AI-Based Resource Provisioning of IoE Services in 6G: A Deep Reinforcement Learning Approach

• Published in: IEEE eTransactions on network and service management 2021-09, Vol.18 (3), p.3527-3540
• Main Authors: Sami, Hani, Otrok, Hadi, Bentahar, Jamal, Mourad, Azzam
• Format: Article
• Language: English
• Subjects: 5G mobile communication; 6G mobile communication; Artificial intelligence; Clustering algorithms; Deep learning; deep reinforcement learning (DRL); Dynamic scheduling; Edge computing; Internet of Everything (IoE); Internet of Things; Markov processes; Mobile computing; Placement; Provisioning; Resource allocation; Resource provisioning; resource scaling; Scaling; Servers; service placement
• ISSN: 1932-4537; 1932-4537
• DOI: 10.1109/TNSM.2021.3066625

Currently, researchers have motivated a vision of 6G for empowering the new generation of the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G, more computing resources are required, a problem that is dealt with by Mobile Edge Computing (MEC). However, due to the dynamic change of service demands from various locations, the limitation of available computing resources of MEC, and the increase in the number and complexity of IoE services, intelligent resource provisioning for multiple applications is vital. To address this challenging issue, we propose in this paper IScaler, a novel intelligent and proactive IoE resource scaling and service placement solution. IScaler is tailored for MEC and benefits from the new advancements in Deep Reinforcement Learning (DRL). Multiple requirements are considered in the design of IScaler's Markov Decision Process. These requirements include the prediction of the resource usage of scaled applications, the prediction of available resources by hosting servers, performing combined horizontal and vertical scaling, as well as making service placement decisions. The use of DRL to solve this problem raises several challenges that prevent the realization of IScaler's full potential, including exploration errors and long learning time. These challenges are tackled by proposing an architecture that embeds an Intelligent Scaling and Placement module (ISP). ISP utilizes IScaler and an optimizer based on heuristics as a bootstrapper and backup. Finally, we use the Google Cluster Usage Trace dataset to perform real-life simulations and illustrate the effectiveness of IScaler's multi-application autonomous resource provisioning.