GAIN: GPU-Based Constraint Checking for Context Consistency

Abstract

Applications in pervasive computing are often context-aware. However, due to uncontrollable environmental noises, contexts collected by applications can be distorted or even conflicting with each other. This is known as the context inconsistency problem. To provide reliable services, applications need to validate contexts before using them. One promising approach is to check contexts against consistency constraints at the runtime of applications. However, this can bring heavy computations due to tremendous amounts of contexts, thus leading to deteriorated performance to applications. Previous work has proposed incremental or concurrent checking techniques to improve the checking performance, but they heavily rely on CPU computing. In this paper, we propose a novel technique GAIN to exploit GPU computing to improve the checking performance. GAIN can automatically recognize parallel units in a constraint and schedule their checking in parallel on GPU cores. We evaluated GAIN with various constraints under different workloads. Our evaluation results show that, compared to CPU-based computing, GAIN saves CPU computing resources for pervasive applications while checks constraints much more efficiently.