Poster: Data-Aware Edge Sampling for Aggregate Query Approximation
Joel Wolfrath, Abhishek Chandra · 2020
Data stream processing is an increasingly important topic due to the prevalence of smart devices and the demand for realtime analytics. One estimate suggests that we should expect nine smart-devices per person by the year 2025 [1]. These devices generate data which might include sensor readings from a smart home, event or system logs on a device, or video feeds from surveillance cameras. As the number of devices increases, the cost of streaming the device data to the cloud over the wide-area network (WAN) will also increase substantially. Transferring and querying this data efficiently has become the focus of much academic research [2]-[5]. Edge computation affords us the opportunity to address this problem by utilizing resources close to the devices. Edge resources have many different use cases, including minimizing end-to-end latency or maximizing throughput [6], [7]. We restrict our focus to minimizing the required WAN bandwidth, which is an effort to address the increase in data volume.