Content Caching for IoT Devices by Using Self-Feedback Adversarial Semi-Bandits Learning

As massive data is generated by Internet of Things (IoT) devices, user-end devices are required to implement computation-intensive functionalities, including multi-sensory data processing and analysis, sophisticated system control schemes, and artificial intelligence. Mobile Edge Computing (MEC) is a significant technology that has the potential to extend the computation and storage capacities of user-end devices by the decentralization of required resources and contents near users and at the edge. A crucial challenge in this direction is the development of a smart mechanism to effectively cache contents upon Edge Servers (ESs) near users for high effectiveness and low latency of content delivery with the constraints on computational and storage capacities of ESs. This study employs a queuing model for analyzing total request delay and interprets the content caching problem as an adversarial semi-bandits problem. We propose an Online Self-feedback Adversarial semi-bandits Learning (OSAL) algorithm that incorporates a dual-layer learning architecture for dynamically generating caching strategies and maximizes the long-term reward. Experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods across various performance metrics in a real-world multi mobile-user content caching case. Note to Practitioners - This work addresses the important trade-off issue between delays and cache storage cost for an edge-cloud computing system that operates with limited cache storage budgets but must meet real-time requirements. Our proposed Online Self-feedback Adversarial semi-bandits Learning (OSAL) algorithm can be used to improve the operational efficiency of the systems with edge computing setting in the area of Internet of Things (IoT). These systems employ edge servers to cache the frequently reusable contents with the aim to minimize accumulative delays of all tasks while maintaining the energy cost at the acceptable level. Theoretical analysis and simulation results jointly show that the proposed solution outperforms several recently proposed ones, thus making it readily applicable to industrial scenarios.