On Understanding Time, Energy and Cost Performance of Wimpy Heterogeneous Systems for Edge Computing

Motivated by the increasing importance of edge computing and improvements in heterogeneous low-power, wimpy systems, we present a novel time-energy-cost analysis of wimpy edge computing in comparison with traditional brawny cloud computing. For this analysis, we use a brawny heterogeneous Amazon EC2 cloud instance with GPU and two wimpy heterogeneous systems represented by Jetson TK1 and Jetson TX1. As the paradigm shift to edge computing is due to the challenges of Big Data processing, we select six representative MapReduce applications with applicability in IoT edge computing. Using our time-energy-cost analysis of both wimpy edge compute nodes and cloud computing systems with GPUs, we present several key insights. Firstly, we advocate the usage of heterogeneous systems with GPU on both edge and cloud since they provide time-energy savings of up to 70% for compute-intensive applications. Secondly, we establish an equivalence ratio between one brawny cloud instance and multiple wimpy edge nodes that achieve the same or better time performance. Based on this equivalence ratio, we show that using wimpy systems as edge computing devices saves cost compared to using traditional cloud computing. Lastly, counter-to-intuition, our analysis shows that the latest Jetson TX1 system exhibits worse time-cost performance compared to the older Jetson TK1 system. This result stems from lower operating core clock frequency and lower instructions-per-cycle of Jetson TX1's GPU on some compute-intensive applications.