Context-aware Data Operation Strategies in Edge Systems for High Application Performance

Applications running in edge computing system seamlessly collect data, process data and take actions accordingly. In many cases, the applications need to assist people in real time, and even have life-or-death consequences such as heart attack detection in healthcare and object detection in driving, which requires low job latency. Moreover, power and bandwidth are constrained resources in edge computing systems. Therefore, a challenge is how to handle data efficiently to reduce job latency, and meanwhile reduce power and bandwidth consumption. Previous works mainly focus on where to store collected source data to reduce the communication latency for source data sharing. Noticing that intermediate and final processing results may be shared by many applications, we propose to store intermediate and final results for sharing to avoid the duplicated computation. We also propose data collection that reduces data collection frequency based on context-related factors to achieve an optimal tradeoff between the overhead and decision making accuracy. We further propose data redundancy elimination to reduce the redundant data transmitted between edge and fog nodes. Our combined data operation strategies show significant improvement over the state-of-the-art methods in terms job latency, power and bandwidth consumption for experiments on both simulated and real edge environment.