Edge–Cloud Collaborative Computation Offloading for Mixed Traffic
Qirui Li, Mian Guo, Zhiping Peng, Delong Cui, Jieguang He
Abstract
Edge computing can avoid long network transmission delay, thus reducing the task response time. However, edge servers cannot respond quickly to massive computing requests due to limited computing power, which may lead to long queuing delays, thus failing to meet low latency requirements of delay-sensitive Internet of Things (IoT) applications. Furthermore, the per-computing-unit energy consumption in edge computing is much higher than that in cloud computing. This article studies the edge–cloud collaborative computing for energy efficiency while meeting the delay requirements of IoT applications in mobile edge computing systems. We formulate the energy efficient computation offloading problem for mixed tasks, including divisible and indivisible tasks. We theoretically analyze the relationship between delay guarantee and energy consumption in edge–cloud collaborative environment. We further analyze the system stability based on Lyapunov stability theory. Then, an energy efficient and delay guaranteed edge–cloud collaborative computation offloading algorithm is proposed to provide delay guarantee for delay-sensitive IoT applications while minimizing the energy consumption of the edge–cloud computing system. Theoretical analysis and experimental results show that, compared with the benchmark algorithms, our algorithm can provide strict delay guarantee for stringent-delay-sensitive IoT applications, and low delay for soft-delay-sensitive IoT applications, while consuming lower energy.