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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
A Dynamic Web Service Registry Framework for Mobile Envir...
Rohit Verma, Abhishek Srivastava · 2016-09-29 · via cs.DC updates on arXiv.org

Advancements in technology have transformed mobile devices from being mere communication widgets to versatile computing devices. Proliferation of these hand held devices has made them a common means to access and process digital information. Most web based applications are today available in a form that can conveniently be accessed over mobile devices. However, webservices (applications meant for consumption by other applications rather than humans) are not as commonly provided and consumed over mobile devices. Facilitating this and in effect realizing a service-oriented system over mobile devices has the potential to further enhance the potential of mobile devices. One of the major challenges in this integration is the lack of an efficient service registry system that caters to issues associated with the dynamic and volatile mobile environments. Existing service registry technologies designed for traditional systems fall short of accommodating such issues. In this paper, we propose a novel approach to manage service registry systems provided 'solely' over mobile devices, and thus realising an SOA without the need for high-end computing systems. The approach manages a dynamic service registry system in the form of light weight and distributed registries. We assess the feasibility of our approach by engineering and deploying a working prototype of the proposed registry system over actual mobile devices. A comparative study of the proposed approach and the traditional UDDI (Universal Description, Discovery, and Integration) registry is also included. The evaluation of our framework has shown propitious results in terms of battery cost, scalability, hindrance with native applications.