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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
Auditing Lustre file system
Sayed Erfan Arefin · 2023-01-21 · via cs.DC updates on arXiv.org

With the increasing demand for data storage and the exponential growth of data, traditional single-server architectures are no longer sufficient to handle the massive amounts of data storage, transfer, and various file system events. As a result, distributed file systems have become a necessity to address the scalability challenges of file systems. One such popular distributed file system is Lustre, which is extensively used in high-performance computing environments. Lustre offers parallel file access, allowing multiple clients to access and store data simultaneously. However, in order to ensure the security and integrity of data, auditing plays a crucial role. Lustre auditing serves as a proof of security and enables the implementation of robust security features such as authentication with Kerberos, mandatory access control with SELinux, isolation, and more. Auditing helps track and monitor file system activities, providing valuable insights into user actions, system events, and potential security breaches. The objective of this project is to explore Lustre auditing using CentOS, a popular Linux distribution, within a Lustre architecture. By implementing Lustre auditing, we aim to enhance the security and reliability of the file system. Additionally, we plan to develop a graphical interface that presents the auditing features in a user-friendly and visually appealing manner. This interface will provide administrators and users with a convenient way to monitor and analyze auditing logs, view access patterns, detect anomalies, and ensure compliance with security policies. By combining the power of Lustre's parallel file system architecture with comprehensive auditing capabilities and an intuitive graphical interface, we aim to provide a robust and user-friendly solution for managing and securing large-scale data storage and access.