.png){kind=icon}
.png){kind=icon}
Return
Multiendpoint DAG-Driven Joint Partitioning-Offloading and Scheduling Optimization for DNN Inference
.png){kind=icon}
PRE
AI
DOI：10.1109/JIOT.2025.3591531.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Model partitioning techniques, which decompose and collaboratively execute subtasks of deep neural networks (DNNs), have emerged as a critical strategy for enhancing distributed inference efficiency. However, in mobile edge computing (MEC), dynamic load fluctuations at edge nodes and the complexity of cross-node task dependencies make the delay minimization problem extremely challenging. Existing studies predominantly adopt a decoupled optimization framework that separately addresses partitioning-offloading and pipeline scheduling, neglecting their inherent cyclic state-dependent coupling. This oversight leads to suboptimal solutions, such as pipeline stagnation caused by mismatched computation and communication timestamps. To address these challenges, we propose a multiendpoint directed acyclic graph (DAG)-driven cooperative optimization approach, enabling partitioning-offloading and pipeline scheduling in MEC. Specifically, the approach involves two core steps: 1) Dynamic prescheduling: We propose an improved DNN scheduling algorithm for constrained subtasks, which simulates node-level queuing delays and pipeline stalls under real-world constraints, translating runtime states into latency objectives. 2) Partitioning and offloading solution retrieval: Based on latency objectives, we introduce a novel multiendpoint DAG structure and design a multinode collaborative optimization retrieval algorithm, enabling adaptive partitioning-offloading remapping of subtasks. Experiments demonstrate the superiority of the proposed method over other advanced methods, reducing the time overhead by an average of 24% and 75% in two different scenarios, respectively. The resource code can be found at: https://github.com/aiheiheiheii/Partition_Scheduling.git.
Keywords:
Internet of Things
Computational modeling
Servers
Pipelines
Delays
Dynamic scheduling
Collaboration
Artificial neural networks
Optimization
Resource management
Constrained scheduling
distributed computing
deep neural network (DNN) inference
model partitioning
multiendpoint directed acyclic graph (DAG)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W