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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
An Alternative Paradigm for Developing and Pricing Storag...
Christos Patsonakis, Mema Roussopoulos · 2019-02-03 · via cs.DC updates on arXiv.org

Smart contract platforms facilitate the development of important and diverse distributed applications in a simple manner. This simplicity stems from the inherent utility of employing the state of smart contracts to store, query and verify the validity of application data. In Ethereum, data storage incurs an underpriced, non-recurring, predefined fee. Furthermore, as there is no incentive for freeing or minimizing the state of smart contracts, Ethereum is faced with a tragedy of the commons problem with regards to its monotonically increasing state. This issue, if left unchecked, may lead to centralization and directly impact Ethereum's security and longevity. In this work, we introduce an alternative paradigm for developing smart contracts in which their state is of constant size and facilitates the verification of application data that are stored to and queried from an external, potentially unreliable, storage network. This approach is relevant for a wide range of applications, such as any key-value store. We evaluate our approach by adapting the most widely deployed standard for fungible tokens, i.e., the ERC20 token standard. We show that Ethereum's current cost model penalizes our approach, even though it minimizes the overhead to Ethereum's state and aligns well with Ethereum's future. We address Ethereum's monotonically increasing state in a two-fold manner. First, we introduce recurring fees that are proportional to the state of smart contracts and adjustable by the miners that maintain the network. Second, we propose a scheme where the cost of storage-related operations reflects the effort that miners have to expend to execute them. Lastly, we show that under such a pricing scheme that encourages economy in the state consumed by smart contracts, our ERC20 token adaptation reduces the incurred transaction fees by up to an order of magnitude.