Deep Reinforcement Learning for Containerized Edge Intelligence Inference Request Processing in IoT Edge Computing

Edge intelligence (EI) refers to a set of connected systems and devices for artificial intelligence (AI) data collected and learned near the data collection site. The EI model inference phase has been improved through edge caching technologies such as intelligent models (IMs). IM inference across heterogeneously distributed edge nodes is worthy of discussion. The present focuses on software-defined infrastructure (SDI) and introduces a containerized EI framework for a mobile wearable Internet-of-Things (IoT) system. This framework, called the containerized edge intelligence framework (CEIF), is an inter-working architecture that allows the provisioning of containerized EI processing intelligent services related to mobile wearable IoT systems. CEIF enables dynamic instantiation of the inference services of AI models that have been pre-trained on clouds. It also accommodates edge computing devices (ECDs) running the container virtualization technique. Dynamic AI learning policies can also help with workload optimization, thereby reducing the response time of the requests of the EI inference. To stall the rapid increase in user workload when inferring the collected data for analysis, we then propose a deep q-learning algorithm in which the container cluster platform learns the varying user workload at the location of each ECD. The requests of the EI inference are scaled with the learned value and are processed successfully without overloading the ECD. When evaluated in a case study, the proposed algorithm enabled scaling of the processing requests of the EI inference in a containerized EI system while minimizing the number of instantiated container EI instances. The EI inference's requests are completed in an under-loaded container EI cluster system.

I. Introduction

The rapid increase of Internet of Things (IoT) devices has raised the demand for IoT-enabled artificial intelligence (AI) platforms. Located in the cloud, these AI platforms learn a new capability from the existing data (training) and apply this capability to new data via an application or service (inference). However, these AI platforms require high computing power to support deep learning (DL) applications, diverse computing resources, massive networking, and high response latency [1]. These challenges can be met by pooling the computing resources at the edge of the network, a concept known as edge computing (EC) [2]. Here, an edge refers to a location, far from the cloud, where an edge device runs edge applications. EC defines the operation of running workloads on these devices [2].