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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
CloudPowerCap: Integrating Power Budget and Resource Mana...
Yong Fu, Anne Holler, Chenyang Lu · 2014-03-06 · via cs.DC updates on arXiv.org

In many datacenters, server racks are highly underutilized. Rack slots are left empty to keep the sum of the server nameplate maximum power below the power provisioned to the rack. And the servers that are placed in the rack cannot make full use of available rack power. The root cause of this rack underutilization is that the server nameplate power is often much higher than can be reached in practice. To address rack underutilization, server vendors are shipping support for per-host power caps, which provide a server-enforced limit on the amount of power that the server can draw. Using this feature, datacenter operators can set power caps on the hosts in the rack to ensure that the sum of those caps does not exceed the rack's provisioned power. While this approach improves rack utilization, it burdens the operator with managing the rack power budget across the hosts and does not lend itself to flexible allocation of power to handle workload usage spikes or to respond to changes in the amount of powered-on server capacity in the rack. In this paper we present CloudPowerCap, a practical and scalable solution for power budget management in a virtualized environment. CloudPowerCap manages the power budget for a cluster of virtualized servers, dynamically adjusting the per-host power caps for hosts in the cluster. We show how CloudPowerCap can provide better use of power than per-host static settings, while respecting virtual machine resource entitlements and constraints.