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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
The carbon footprint of distributed cloud storage
Lorenzo Posani, Alessio Paccoia, Marco Moschettini · 2018-03-19 · via cs.DC updates on arXiv.org

The ICT (Information Communication Technologies) ecosystem is estimated to be responsible, as of today, for 10% of the total worldwide energy demand - equivalent to the combined energy production of Germany and Japan. Cloud storage, mainly operated through large and densely-packed data centers, constitutes a non-negligible part of it. However, since the cloud is a fast-inflating market and the energy-efficiency of data centers is mostly an insensitive issue for the collectivity, its carbon footprint shows no signs of slowing down. In this paper, we analyze a novel paradigm for cloud storage (implemented by Cubbit, http://cubbit.io), in which data are stored and distributed over a network of p2p-interacting ARM-based single-board devices. We compare Cubbit's distributed cloud to the traditional centralized solution in terms of environmental footprint and energy efficiency. We demonstrate that, compared to the centralized cloud, the distributed architecture of Cubbit has a carbon footprint reduced of a 77% factor for data storage and of a 50% factor for data transfers. These results provide an example of how a radical paradigm shift in a large-reach technology can benefit both the final consumer as well as our society as a whole.