Andrii Bidochko

• Updated: August 21, 2026
• 2 min read

User‑Assisted Collaborative Distributed Inference for Efficient QoS‑Aware Autoscaling

User‑Assisted Collaborative Distributed Inference for Efficient QoS‑Aware Autoscaling

Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. This article presents a collaborative distributed inference system that combines dedicated infrastructure with resources contributed by service users. Dedicated resources guarantee baseline quality‑of‑service (QoS), while volunteered resources absorb peak demand, enabling cost‑effective autoscaling.

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Key Contributions

• High‑dimensional generative Markov model for stochastic user‑resource interactions.
• Simulation framework that supports task scheduling and QoS‑aware resource allocation.
• Empirical evaluation showing reduced dedicated resource consumption and improved P99 latency.

Why Collaborative Distributed Inference?

Traditional centralized inference incurs linear cost growth as request volume increases. By leveraging user‑contributed compute, the system scales elastically without proportional investment in dedicated hardware. This approach is especially valuable for edge‑centric AI workloads where latency and bandwidth constraints are critical.

Implementation Highlights

The system is built on a modular micro‑service architecture. Core components include:

1. Resource Manager: Dynamically discovers and registers volunteered resources.
2. Scheduler: Balances load between dedicated and volunteered nodes using the Markov‑based policy.
3. QoS Monitor: Continuously measures latency and throughput to enforce service‑level agreements.

For a deeper technical dive, visit our documentation page: ubos.tech/docs/distributed-inference.

Results

Simulation across varying user populations demonstrates that as the number of participants grows, the distributed scheduling policy consistently outperforms pure centralized scheduling. Key metrics include:

• Up to 45% reduction in dedicated resource usage.
• Improved request‑completion rates by 30%.
• P99 latency improvements of 25% under peak load.

Conclusion

The collaborative distributed inference framework offers a scalable, cost‑effective solution for AI‑driven services. By integrating user‑assisted resources, organizations can achieve higher QoS while minimizing infrastructure spend.

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Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.