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Agentic Performance at the Edge: Insights from Benchmarking Autonomous FAIR Digital Objects: From Passive Assertions to Active Knowledge DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models Metal-Sci: A Scientific Compute Benchmark for Evolutionary LLM Kernel Search on Apple Silicon From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs DisagMoE: Computation-Communication overlapped MoE Training via Disaggregated AF-Pipe Parallelism FedGMI: Generative Model-Driven Federated Learning for Probabilistic Mixture Inference PAAC: Privacy-Aware Agentic Device-Cloud Collaboration Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service FlashEvolve: Accelerating Agent Self-Evolution with Asynchronous Stage Orchestration Private Vertical Federated Inference for Time-Series Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation FLAM: Evaluating Model Performance with Aggregatable Measures in Federated Learning \mathsf{VISTA}: Decentralized Machine Learning in Adversary Dominated Environments UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment SparseRL-Sync: Lossless Weight Synchronization with ~100x Less Communication Resource-Element Energy Difference for Noncoherent Over-the-Air Federated Learning Execution Envelopes: A Shared Admission Contract for Backend AI Execution Requests Regulating Branch Parallelism in LLM Serving CLAD: A Clustered Label-Agnostic Federated Learning Framework for Joint Anomaly Detection and Attack Classification CCL-Bench 1.0: A Trace-Based Benchmark for LLM Infrastructure Safactory: A Scalable Agentic Infrastructure for Training Trustworthy Autonomous Intelligence VibeServe: Can AI Agents Build Bespoke LLM Serving Systems? 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Reactive Composition of UAV Delivery Services in Urban Environments
Woojin Lee, Babar Shahzaad, Balsam Alkouz, Athman Bouguettaya · 2024-04-29 · via cs.DC updates on arXiv.org

We propose a novel failure-aware reactive UAV delivery service composition framework. A skyway network infrastructure is presented for the effective provisioning of services in urban areas. We present a formal drone delivery service model and a system architecture for reactive drone delivery services. We develop radius-based, cell density-based, and two-phased algorithms to reduce the search space and perform reactive service compositions when a service failure occurs. We conduct a set of experiments with a real drone dataset to demonstrate the effectiveness of our proposed approach.

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