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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
Simple Destination-Swap Strategies for Adaptive Intra- an...
Chen Avin, Omer Dunay, Stefan Schmid · 2013-09-23 · via cs.DC updates on arXiv.org

This paper investigates the opportunities and limitations of adaptive virtual machine (VM) migration to reduce communication costs in a virtualized environment. We introduce a new formal model for the problem of online VM migration in two scenarios: (1) VMs can be migrated arbitrarily in the substrate network; e.g., a private cloud provider may have an incentive to reduce the overall communication cost in the network. (2) VMs can only be migrated within a given tenant; e.g., a user that was assigned a set of physical machines may exchange the functionality of the VMs on these machines. We propose a simple class of Destination-Swap algorithms which are based on an aggressive collocation strategy (inspired by splay datastructures) and which maintain a minimal and local amount of per-node (amortized cost) information to decide where to migrate a VM and how; thus, the algorithms react quickly to changes in the load. The algorithms come in two main flavors, an indirect and distributed variant which keeps existing VM placements local, and a direct variant which keeps the number of affected VMs small. We show that naturally, inter-tenant optimizations yield a larger potential for optimization, but generally also a tenant itself can improve its embedding. Moreover, there exists an interesting tradeoff between direct and indirect strategies: indirect variants are preferable under skewed and sparse communication patterns due to their locality properties.