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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
Practically-Self-Stabilizing Virtual Synchrony
Shlomi Dolev, Chryssis Georgiou, Ioannis Marcoullis, Elad Michae · 2015-02-18 · via cs.DC updates on arXiv.org

Virtual synchrony is an important abstraction that is proven to be extremely useful when implemented over asynchronous, typically large, message-passing distributed systems. Fault tolerant design is a key criterion for the success of such implementations. This is because large distributed systems can be highly available as long as they do not depend on the full operational status of every system participant. Namely, they employ redundancy in numbers to overcome non-optimal behavior of participants and to gain global robustness and high availability. Self-stabilizing systems can tolerate transient faults that drive the system to an arbitrary unpredicted configuration. Such systems automatically regain consistency from any such arbitrary configuration, and then produce the desired system behavior. Practically self-stabilizing systems ensure the desired system behavior for practically infinite number of successive steps e.g., $2^{64}$ steps. We present the first practically self-stabilizing virtual synchrony algorithm. The algorithm is a combination of several new techniques that may be of independent interest. In particular, we present a new counter algorithm that establishes an efficient practically unbounded counter, that in turn can be directly used to implement a self-stabilizing Multiple-Writer Multiple-Reader (MWMR) register emulation. Other components include self-stabilizing group membership, self-stabilizing multicast, and self-stabilizing emulation of replicated state machine. As we base the replicated state machine implementation on virtual synchrony, rather than consensus, the system progresses in more extreme asynchronous executions in relation to consensus-based replicated state machine.