Qize Guo

Qize Guo, Bjoern Riemer, Tarik Taleb et al.

Beyond-5G (B5G) and 6G networks are expected to enable more complex industrial services, which often operate according to multi-phase workflows with phase-specific communication requirements. However, current interaction between applications and networks remains predominantly request-driven: Quality of Service (QoS) is requested at each workflow phase transition and evaluated independently, without explicit consideration of upcoming demand or network's near-term capability. This mismatch limits the ability of both sides to plan ahead, often resulting in foreseeable incompatibilities, even service disruptions. This article presents a capability-aware coordination framework for workflow-based industrial services. Within a bounded planning window, the network exposes the QoS profiles it can sustainably support, while the industrial side maps upcoming workflow phases to these disclosed capabilities and submits the resulting demand trajectory for joint assessment. The framework also supports coordinated updates when network conditions change during execution. An industrial video inspection case study on a real B5G system, complemented by large-scale simulation, illustrates that such coordination can improve service continuity, reduce disruptive rejections, and increase workflow completion under heavy load. The results suggest that future industrial networking should move beyond reactive per-request QoS handling toward forward-looking, capability-aware, workflow-level coordination.

Masoud Shokrnezhad, Tarik Taleb, Yan Chen et al.

Key Value Indicators (KVIs) provide a decision oriented view of a service by summarizing how operational performance translates into stakeholder value, risk, and outcomes. However, in many domains KVIs are difficult to compute in practice because they require selecting relevant KVI categories, defining measurable Key Performance Indicators (KPIs), collecting KPI values, and applying consistent calculation logic, all of which is typically performed manually and inconsistently from unstructured service documentation. This paper presents KPI2KVI, a tool that transforms a natural language service description into computed KVI estimates by orchestrating a deterministic multi agent workflow powered by Large Language Models (LLMs) that (i) elicits missing service context, (ii) extracts and finalizes relevant KVI categories from a taxonomy, (iii) generates service specific KPIs with units and descriptions, (iv) collects KPI values through an interactive dialogue and also supports intelligent estimation for KPI values that are unavailable, and (v) computes interval valued KVI outputs (minimum, exact, maximum) with traceable explanations for each KVI code. Simulations with representative service descriptions demonstrate that KPI2KVI consistently produces a complete end to end mapping from description to KVI intervals and provides transparent calculation narratives that support post hoc auditing and interactive advisory queries.