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cs.DC updates on arXiv.org

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
From Conventional Multi-Vendor Failover to Adaptive API R...
[Submitted on 26 May 2026 (v1), last revised 30 Aug 2026 (this v · 2026-05-26 · via cs.DC updates on arXiv.org

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Abstract:High-scale online services often rely on third-party APIs in user-facing flows such as authentication, messaging, payments, fraud detection, and identity verification. Integrating alternate providers and operating conventional failover controls is a common resilience baseline, but redundancy alone does not make provider selection adaptive, explainable, or policy-aware. This paper reports an anonymized industrial experience evolving a conventional multi-vendor SMS-provider failover arrangement in a large marketplace setting into configuration-driven adaptive API routing. The report emphasizes practical motivation, industrial context, design rationale, rollout path, operational challenges, lessons learned, and transferability conditions. The approach uses operation-specific pluggable factor lists to separate routing policy from application code, combines hard eligibility gates with weighted provider scoring, and closes the loop with business-outcome telemetry, decision logs, traffic-shift controls, and recovery safeguards. We explain why conventional mechanisms such as timeouts, retries, circuit breakers, static priority lists, dashboards, alerts, and incident runbooks remain necessary but insufficient for partial, regional, quota-related, or business-outcome degradation. A supporting synthetic replay evaluation examines complete outage, latency spike, regional failure, quota exhaustion, partial degradation, and stale telemetry scenarios without disclosing production data. The experience suggests that adaptive provider routing can reduce dependence on incident-time interpretation when the operation is critical, telemetry volume is sufficient, and organizational controls exist for policy ownership, explainability, and safe traffic movement. The paper concludes with practitioner guidance and cautions for teams considering similar architectures.

Submission history

From: Nataraj Agaram Sundar [view email]
[v1] Tue, 26 May 2026 00:26:49 UTC (21 KB)
[v2] Sun, 30 Aug 2026 01:22:32 UTC (82 KB)