A self-learning approach for proactive resource and service provisioning in fog environment

Abstract

With increasing growth in IoT, the number of devices connected to the Internet is constantly growing. Moreover, the increase in the volume of data and their transmission through the Internet of Things, as well as the existence of inadequate bandwidth, limits cloud-based storage and data processing. Both fog and cloud computing provide the storage space, application, and data for users; however, fog is more proximate to the end user with wider geographical distribution. When bringing the computing resources closer to the required location in the fog environment, the efficiency of the system increases, and the distance at which data must be transmitted decreases. On the other hand, implementing IoT applications and satisfying the requests of end users in fog computing will create new challenges in resource allocation and dynamic resource provisioning. The flexible and usually automatic mechanisms require the determination of required virtual resources to minimize the resource consumption and service level agreement (SLA). In this paper, we introduce a framework for increasing resource management efficiency in the IoT ecosystem based on deep reinforcement learning (DRL). The proposed deep neural network (DNN) method for estimating value functions improves adaptability to different oscillating conditions, learns past sensible strategies, and as a self-learning adaptive system by replicating interactions with the fog environment. The DRL algorithm finds the best destination for implementing IoT services to compromise between minimizing average power consumption, minimizing average service latency, reducing costs, and balancing resource allocation. Finally, through simulations, we show that under different loading rates, the policy used compared to other comparable solutions is to increase utilization and reduce the rate of delay, while ensuring an acceptable level of service quality.