Context-aware resource allocation for mobile edge cloud

Abstract

Selecting the best resource for service performance in a smart space is one of the key goals of mobile edge computing, which makes up the edge cloud. As more and more devices in the Internet of Things (IoT) environment become service-specific and edge cloud service requirements become more diverse, it is difficult to accurately measure resource capabilities with previous utility functions based solely on computation metrics such as CPU and memory. In this paper, we propose a resource allocation scheme for efficient mobile edge cloud through context awareness. We present three contexts that can influence resource capability from various service cases and express how the ability of each resource element can be changed through generalized utility function. Our proposed scheme has been verified by experiments performed through actual service scenarios and selected resources optimized for service requirements.