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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
On Byzantine Fault Tolerance in Multi-Master Kubernertes ...
Gor Mack Diouf, Halima Elbiaze, Wael Jaafar · 2019-04-11 · via cs.DC updates on arXiv.org

Docker container virtualization technology is being widely adopted in cloud computing environments because of its lightweight and effiency. However, it requires adequate control and management via an orchestrator. As a result, cloud providers are adopting the open-access Kubernetes platform as the standard orchestrator of containerized applications. To ensure applications' availability in Kubernetes, the latter uses Raft protocol's replication mechanism. Despite its simplicity, Raft assumes that machines fail only when shutdown. This failure event is rarely the only reason for a machine's malfunction. Indeed, software errors or malicious attacks can cause machines to exhibit Byzantine (i.e. random) behavior and thereby corrupt the accuracy and availability of the replication protocol. In this paper, we propose a Kubernetes multi-Master Robust (KmMR) platform to overcome this limitation. KmMR is based on the adaptation and integration of the BFT-SMaRt fault-tolerant replication protocol into Kubernetes environment. Unlike Raft protocol, BFT-SMaRt is resistant to both Byzantine and non-Byzantine faults. Experimental results show that KmMR is able to guarantee the continuity of services, even when the total number of tolerated faults is exceeded. In addition, KmMR provides on average a consensus time 1000 times shorter than that achieved by the conventional platform (with Raft), in such condition. Finally, we show that KmMR generates a small additional cost in terms of resource consumption compared to the conventional platform.