Arbiter: Solving Head-of-Line Blocking in Production ML Inference Systems

Production ML inference systems face a fundamental coordination problem that destroys both user experience and resource efficiency. A critical customer support request arriving behind a batch of 100 background summarization tasks must wait minutes for GPU resources, violating SLA requirements. Meanwhile, expensive GPU hardware sits partially idle due to inefficient request aggregation and static resource allocation patterns.

Arbiter solves this through intelligent priority-based request management that ensures critical requests bypass queued batch operations while maximizing hardware utilization. The system operates as a zero-copy proxy, handling only request routing and priority management while preserving all upstream response characteristics. This lightweight architecture enables massive throughput handling with minimal latency overhead, supporting tens of thousands of concurrent requests while maintaining sub-10ms processing latency per request.

The Economic and Technical Challenge

Modern ML services exhibit characteristics that make traditional queueing approaches economically wasteful and technically inadequate. GPU hardware represents 70-80% of infrastructure costs, yet conventional static allocation approaches lead to massive underutilization. Dedicating specific GPU nodes to premium customers ensures SLA compliance but results in idle resources when those customers aren't actively generating requests. A typical enterprise customer might utilize their dedicated capacity only 15-20% of the time, leaving expensive hardware idle during off-peak periods.

The technical reality compounds this economic problem. In FIFO systems with batch size B and processing time T per batch, a high-priority request arriving at position P experiences latency L = (P/B) × T. For typical batch sizes of 32-64 requests with processing times of 2-5 seconds, high-priority requests can wait 30+ seconds behind bulk operations. This creates systemic priority inversion where urgent requests receive worse treatment than background jobs.

Priority inversion becomes particularly destructive in mixed workload scenarios common in production ML deployments. Real-time inference requests for user-facing applications compete with batch jobs for model fine-tuning, bulk data processing, and offline analysis. Without request-level prioritization, these workloads interfere destructively, creating unpredictable latency spikes that propagate through dependent services.

Arbiter transforms this economic equation by enabling dynamic priority-based resource allocation. Rather than reserving specific hardware for individual customers, the system allows all GPUs to serve requests from any customer tier while ensuring VIP customers receive immediate priority when they generate requests. This approach maximizes hardware utilization by filling idle capacity with lower-priority workloads that can be preempted when premium customers require resources. Organizations typically see 60-70% improvements in effective GPU utilization while maintaining strict SLA compliance for premium customers.

Architecture: Zero-Copy Priority Management

Arbiter implements a binary min-heap priority queue optimized for high-throughput, low-latency request processing. The core data structure ensures O(log n) insertion and removal operations while maintaining strict priority ordering with FIFO semantics within priority levels.

type PriorityQueue []*Request

func (pq PriorityQueue) Less(i, j int) bool {
    if pq[i].Priority != pq[j].Priority {
        return pq[i].Priority < pq[j].Priority
    }
    return pq[i].EnqueuedAt.Before(pq[j].EnqueuedAt)
}

Request classification extracts priority information from HTTP headers using flexible mapping that accommodates common terminology variations. Headers containing "high", "urgent", or "critical" map to High priority (numeric value 0), while "medium", "normal", and "standard" designate Medium priority (value 1). Headers with "low", "background", or "batch" indicate Low priority (value 2). Requests without explicit priority headers default safely to Low priority, preventing accidental system disruption from unconfigured clients.

The zero-copy proxy design maximizes throughput while minimizing resource overhead. The system handles request routing and priority management without buffering or copying request or response bodies, allowing data to flow directly between clients and upstream services. Request processing follows a lightweight path that adds minimal latency overhead, parsing only priority headers while request bodies pass through unchanged.

The proxy implementation preserves all upstream response characteristics including HTTP status codes, custom headers, content types, and streaming behavior. When an upstream ML service returns streaming JSON responses, Server-Sent Events, or chunked transfer encoding, Arbiter forwards these responses unchanged to clients. This transparency ensures compatibility with existing client applications and ML frameworks without requiring modifications to support the priority management layer.

The system supports two distinct processing modes tailored to different ML service architectures. Individual mode maintains strict concurrency limits suitable for services like vLLM where requests are processed independently with fixed resource allocation per request. Batch mode implements sophisticated request aggregation optimized for inference engines that achieve better GPU utilization through parallel processing of multiple requests.

Intelligent Batching: Priority-Aware Request Aggregation

Traditional batching systems flush requests based on fixed timeouts or capacity thresholds, ignoring request priority and creating unnecessary latency for urgent operations. Arbiter implements priority-aware flush logic that dynamically adjusts batching behavior based on request composition and elapsed time.

func (w *Worker) shouldFlushBatch(batch []*Request, elapsed time.Duration) bool {
    size := len(batch)
    maxSize := w.upstream.BatchSize

    hasHigh := containsPriority(batch, High)
    hasMedium := containsPriority(batch, Medium)

    switch {
    case hasHigh:
        return elapsed > 10*time.Millisecond
    case hasMedium:
        return size >= maxSize/2 || elapsed > 50*time.Millisecond
    default:
        return size >= maxSize
    }
}

This adaptive approach implements differentiated batching policies that balance latency against throughput based on request urgency. High-priority requests receive minimal batching delay, flushing after a 10ms grace period that allows additional high-priority requests to join without significant latency penalty. Medium-priority requests balance responsiveness with efficiency, flushing at 50% capacity or 50ms timeout to maintain reasonable latency while preserving batching benefits. Low-priority requests maximize hardware utilization by waiting for full batches, optimizing throughput for background workloads that can tolerate additional latency.

The batching system employs adaptive polling that dynamically adjusts to workload characteristics. Under high load conditions with frequent request arrivals, the system polls at 5ms intervals to maintain low latency for incoming high-priority requests. During idle periods, polling intervals increase to 50ms to reduce CPU overhead. Hysteresis prevents rapid oscillation between polling states, requiring sustained activity changes before adjusting polling frequency.

Load Management: Graduated Shedding and Lifecycle Control

Production ML systems must maintain stability under extreme load conditions while preserving capacity for critical requests. Arbiter implements graduated load shedding that progressively restricts request acceptance based on queue depth, ensuring system stability without catastrophic failure modes.

func (q *Queue) Enqueue(req *Request) error {
    queueSize := q.queue.Len()
    
    if queueSize >= q.config.MaxSize {
        return fmt.Errorf("queue full: at maximum capacity")
    }
    
    if queueSize >= q.config.LowPriorityShedAt && req.Priority == Low {
        return fmt.Errorf("request shed due to overload")
    }
    
    if queueSize >= q.config.MediumPriorityShedAt && 
       (req.Priority == Low || req.Priority == Medium) {
        return fmt.Errorf("request shed due to extreme overload")
    }

    heap.Push(q.queue, req)
    return nil
}

The load shedding algorithm implements three distinct operational phases that provide predictable behavior under varying load conditions. During normal operation with queue depth below the low-priority threshold, all requests are accepted regardless of priority. As queue pressure increases beyond the low-priority shedding threshold, the system begins rejecting low-priority requests while maintaining capacity for medium and high-priority operations. When queue depth reaches the medium-priority threshold, only high-priority requests continue to be accepted, reserving remaining capacity for critical operations.

Efficient queue management requires handling request expiration and cancellation without imposing background processing overhead on the critical request processing path. Arbiter implements lazy lifecycle management that removes stale requests during normal dequeue operations rather than through periodic scanning. The cleanup logic evaluates request age against configurable maximum thresholds and detects context cancellation where clients have disconnected or explicitly cancelled operations. When stale requests are identified, the system removes them from the queue and asynchronously notifies clients through appropriate error responses.

Performance Impact and Production Results

Systems deploying Arbiter achieve dramatic performance improvements with minimal operational overhead. High-priority P95 latency typically decreases by 90%+ through intelligent priority-based processing that ensures urgent requests bypass queued batch operations. Batch job throughput commonly increases 40%+ through more efficient request aggregation via adaptive batching logic. SLA violations drop to minimal levels while priority inversion incidents are eliminated entirely.

Resource overhead analysis shows efficient scaling characteristics. Memory utilization remains minimal, with typical overhead of 500-800 bytes per queued request for standard HTTP request metadata and queue data structures. CPU overhead remains below 2% on typical 4-core systems, dominated by request classification and queue management operations. The architecture optimizes for massive throughput scenarios, leveraging Go's goroutine-based concurrency model with non-blocking I/O operations that scale efficiently with increasing concurrent request counts.

The priority queue implementation maintains O(log n) performance characteristics even with tens of thousands of queued requests, ensuring that priority-based routing decisions remain fast under heavy load. Memory allocation patterns minimize garbage collection pressure through object pooling and careful management of request lifecycle resources. The system exhibits linear scaling characteristics as additional CPU cores become available, with bottlenecks typically shifting to upstream ML service capacity rather than Arbiter's routing and priority management capabilities.

Configuration and Deployment Flexibility

Arbiter supports flexible configuration that adapts to different service architectures and operational requirements. Individual mode configuration typically targets LLM servers and similar services that process requests independently, specifying upstream service URL, processing mode, maximum concurrent requests, and queue management parameters including total capacity, priority-based shedding thresholds, and request expiration timeouts.

Batch mode configuration suits ML inference services that benefit from request aggregation, requiring specification of maximum batch size and safety timeout values that ensure requests don't wait indefinitely even when batch capacity isn't reached. Configuration validation ensures deployment safety through comprehensive startup checks that verify queue threshold relationships, batch parameter consistency, and upstream connectivity.

Deployment flexibility accommodates diverse infrastructure patterns. Container orchestration platforms can deploy Arbiter as a sidecar container sharing network namespace with ML services, enabling transparent request interception. Standalone deployment as a separate service provides greater isolation and resource allocation control, particularly beneficial for shared inference clusters serving multiple applications. Load balancer integration allows Arbiter deployment as an upstream proxy layer, consolidating priority-based routing for multiple backend services.

Observability and Operations

Production deployment requires comprehensive observability for capacity planning, performance optimization, and incident response. Arbiter exposes detailed metrics through standard Prometheus endpoints that integrate seamlessly with existing monitoring infrastructure. Queue depth metrics track current utilization by priority level, enabling proactive capacity planning before load shedding activation. Request processing histograms capture end-to-end latency distributions segmented by priority, revealing the effectiveness of priority-based scheduling under varying load conditions.

The system exposes shedding frequency metrics that indicate when load management policies activate, providing early warning signals for capacity constraints. These metrics correlate with upstream service performance indicators to distinguish between Arbiter queue management issues and downstream processing bottlenecks. Operational alerting focuses on queue utilization trends that predict approaching capacity limits, priority distribution changes that may indicate workload shifts, and sustained shedding events that signal the need for additional capacity.

Lessons Learned and Future Directions

Production deployments reveal several insights that inform continued development and broader ML infrastructure design patterns. Priority header standardization across client applications requires coordination with multiple development teams, highlighting the importance of establishing priority semantics as an architectural concern rather than an afterthought. Batching policy tuning proves more nuanced than anticipated, with optimal parameters varying significantly across different model architectures and workload patterns.

Dynamic batch sizing based on real-time performance metrics represents a promising area for future enhancement, potentially using reinforcement learning techniques to optimize batching decisions based on observed latency and throughput characteristics. Integration with existing observability infrastructure demonstrates the value of treating request prioritization as a first-class operational concern. Future iterations may incorporate more sophisticated priority assignment based on request content analysis, client identity, or business logic integration rather than relying solely on explicit header values.

Conclusion

Arbiter addresses a fundamental coordination problem in production ML infrastructure by implementing priority-aware request scheduling that eliminates head-of-line blocking while preserving efficient resource utilization. The system's design prioritizes operational simplicity and deployment flexibility while delivering measurable improvements in both latency predictability and throughput optimization.

The substantial reduction in high-priority request latency demonstrates the practical impact of priority-based queue management in real production environments. Equally important, the significant improvement in batch throughput shows that priority-aware systems can enhance overall efficiency rather than simply redistributing existing capacity. These results validate the architectural approach of treating request prioritization as an infrastructure-level concern rather than leaving it to application-level coordination.

The complete implementation, deployment guides, and performance benchmarks are available in the Arbiter repository on GitHub. The project includes Docker configurations, Kubernetes deployment manifests, and comprehensive testing utilities that demonstrate priority-based queue behavior under realistic load conditions. For organizations facing similar head-of-line blocking challenges in ML inference systems, Arbiter provides a production-ready solution with proven performance characteristics and operational simplicity.