Edge-LLM Inference With Cost-Aware Layer Allocation and Adaptive Scheduling
Year: 2025
Type: article
Abstract: This paper addresses two key challenges in distributed Large Language Model (LLM) inference at the edge: (1) cost-efficient and fair task allocation, and (2) dynamic scheduling under deadline constraints. We propose two mechanisms: the Fair Cost-Efficient Incentive Mechanism (FCIM) for task and layer assignment, and the Adaptive Dynamic Scheduling Algorithm (ADSA) for execution scheduling on individual devices. FCIM is an auction-based mechanism that selects cost-effective, memory-feasible devices while minimizing task latency, reward cost, and device usage. Its adaptive reward design ensures positive utility and fairness, even under shifting system priorities. ADSA enables preemption-aware, deadline-driven scheduling by dynamically reordering tasks based on arrival time and workload characteristics. Simulations demonstrate that FCIM reduces communication overhead by 54.7% and task completion time by 36.9% compared to static and performance-driven baselines, while ADSA reduces queueing delay by 39% under strict deadline constraints.
Source: IEEE Access
Authors: Sama Habibi, Ozgur Ercetin
Institution: Sabancı Üniversitesi
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4412623478