EASE: Energy‐efficient task scheduling for edge computing under uncertain runtime and unstable communication conditions

Summary Continuously growing network traffic has become a major technical bottleneck of the cloud service to develop the Internet of Things (IoTs) and mobile applications. Edge computing as a promising computing pattern deployed close to service users is expected to improve the quality of service (QoS). To fully utilize the capabilities of edge devices, a Device‐to‐Device (D2D)–based computing resource sharing and aggregation framework is proposed. Under this framework, this paper exploits the Beta distributions to characterize the uncertain communication rate and processing capability of the edge environment. The reliability and energy consumption of local computing and shared computing under uncertain conditions are, respectively, studied. We model the task scheduling as an Integer Programming problem, whose objective is to minimize the energy consumption while ensuring the reliability. Based on that, a heuristic task scheduling algorithm named EASE is proposed. Through a lot of simulation experiments, the performance of EASE is effectively evaluated under the static and dynamic environments. Compared with three comparison algorithms, EASE shows many advantages in terms of reliability, adaptability, and energy saving.