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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
SHADOW: Seamless Handoff And Zero-Downtime Orchestrated W...
Hai Dinh-Tuan · 2026-03-26 · via cs.DC updates on arXiv.org

Migrating stateful microservices in Kubernetes requires careful state management because in-memory state is lost when a container restarts. For StatefulSet-managed workloads, the problem is amplified by identity constraints that prohibit two pods with the same ordinal from running simultaneously, forcing a sequential stop-restore cycle with unavoidable downtime. This paper presents SHADOW (Seamless Handoff And Zero-Downtime Orchestrated Workload Migration), a Kubernetes-native framework that implements the Message-based Stateful Microservice Migration (MS2M) approach as a Kubernetes Operator. SHADOW introduces the ShadowPod strategy, where a shadow pod is created from a CRIU checkpoint image on the target node while the source pod continues serving traffic, allowing concurrent operation during message replay. For StatefulSet workloads, an identity swap procedure with the ExchangeFence mechanism re-checkpoints the shadow pod, creates a StatefulSet-owned replacement, and drains both message queues to guarantee zero message loss during the handoff. An evaluation on a bare-metal Kubernetes cluster with 280 migration runs across four configurations and seven message rates shows that, compared to the sequential baseline on the same StatefulSet workload, the ShadowPod strategy reduces the restore phase by up to 92%, eliminates service downtime, and reduces total migration time by up to 77%, with zero message loss across all 280 runs.