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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
Implementation Technologies of an Advanced Cloud-based Sy...
Sotiris P. Gayialis, Evripidis P. Kechagias, Angeliki Deligianni · 2022-02-17 · via cs.DC updates on arXiv.org

Today's era is characterized as the "digital transformation era". Digital processes and information systems are used in every aspect of social and business activity. The use of information technology over the internet is so extensive that we interact with it daily without even recognizing it. The technological advances can offer a plethora of improvements for the supply chain processes, especially in the field of distribution planning and execution. The scope of this paper is to present the technological content of an advanced routing and scheduling system for transportation and delivery of goods. The system focuses on the routing and scheduling problem in urban areas, as city logistics have become a complex environment for companies to deliver their goods. The presented system deals with both static and dynamic routing and scheduling problems. More specifically, the system can create initial routing plans based on orders, available vehicles, time windows, and traffic forecasting data. Afterwards, during the execution of the plans, the system can monitor the fleet, detect deviations from the original plans, and finally, perform rerouting operations when needed. After a brief presentation of the system's modules and functionality, the paper describes thoroughly the technologies used to develop the system. The technological elements of the system are integrated into a cloud environment offering a system that is easy to maintain and can effectively support logistics companies' distribution activities. The system is provided as a Software as a Service with data being maintained on a central host and processed on the cloud. Therefore, logistics companies that decide to implement it can achieve faster, more accurate and more cost-efficient distribution activities while ensuring better customer service.