GPGPUs (General Purpose Graphic Processing Units) provide massive computational power. However, applying GPGPU technology to real-time computing is challenging due to the non-preemptive nature of GPGPUs. Especially, a job running in a GPGPU or a data copy between a GPGPU and CPU is non-preemptive. As a result, a high priority job arriving in the middle of a low priority job execution or memory copy suffers from priority inversion. To address the problem, we present a new lightweight approach to supporting preemptive memory copies and job executions in GPGPUs. Moreover, in our approach, a GPGPU job and memory copy between a GPGPU and the hosting CPU are run concurrently to enhance the responsiveness. To show the feasibility of our approach, we have implemented a prototype system for preemptive job executions and data copies in a GPGPU. The experimental results show that our approach can bound the response times in a reliable manner. In addition, the response time of our approach is significantly shorter than those of the unmodified GPGPU runtime system that supports no preemption and an advanced GPGPU model designed to support prioritization and performance isolation via preemptive data copies.

Applying cyber security mechanisms, such as cryptography, trusted computing, and network intrusion detection, is insufficient to secure cyber-physical systems (CPS) such as smart vehicles, because most sensors (and actuators) are designed without security considerations and remain vulnerable to sensor spoofing attacks in the analog domain. To address this problem, we present a new approach to detect and handle sensor spoofing attack against automotive radars-a key component for assisted and autonomous driving-by extending multiple beamforming in an automotive multi-input multi-output (MIMO) radar. In a simulation study for adaptive cruise control based on the car following model, our approach significantly outperforms three state-of-the-art baselines in terms of attack detection and ranging accuracy.

It is challenging to process transactions in a timely fashion using fresh data, e.g., current stock prices, since database workloads may considerably vary due to dynamic data/resource contention. Further, transaction timeliness and data freshness requirements may compete for system resources. In this paper, we propose a novel feedback control model to support the desired data service delay by managing the size of the ready queue, which indicates the amount of the backlog in the database. We also propose a new self-adaptive update policy to adapt the freshness of cold data in a differentiated manner based on temporal data access and update patterns. Unlike most existing work on feedback control of real-time database (RTDB) performance, we actually implement and evaluate feedback control and database workload adaptation techniques in a real database testbed modeling stock trades. For performance evaluation, we undertake experiments in the testbed, which consists of thousands of client threads concurrently requesting database services for stock quotes, trades, and portfolio updates in a bursty manner. In these experiments, our database system supports the desired response time bound and data freshness, while processing a significantly larger number of transactions in time compared to the tested baselines.