Executing service workflows with directed acyclic graph (DAG) dependencies over wireless distributed computing networks requires joint control of function placement, routing, and scheduling, together with synchronization of inputs from the same request at merge points. We study throughput-optimal control of these networks under stochastic request arrivals, time-varying wireless links, and interference constraints. We use a valid mapping abstraction that assigns each service function to a computation node and each DAG dependency to a routing path. Using this abstraction, we characterize the network capacity region to support service DAGs, and prove that synchronizing the packets of a request at merge points does not shrink the region. We develop Dynamic Computing Network Control for service DAGs (DCNC-DAG), a dynamic control policy that selects queue-aware valid mappings and schedules service DAG execution by nearest-to-origin (NTO) while enforcing input synchronization at merge points. With exact minimum-cost valid mappings, DCNC-DAG is throughput-optimal and ensures rate stability of the physical queues under bounded arrivals. These guarantees hold for any fixed finite interval between mapping updates, even when mapping decisions rely on boundedly stale queue length information. For larger service DAGs, we develop DCNC-DAG-H, which replaces exact mapping optimization with a queue-aware heuristic. Simulations show that DCNC-DAG supports $2.7\times$-$5.5\times$ the throughput of queue-blind baselines, while DCNC-DAG-H achieves up to $2.68\times$ those of the corresponding baselines on larger workflow and AI service DAGs.
The Musical Metaverse relies on distributed audio, visual, inference, and delivery functions deployed across heterogeneous edge-cloud resources. Queue-aware orchestration frameworks such as SPARQ jointly optimize service placement, routing, and resource allocation, but fix the execution regime of each resource in advance: it is either shared, favoring statistical multiplexing, or guaranteed, favoring performance isolation. This paper introduces MusiSPARQ, a subscription-aware control framework that promotes resource isolation from a fixed infrastructure attribute to a runtime decision. Each switchable resource is represented through coupled shared- and guaranteed-resource pools drawing from the same physical-capacity budget, and at each orchestration epoch the controller selects which class-specific service flows remain shared and which receive isolated capacity. The per-epoch decision jointly accounts for class-specific latency objectives, per-class maximum-latency limits, queueing stability, isolation cost, and switching overhead. In a dynamic virtual-concert scenario, MusiSPARQ reduces the Premium latency-target violation rate from 16.19% to 0.29% relative to fully shared execution, while reserving on average only 0.71% of capacity and, unlike static isolation, keeping Standard and Basic traffic within their latency targets.
Ali Al Housseini, Jaime Llorca, Cristina Rottondi, Luca Turchet, Tiziano Leidi, Omran Ayoub
The Musical Metaverse (MM) enables geographically distributed musicians, audiences, sound engineers, and AI agents to interact through synchronized audio, visual, kinematic, and haptic streams, imposing latency, synchronization, bandwidth, and scalability requirements that are difficult to satisfy with conventional bit-level communication. This paper argues that semantic communication, understood here as the transmission of musically meaningful information rather than raw audio-visual-haptic samples, provides a principled communication paradigm for the MM. We define the MM from a communication perspective and derive modality-specific require ments; propose a three-pillar framework for musical semantic communication comprising semantics extraction, semantic-aware orchestration, and cross-modal semantic synchronization; survey representative work across semantic communication, music AI and edge orchestration, identifying the gaps that prevent their integration; and propose a semantic-first reference architecture based on device-side semantic encoders, edge semantic routers, and cross-modal semantic synchronization. The resulting framework positions the MM as a concrete and demanding use case for 6G semantic communication.