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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
A Comparative Taxonomy and Survey of Public Cloud Infrast...
Dimitrios Sikeridis, Ioannis Papapanagiotou, Bhaskar Prasad Rima · 2017-10-04 · via cs.DC updates on arXiv.org

An increasing number of technology enterprises are adopting cloud-native architectures to offer their web-based products, by moving away from privately-owned data-centers and relying exclusively on cloud service providers. As a result, cloud vendors have lately increased, along with the estimated annual revenue they share. However, in the process of selecting a provider's cloud service over the competition, we observe a lack of universal common ground in terms of terminology, functionality of services and billing models. This is an important gap especially under the new reality of the industry where each cloud provider has moved towards his own service taxonomy, while the number of specialized services has grown exponentially. This work discusses cloud services offered by four dominant, in terms of their current market share, cloud vendors. We provide a taxonomy of their services and sub-services that designates major service families namely computing, storage, databases, analytics, data pipelines, machine learning, and networking. The aim of such clustering is to indicate similarities, common design approaches and functional differences of the offered services. The outcomes are essential both for individual researchers, and bigger enterprises in their attempt to identify the set of cloud services that will utterly meet their needs without compromises. While we acknowledge the fact that this is a dynamic industry, where new services arise constantly, and old ones experience important updates, this study paints a solid image of the current offerings and gives prominence to the directions that cloud service providers are following.