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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
IOArbiter: Dynamic Provisioning of Backend Block Storage ...
Moo-Ryong Ra, Hee Won Lee · 2019-04-23 · via cs.DC updates on arXiv.org

With the advent of virtualization technology, cloud computing realizes on-demand computing. The capability of dynamic resource provisioning is a fundamental driving factor for users to adopt the cloud technology. The aspect is important for cloud service providers to optimize the expense for running the infrastructure as well. Despite many technological advances in related areas, however, it is still the case that the infrastructure providers must decide hardware configuration before deploying a cloud infrastructure, especially from the storage's perspective. This static nature of the storage provisioning practice can cause many problems in meeting tenant requirements, which often come later into the picture. In this paper, we propose a system called IOArbiter that enables the dynamic creation of underlying storage implementation in the cloud. IOArbiter defers storage provisioning to the time at which a tenant actually requests a storage space. As a result, an underlying storage implementation, e.g., RAID-5, 6 or Ceph storage pool with 6+3 erasure coding, will be materialized at the volume creation time. Using our prototype implementation with Openstack Cinder, we show that IOArbiter can simultaneously satisfy a number of different tenant demands, which may not be possible with a static configuration strategy. Additionally QoS mechanisms such as admission control and dynamic throttling help the system mitigate a noisy neighbor problem significantly.