A REVIEW OF CLOUD-BASED AUDIO PROCESSING USING DIRECTED ACYCLIC SIGNAL-PROCESSING GRAPHS IN MICROSERVICE ARCHITECTURES

Oleh Chaplia, Halyna Klym

Abstract


Background. Audio processing chains in digital audio workstations (DAWs) are directed acyclic graphs (DAGs) of digital signal processing (DSP) nodes. Executing such graphs in the cloud promises elastic compute, collaboration, and access from thin clients. Microservices dominate cloud systems, but their principles were shaped by request-and-response workloads, not by block-synchronous audio. This paper reviews approaches that process and render audio in the cloud as DSP DAGs and evaluates them against microservice principles.

Materials and Methods. A systematic protocol was applied to two complete proceedings volumes of the IEEE International Symposium on the Internet of Sounds, using database searches and backward snowballing. From 97 proceedings papers and 49 full texts from other sources, 43 peer-reviewed studies published from 2022 to 2026 were included and grouped into eight method families, and each method was assessed on execution location, service granularity, real-time mode, reported latency and its measurement point, scaling, fault tolerance, and support for DAW-originated graphs.

Results and Discussion. Browser DAWs keep the DAG on the client and use the cloud for storage and collaboration. Networked music performance systems relay streams within strict latency budgets but do not render offline graphs remotely. Edge rendering services move a single stage to the network edge. Audio microservices expose offline tasks through REST APIs. Cloud-native media pipelines run containerized graphs with self-healing, and generic DAG engines provide scheduling and scaling but ignore sample-accurate timing. None of the reviewed systems execute a DAW-originated DSP DAG as microservices with block-level deadlines. Reported latencies are measured at different points and are not comparable. Cloud and container systems report hundreds of milliseconds to seconds.

Conclusion. Existing systems lack a portable representation of DAW graphs, a granularity model for stateful DSP nodes, deadline-aware scheduling in container orchestrators, low-latency block transport between services, and benchmarks for cloud audio rendering. These gaps define the research agenda for microservice-based cloud audio processing.

Keywords: cloud computing, microservices, digital signal processing, directed acyclic graph, digital audio workstation, reliability


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References


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DOI: http://dx.doi.org/10.30970/eli.35.6

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