A Reactive Serverless Fabric for Autonomous Workflow Tuning
DOI:
https://doi.org/10.5281/00cxmh91Keywords:
Event-Driven Computing,Serverless Orchestration,Cloud Workflow Automation,Autonomous Cloud Operations,Function-as-a-Service (FaaS),Workflow Optimization,Cloud-Native Architecture,Event Streaming and Messaging,Intelligent Resource Scheduling,Distributed Serverless Systems.Abstract
Workflow-oriented applications in multi-cloud environments increasingly face variable demand, elastic scaling needs, and structural diversity, making serverless models attractive for their low runtime costs and ease of use. Autonomous workflow execution, however, remains a persistent challenge: prior survey work shows that managing complex, heterogeneous workflows across clouds is daunting, and the steep learning curve often discourages adoption. Event-driven approaches address this by letting users focus on descriptive specifications rather than procedural implementation details. Yet despite the growing popularity of event-driven cloud offerings, recent survey findings point to a lack of system support for complex workflows that inherently depend on multiple triggers. The high execution frequency typical of serverless functions further intensifies the need for robust event-driven support. Recognizing both the scale and distributed nature of these functions, we design a serverless architecture capable of managing complex agent-based workflows economically and automatically, in which every action module is realized as a self-contained, event-triggered function operating in a serverless manner.
We propose the Event-Driven Serverless Orchestration (EDSO) framework, which autonomously manages a complex distributed agent application structured as a closed-loop, event-driven control system. Autonomy is achieved through self-adaptation mechanisms that combine real-time reactive scheduling with production-distribution optimization. EDSO functions primarily as a central orchestrator coordinating other functions responsible for executing major agents. The orchestrator itself handles three core modules: an outer-agent trigger that responds to cloud environment notifications, a real-time scheduler activated upon completion of triggered agents within the outer agent’s cloud zone, and a major-agent-control optimization function that ensures resource-efficient operation of the major agents—generating execution events for the inner-agent set and managing their distribution.
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