Co-Approximator: Enabling Performance Prediction in Colocated Applications.

Abstract

Today's Internet of Things (IoT) devices can colocate multiple applications on a platform with hardware resource sharing. Such colocations allow for increasing the throughput of contemporary IoT applications, similar to the use of multi-tenancy in clouds. However, avoiding performance interference among colocated applications through virtualized performance isolation is expensive in IoT platforms due to resource limitations. Hence, on the one hand, colocated IoT applications without performance isolation contend for shared limited resources, which makes their performance variance discontinuous and a priori unknown. On the other hand, different combinations of colocated applications make the overall state space exceedingly large. All of these make such colocated routines challenging to predict, making it difficult to plan which applications to colocate on which platform.We propose Co-Approximator, a technique for systematically sampling an exponentially large colocated application state space and efficiently approximating it from only four available complete colocation samples. We demonstrate the performance of Co-Approximator with 17 standard benchmarks and three pipelined data processing applications on different IoT platforms, where on average, Co-Approximator reduces existing techniques' approximation error from 61% to just 7%.