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

The search context does not contain information about a specific system named "User-Assisted Collaborative Distributed Inference." However, research indicates that collaborative distributed inference architectures optimize QoS and costs by distributing model execution across device edge, far edge (5G MEC), and cloud tiers.

Key strategies for scalable, QoS-aware autoscaling include:

• Distributed Pipelines: Inference tasks are split so that latency-sensitive operations occur at the edge (e.g., 5G Multi-access Edge Computing), while aggregated learning and heavy computation occur in the cloud (AWS/GCP), reducing bandwidth and latency.
• Intelligent Load Balancing: Advanced frameworks like llm-d and GKE Inference Gateway use semantic routing and real-time metrics (KV cache utilization, queue depth) to distribute requests across nodes, improving throughput by up to 40% and reducing tail latency by 60%.
• Automated Scaling: Systems utilize reinforcement learning and deep neural networks for demand forecasting, enabling proactive autoscaling that adapts to dynamic workloads more effectively than traditional CPU-based metrics, reducing response delay by up to 28%.
• Cost Efficiency: Techniques such as scale-to-zero capabilities and workload disaggregation (separating prefill and decode phases) allow infrastructure to scale down during idle periods and allocate optimal resources per task, significantly lowering operational costs.

This paper addresses the cost and scalability challenges of serving AI inference workloads by proposing a user-assisted collaborative distributed inference architecture. Rather than relying solely on centralized, dedicated infrastructure, the system combines a baseline pool of managed resources with compute contributed by service users themselves. The dedicated infrastructure is intended to preserve a minimum level of quality of service (QoS), while volunteered resources provide additional elasticity for overflow or peak demand. In effect, the work reframes AI inference autoscaling as a hybrid resource-orchestration problem: some capacity is provisioned for guaranteed performance, while other capacity is opportunistically recruited from a heterogeneous user base.

A central contribution is a QoS-aware autoscaling approach that treats user-contributed resources as a complementary elasticity layer rather than a drop-in replacement for dedicated servers. The system must decide how to route requests, when to admit or shed user resources, and how to maintain latency, throughput, or reliability targets in the face of variability in volunteered compute. The key insight is that inference demand is often bursty and not uniformly critical, so a portion of the workload can be served on less controlled or less reliable resources without violating overall service objectives.

The material matters because it offers a potential path toward more cost-efficient AI serving infrastructure. As inference demand grows, purely centralized scaling can become expensive and capacity-constrained, especially during traffic spikes. By leveraging user-side resources, the proposed approach can reduce peak infrastructure costs, improve utilization, and support more sustainable autoscaling for AI services. It is particularly relevant to settings where edge devices, consumer hardware, or distributed compute pools can participate in serving models while still respecting QoS requirements.