Value Iteration Architecture Based Deep Learning for Intelligent Routing Exploiting Heterogeneous Computing Platforms

• Published in: IEEE transactions on computers Vol. 68; no. 6; pp. 939 - 950
• Main Authors: Fadlullah, Zubair Md, Mao, Bomin, Tang, Fengxiao, Kato, Nei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Complexity; Computation; Computer architecture; Computer simulation; computing platforms; Deep learning; deep reinforcement learning; Graphics processing units; Heterogeneous networks; Iterative methods; link failures; Machine learning; Network routing; Network topologies; Packet transmission; Platforms; Proposals; Routing; Supervised learning
• ISSN: 0018-9340; 1557-9956
• DOI: 10.1109/TC.2018.2874483

Recently, the rapid advancement of high computing platforms has accelerated the development and applications of artificial intelligence techniques. Deep learning, which has been regarded as the next paradigm to revolutionize users’ experiences, has attracted networking researchers’ interests to relieve the burden due to the exponentially growing traffic and increasing complexities. Various intelligent packet transmission strategies have been proposed to tackle different network problems. However, most of the existing research just focuses on the network related improvements and neglects the analysis about the computation consumptions. In this paper, we propose a Value Iteration Architecture based Deep Learning (VIADL) method to conduct routing design to address the limitations of existing deep learning based routing algorithms in dynamic networks. Besides the network performance analysis, we also study the complexity of our proposal as well as the resource consumptions in different deployment manners. Moreover, we adopt the Heterogeneous Computing Platform (HCP) to conduct the training and running of the proposed VIADL since the theoretical analysis demonstrates the significant reduction of the time complexity with the multiple GPUs in HCPs. Furthermore, simulation results demonstrate that compared with the existing deep learning based method, our proposal can guarantee more stable network performance when network topology changes.