Cover Image

Rethinking Inference Placement for Deep Learning across Edge and Cloud Platforms: A Multi-Objective Optimization Perspective and Future Directions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Zongshun, Matta, Ibrahim
Format:	Preprint
Published:	2025
Subjects:	Distributed, Parallel, and Cluster Computing Artificial Intelligence Performance
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference
by: Jeong, Bodon, et al.
Published: (2026)

SERFLOW: A Cross-Service Cost Optimization Framework for SLO-Aware Dynamic ML Inference
by: Zhang, Zongshun, et al.
Published: (2025)

Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers
by: Renney, Harri, et al.
Published: (2026)

EdgeProfiler: A Fast Profiling Framework for Lightweight LLMs on Edge Using Analytical Model
by: Pinnock, Alyssa, et al.
Published: (2025)

AGOCS -- Accurate Google Cloud Simulator Framework
by: Sliwko, Leszek, et al.
Published: (2025)

ExpertFlow: Adaptive Expert Scheduling and Memory Coordination for Efficient MoE Inference
by: Shen, Zixu, et al.
Published: (2025)

E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
by: Zhang, Le, et al.
Published: (2024)

The SAP Cloud Infrastructure Dataset: A Reality Check of Scheduling and Placement of VMs in Cloud Computing
by: Uhlig, Arno, et al.
Published: (2025)

CoServe: Efficient Collaboration-of-Experts (CoE) Model Inference with Limited Memory
by: Suo, Jiashun, et al.
Published: (2025)

Adaptive Workload Distribution for Accuracy-aware DNN Inference on Collaborative Edge Platforms
by: Taufique, Zain, et al.
Published: (2023)

Democratizing AI: A Comparative Study in Deep Learning Efficiency and Future Trends in Computational Processing
by: Amin, Lisan Al, et al.
Published: (2026)

Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge
by: Abstreiter, Maximilian, et al.
Published: (2025)

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
by: Li, Xiangchen, et al.
Published: (2025)

Collaborative Processing for Multi-Tenant Inference on Memory-Constrained Edge TPUs
by: Ng, Nathan, et al.
Published: (2026)

When AI Bends Metal: AI-Assisted Optimization of Design Parameters in Sheet Metal Forming
by: Tarraf, Ahmad, et al.
Published: (2025)

Opt4GPTQ: Co-Optimizing Memory and Computation for 4-bit GPTQ Quantized LLM Inference on Heterogeneous Platforms
by: Zhang, Yaozheng, et al.
Published: (2025)

oneDAL Optimization for ARM Scalable Vector Extension: Maximizing Efficiency for High-Performance Data Science
by: Sharma, Chandan, et al.
Published: (2025)

Bridding OT and PaaS in Edge-to-Cloud Continuum
by: Barrios, Carlos J, et al.
Published: (2025)

Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices
by: Sedlak, Boris, et al.
Published: (2025)

Standardized Methods and Recommendations for Green Federated Learning
by: Tapp, Austin, et al.
Published: (2026)

Green or Fast? Learning to Balance Cold Starts and Idle Carbon in Serverless Computing
by: Sun, Bowen, et al.
Published: (2026)

Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures
by: Vellaisamy, Prabhu, et al.
Published: (2025)

Edge-First Language Model Inference: Models, Metrics, and Tradeoffs
by: Jang, SiYoung, et al.
Published: (2025)

Scaling Multi Agent Reinforcement Learning for Underwater Acoustic Tracking via Autonomous Vehicles
by: Gallici, Matteo, et al.
Published: (2025)

Resource Management Schemes for Cloud-Native Platforms with Computing Containers of Docker and Kubernetes
by: Mao, Ying, et al.
Published: (2020)

Cloud Resource Allocation with Convex Optimization
by: Boghani, Shayan, et al.
Published: (2025)

FastPersist: Accelerating Model Checkpointing in Deep Learning
by: Wang, Guanhua, et al.
Published: (2024)

Can Large Language Models Predict Parallel Code Performance?
by: Bolet, Gregory, et al.
Published: (2025)

Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study
by: Zhu, Jianwei, et al.
Published: (2024)

GhostServe: A Lightweight Checkpointing System in the Shadow for Fault-Tolerant LLM Serving
by: Jayakody, Shakya, et al.
Published: (2026)

Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection
by: Barron, Ryan, et al.
Published: (2024)

Counting Without Running: Evaluating LLMs' Reasoning About Code Complexity
by: Bolet, Gregory, et al.
Published: (2025)

Shared Memory-contention-aware Concurrent DNN Execution for Diversely Heterogeneous System-on-Chips
by: Dagli, Ismet, et al.
Published: (2023)

Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions
by: Teranishi, Keita, et al.
Published: (2025)

Compiler-First State Space Duality and Portable $O(1)$ Autoregressive Caching for Inference
by: Santoni, Cosmo
Published: (2026)

Multi-DNN Inference of Sparse Models on Edge SoCs
by: Luo, Jiawei, et al.
Published: (2026)

ARKV: Adaptive and Resource-Efficient KV Cache Management under Limited Memory Budget for Long-Context Inference in LLMs
by: Lei, Jianlong, et al.
Published: (2026)

Adaptive DNN Partitioning and Offloading in Heterogeneous Edge-Cloud Continuum
by: Deng, Akuen Akoi, et al.
Published: (2026)

Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
by: Jung, Victor J. B., et al.
Published: (2024)

Generative AI on the Edge: Architecture and Performance Evaluation
by: Nezami, Zeinab, et al.
Published: (2024)