-while aiding broader adoption across the (network) research community.
Ultra-wideband delivers superior indoor localization accuracy and is emerging as the nexus of modern industrial cyber-physical systems, catalyzing location-aware intelligence, rapid shop-floor reconfiguration, and real-time closed-loop motion control for mobile robotics. However, prevailing self-calibration approaches consistently fail when confronted with the demanding realities of industrial deployment, rendering theoretically sound methods operationally infeasible. We present an autonomous airborne self-calibration framework that seamlessly unifies network-geometry recovery and site-datum registration, realizing zero-skill staging, one-time drop-in deployment, zero-touch recalibration, and plant-scale operation.
The growth of commercial space enables applications such as realtime Earth observation and global edge computing. A natural question arises: can existing public cloud infrastructures be effectively extended to leverage LEO satellite links, providing scalable compute capabilities for satellite data processing and cross-region connectivity? Integrating public clouds with LEO networks, however, presents challenges such as dynamic satellite topologies, session continuity as visible satellites change, limited onboard resources, and underlay complexity for tenant VMs. To address this, we present IrisGW, a cloud gateway architecture enabling seamless cross-region VM communication over topology-varying satellites. Built atop mature cloud virtualization, IrisGW embeds destination locations into VXLAN tunnels for geo-based routing, allowing access satellites to forward packets efficiently. This reduces routing inflation, minimizes on-satellite state, and provides transparent access for tenant VMs. IrisGW demonstrates how public clouds and LEO networks can be coalesced to support robust, planet-scale cloud services.
The rise of programmable network devices - such as switches and SmartNICs - has enabled a new computing paradigm: In-Network Computing (INC). By performing computation directly in the data plane, INC can reduce communication overhead, accelerate distributed coordination, and improve the efficiency of system-wide operations. Distributed systems stand to benefit significantly from this paradigm shift. Many of their core functions-such as replication, consensus, scheduling, aggregation, and load balancing-are communication-heavy and latency-sensitive. In-network computing provides a compelling opportunity to rethink these mechanisms by pushing parts of their logic into the network fabric. While recent research has demonstrated the feasibility of INC for specific tasks (e.g., cache indexing, gradient aggregation, distributed counters), broader system-level integration remains challenging. Open questions span from programming abstractions and hardware constraints to performance modeling, security, and compatibility with evolving distributed runtimes. This workshop focuses on innetwork computing and AI for distributed systems (INCAS). We aim to bring together researchers and practitioners to share emerging ideas, explore new design patterns, and identify key challenges at the intersection of programmable networks and distributed system architecture.
