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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
A Framework for Consistency Models in Distributed Systems
Paulo Sérgio Almeida · 2024-11-25 · via cs.DC updates on arXiv.org

We define am axiomatic timeless framework for asynchronous distributed systems, together with well-formedness and consistency axioms, which unifies and generalizes the expressive power of current approaches. 1) It combines classic serialization per-process with a global visibility. 2) It defines a physical realizability well-formedness axiom to prevent physically impossible causality cycles, while allowing possible and useful visibility cycles, to allow synchronization-oriented abstractions. 3) Allows adding time-based constraints, from a logical or physical clock, either partially or totally ordered, in an optional and orthogonal way, while keeping models themselves timeless. 4) It simultaneously generalizes from memory to general abstractions, from sequential to concurrent specifications, either total or partial, and beyond serial executions. 5) Defines basic consistency axioms: monotonic visibility, local visibility, and closed past. These are satisfied by what we call serial consistency, but can be used as building blocks for novel consistency models with histories not explainable by any serial execution. 6) Revisits classic pipelined and causal consistency, revealing weaknesses in previous axiomatic models for PRAM and causal memory. 7) Introduces convergence and arbitration as safety properties for consistency models, departing from the use of eventual consistency, which conflates safety and liveness. 8) Formulates and proves the CLAM theorem for asynchronous distributed systems: any wait-free implementation of practically all data abstractions cannot simultaneously satisfy Closed past, Local visibility, Arbitration, and Monotonic visibility. While technically incomparable, the CLAM theorem is practically stronger than the CAP theorem, as it allows reasoning about the design space and possible tradeoffs in highly available partition tolerant systems.