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Vol. 6 No. 1 (2025)
2025 Edition

Articles

Adaptive Scheduling Algorithm for AI Inference Tasks Based on Deep Reinforcement Learning in Cloud-Edge Collaborative Environment

Published 2025-07-21

  • Xiaowen Ma
    • ,
  • Shukai Fan

Xiaowen Ma
Master of Science in Marketing Analytics, University of Rochester, NY, USA

Shukai Fan
Data Sciences, University of Michigan, MI, USA

Abstract

The rapid proliferation of artificial intelligence applications and the increasing demand for real-time inference processing have created significant challenges in resource allocation and task scheduling within cloud-edge collaborative environments. This paper proposes an adaptive scheduling algorithm that leverages deep reinforcement learning techniques to optimize the distribution and execution of AI inference tasks across heterogeneous cloud-edge infrastructure. The algorithm addresses critical issues including dynamic resource allocation, latency minimization, energy consumption optimization, and quality of service maintenance in distributed computing environments. Through comprehensive experimental evaluation using synthetic and real-world datasets, our approach demonstrates superior performance compared to traditional scheduling methods, achieving up to 35% reduction in average response time, 28% improvement in resource utilization efficiency, and 42% decrease in energy consumption. The proposed framework incorporates multi-objective optimization techniques, considering factors such as computational capacity, network bandwidth, data locality, and service level agreements. The experimental results validate the effectiveness of the deep reinforcement learning-based approach in adapting to dynamic workload patterns and varying network conditions while maintaining system stability and performance consistency across different deployment scenarios.

Keywords

Deep Reinforcement Learning, Cloud-Edge Computing, AI Inference Scheduling, Resource Optimization

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