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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
Enabling Microsoft OneDrive Integration with HTCondor
Derek Weitzel · 2019-07-08 · via cs.DC updates on arXiv.org

Accessing data from distributed computing is essential in many workflows, but can be complicated for users of cyberinfrastructure. They must perform multiple steps to make data available to distributed computing using unfamiliar tools. Further, most research on data distribution has focused on the efficiency of providing data to computing resources rather than considering the ease of use for distributing data. Creating an easy to use data distribution method can reduce the time researchers spend learning cyberinfrastructure and increase its usefulness. Microsoft OneDrive is a online storage solution providing both file storage and sharing. OneDrive provides many different clients to access data stored in the service. It provides many features that users of cyberinfrastructure could find useful such as automatic synchronization with desktop clients. A barrier to using services such as OneDrive is the credential management necessary to access the service. Recent innovations in HTCondor have allowed the management of OAuth credentials to be handled by the scheduler on the user's behalf. The user no longer has to copy credentials along with the job, HTCondor will handle the acquisition, renewal, and secure transfer of credentials on the user's behalf. In this paper, I will focus on providing an easy to use data distribution method utilizing Microsoft OneDrive. Measuring ease of use is difficult, therefore I will will describe the features and advantages of using OneDrive. Additionally, I will compare it to measurements of data distribution methods currently used on a national cyberinfastructure, the Open Science Grid.