Jul 2026· International Symposium on Service Oriented Software Engineering· pp. 90-97· 0 citations· 31 references
Abstract
Edge AI is evolving from isolated inference toward long-running services that coordinate model pipelines, data streams, state, and accelerators near users and physical environments. Cloud-native and edge-native platforms offer useful foundations, but their primary control objects–containers, nodes, links, and enrolled sites–generally do not expose the model, data, state, quality, and participation semantics required by these services. This paper presents eAI+, a vision for an edgeAI-native service platform built around three first-class control objects: AI service graphs, dynamic edge resource fabrics, and participant contracts. eAI+ aims to preserve service quality under latency, privacy, reliability, cost, and participation constraints through three coordinated mechanisms. Runtime would select safe execution adaptations based on current workload, environment, and contract signals. Deployment would map service-graph components and prepared fallbacks to heterogeneous resources. PolyLink is the participant-contract module for plug-and-play resource onboarding; it would register contributors, verified resource offers, capabilities, and participation terms. Once a resource is onboarded, it would become available to Deployment for placing eligible service-graph components under the registered contract, while PolyLink would maintain metering, reputation, rewards, and exit events. Migration would transfer only continuity-critical state or control when mobility, overload, policy changes, or contributor lifecycle events invalidate the current placement. This framing treats edge AI as a coordinated service-platform problem across models, data, state, resources, and contracts.
PhyAI, a Physical AI inference engine with a single runtime that keeps architecture-specific conditioning, solver, cache, and output logic in model adapters while sharing graph execution, kernels, memory management, and parallel services, is built.
Cheng-Hua Wang, Daliang Xu, Dongqi Cai et al.· 0 citations
Frontier open-weight models are increasingly available, but serving them still largely assumes datacenter infrastructure. We present FreeToken, an edge-native MoE serving system that treats a personal machine not as a small GPU, but as a unified, elastic inference platform. FreeToken co-designs the full serving stack, including model layout and loading, expert residency, CPU--GPU execution, agentic state reuse, and runtime memory management, around two realities of local AI: agent workloads continuously change their execution pattern, and edge hardware exposes heterogeneous resources whose balance differs from machine to machine. Rather than committing to a fixed offloading strategy, FreeToken continuously maps computation and model state onto the resources actually available. FreeToken supports more than 20 MoE models and real coding and tool-using agents across hardware ranging from an 8GB laptop GPU to a single workstation GPU. More importantly, it changes what these machines can practically serve, from a 35B model on a laptop to a 284B model on a gaming desktop and the 753B GLM-5.2 on a single workstation GPU. FreeToken turns open weights into deployable local software, making the machines users already own a practical platform for frontier-scale intelligence. We release the system at flashml.ai.
Shuo Yang, Xiao-yun Fan, Melissa Z. Pan et al.· 2 citations
The proposed approach separates the research-infrastructure layer, which exposes and manages distributed resources, from the application layer, where Cyber-Physical workflows are organized according to an Edge-Fog-Cloud pattern in which placement, timing, and data provenance are treated as first-class experimental concerns.
Fabio Orazio Mirto, Giuseppe Tricomi, L. D’Agati et al.· 0 citations
Esiur preserves object identity across nodes and performs incremental state synchronization without application-level coordination, enabling runtime type definition (TypeDef) discovery and efficient transmission of strongly typed and heterogeneous data without external schemas.
Ahmed Kh. Zamil, Ammar D. Jasim· IEEE Access· 0 citations
AnyLog provides a cloud-like operating model for distributed SQL, real-time automation, Edge AI, federated learning, and resilient decision-making without a single point of failure or any dependence on centralized infrastructure.
Roy Shadmon, Mark Davidson, Eric Aquaronne et al.· 0 citations
This work presents SemanticXR, the first device-cloud system for real-time, open-vocabulary semantic mapping and querying under XR power, bandwidth, and memory constraints, and key insight is to elevate semantically identifiable objects to first-class units of system design, governing how the system communicates, executes, and manages memory across the device and the server.
Rahul Singh, Devdeep Ray, Connor Smith et al.· 0 citations