Joint Task Offloading, Resource Allocation and Model Placement for AI as a Service in 6G Network
Yuhao Chai, Kaice Gao, Guohan Zhang, Lu Lu, Qin Li, Yong Zhang
Abstract
In the future, 6G network is expected to achieve deep integration of communication and computation, where computation-centric services will be ubiquitous in the network. There are differences in data size, computing power types (CPU/GPU), model complexity, and Quality of Service (QoS) requirements among various CPU computing services and artificial intelligence (AI) services. By providing AI as a Service (AIaaS) in 6G network, the deployment of AI models and the scheduling of task and computing resources can be accelerated. The fundamental challenge lies in the effective amalgamation of the long-term strategy of the model placement problem and the short-term strategy of the task scheduling problem to attain dynamic scheduling and management of tasks and heterogeneous computing resources. A two-timescale optimization method for joint task offloading, computing resource allocation and model placement is proposed in this article. We present an edge-network-cloud framework that configures AIaaS functional units, taking into account the heterogeneous computing requirements and QoS demands of different services. A long-term problem to minimize latency and energy consumption is formulated. To work out the coupled optimization parameters, the problem is decomposed into short-term deterministic sub-problems using Lyapunov optimization. We propose low-complexity algorithms for joint task offloading strategy based on deferred acceptance algorithm, computing resource allocation strategy based on convex optimization, and model placement strategy based on multi-armed bandits. Experimental results demonstrate that our approach outperforms reinforcement learning and other popular optimization algorithms in terms of complexity and effectiveness.