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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
No Reservations: A First Look at Amazon's Reserved Instan...
Pradeep Ambati, David Irwin, Prashant Shenoy · 2020-05-26 · via cs.DC updates on arXiv.org

Cloud users can significantly reduce their cost (by up to 60\%) by reserving virtual machines (VMs) for long periods (1 or 3 years) rather than acquiring them on demand. Unfortunately, reserving VMs exposes users to \emph{demand risk} that can increase cost if their expected future demand does not materialize. Since accurately forecasting demand over long periods is challenging, users often limit their use of reserved VMs. To mitigate demand risk, Amazon operates a Reserved Instance Marketplace (RIM) where users may publicly list the remaining time on their VM reservations for sale at a price they set. The RIM enables users to limit demand risk by either selling VM reservations if their demand changes, or purchasing variable- and shorter-term VM reservations that better match their demand forecast horizon. Clearly, the RIM's potential to mitigate demand risk is a function of its price characteristics. However, to the best of our knowledge, historical RIM prices have neither been made publicly available nor analyzed. To address the problem, we have been monitoring and archiving RIM prices for 1.75 years across all 69 availability zones and 22 regions in Amazon's Elastic Compute Cloud (EC2). This paper provides a first look at this data and its implications for cost-effectively provisioning cloud infrastructure.