Reinforcement learning for cost-effective IoT service caching at the edge

In the edge computing environment, Internet of Things (IoT) application service providers can rent resources from edge servers to cache their service items such as datasets and code libraries, and thus significantly reducing the service request latency and the core network traffic. Since IoT service providers need to pay for the rented edge computing resources, it is essential to find a dynamical service caching strategy to minimize the service cost while optimizing the performance objective such as service latency reduction. However, most of the existing studies either overlooked the problem of collaborative service caching or failed to consider the system's long-term service cost and latency. In this paper, to address such a problem, we coordinate multiple edge servers to cache service items and formulate the collaborative service caching problem using a multi-agent multi-armed bandit model. Furthermore, we propose a utility-aware collaborative service caching (UACSC) scheme based on a multi-agent reinforcement learning. The UACSC scheme can coordinate multiple edge servers to make a dynamic joint caching decision, aiming at maximizing the system's long-term utility. To evaluate the performance of our proposed scheme, we implement four representative baseline algorithms and compare them with six different performance metrics. In addition, a real-world case study is also presented to demonstrate the effectiveness of the UACSC scheme. Comprehensive experimental results show that the UACSC scheme can effectively coordinate multiple edge servers to cache service items, and achieve higher service latency reduction and lower service cost compared with other baseline algorithms.