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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
An Algorithm for Tolerating Crash Failures in Distributed...
Vincenzo De Florio, Geert Deconinck, Rudy Lauwereins · 2016-01-17 · via cs.DC updates on arXiv.org

In the framework of the ESPRIT project 28620 "TIRAN" (tailorable fault tolerance frameworks for embedded applications), a toolset of error detection, isolation, and recovery components is being designed to serve as a basic means for orchestrating application-level fault tolerance. These tools will be used either as stand-alone components or as the peripheral components of a distributed application, that we call 'the backbone". The backbone is to run in the background of the user application. Its objectives include (1) gathering and maintaining error detection information produced by TIRAN components like watchdog timers, trap handlers, or by external detection services working at kernel or driver level, and (2) using this information at error recovery time. In particular, those TIRAN tools related to error detection and fault masking will forward their deductions to the backbone that, in turn, will make use of this information to orchestrate error recovery, requesting recovery and reconfiguration actions to those tools related to error isolation and recovery. Clearly a key point in this approach is guaranteeing that the backbone itself tolerates internal and external faults. In this article we describe one of the means that are used within the TIRAN backbone to fulfill this goal: a distributed algorithm for tolerating crash failures triggered by faults affecting at most all but one of the components of the backbone or at most all but one of the nodes of the system. We call this the algorithm of mutual suspicion.