FoggyCache
Peizhen Guo, Bo Hu, Rui Li, Wenjun Hu · 2018
Mobile and IoT scenarios increasingly involve interactive and computation intensive contextual recognition. Existing optimizations typically resort to computation offloading or simplified on-device processing. Instead, we observe that the same application is often invoked on multiple devices in close proximity. Moreover, the application instances often processsimilar contextual data that map to thesame outcome. In this paper, we proposecross-device approximate computation reuse, which minimizes redundant computation by harnessing the "equivalence'' between different input values and reusing previously computed outputs with high confidence. We devise adaptive locality sensitive hashing (A-LSH) and homogenized k nearest neighbors (H-kNN). The former achieves scalable and constant lookup, while the latter provides high-quality reuse and tunable accuracy guarantee. We further incorporate approximate reuse as a service, called ame, in the computation offloading runtime. Extensive evaluation shows that, when given 95% accuracy target, ame\ consistently harnesses over 90% of reuse opportunities, which translates to reduced computation latency and energy consumption by a factor of 3 to 10.