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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
Evaluation of Energy-efficient VM Consolidation for Cloud...
Nasrin Akhter, Mohamed Othman, Ranesh Kumar Naha · 2018-12-15 · via cs.DC updates on arXiv.org

In this paper, a re-evaluation undertaken for dynamic VM consolidation problem and optimal online deterministic algorithms for the single VM migration in an experimental environment. We proceeded to focus on energy and performance trade-off by planet lab workload traces, which consists of a thousand Planetlab VMs with widespread simulation environments. All experiments are done in a simulated cloud environment by the CloudSim simulation tool. A new paradigm of utility-oriented IT services is cloud computing, which offers a pay-as-you-go model. In recent years, there has been increasing interest among many users from business, scientific, engineering and educational territories in cloud computing. There is increasing concern that high energy consumption issues are a disadvantage for various institutions. However, so far too little attention has been given to the various methods to reduce energy consumption in cloud environments while ensuring performance. Besides the evaluation of energy-efficient data center management algorithms in the cloud, we proposed a further research directed toward the development of energy efficient algorithms. By the experimental evaluation of the current proposal for the competitive analysis of dynamic VM consolidation and optimal online deterministic algorithms for the single VM migration, we found different results for different algorithm combinations. Cloud-based data centers` consume massive energy, which has a negative effect on the environment and operational cost, this work contributes to the energy consumption reduction in the cloud environment.