PASTO: Enabling Secure and Efficient Task Offloading in TrustZone-Enabled Edge Clouds
Yuepeng Li, Deze Zeng, Lin Gu, Andong Zhu, Quan Chen, Shui Yu
Abstract
Offloading tasks to edge servers has been regarded as a potential way to solve the computation resource poverty problem of the end devices like autonomous vehicles. However, due to the sharing and openness features of edge computing, it raises severe security problems. In order to combat such issues, Trust Execution Environment (TEE), such as TrustZone, is advocated to strength the security of task offloading to edge computing in the hardware level. For the exploration of TrustZone, the inevitable involvement of cryptographic operations make existing offloading strategies not applicable, or not efficient enough, any more. In addition, TrustZone does not allow multiple tasks to coexist and execute on one CPU core at the same time. Taking the above issues into consideration, we investigate a secure task offloading problem for minimizing the total task completion time under the constraint of energy budget. To address this problem, we propose a Priority-aware Secure Task Offloading (PASTO) algorithm and evaluate the performance of PASTO by both numerical analysis and prototype based experiments. All the experiment results show that PASTO can effectively reduce the total task completion time in comparison with other state-of-the-art offloading approaches in TrustZone-enabled edge clouds.