Understanding Performance of Edge Prefetching

Zhengyuan Pang, Lifeng Sun, Zhi Wang, Yuan Xie, Shiqiang Yang

Abstract: When using online services, the time that users wait for the requested content to be downloaded from online servers to local devices can significantly influence user experience. To reduce user waiting time, the content which are likely to be requested in the future can be pre-downloaded to the local cache on edge proxies (i.e. edge prefetching). This paper addresses the performance issues of prefetching at edge proxies (e.g. Wi-Fi Access Points (APs), cellular base stations). We introduce an AP-based prefetching framework and study the impact of several factors on the benefit and the cost of this framework based on trace-driven simulation experiments. Useful insights which can be used to guide the design of prediction algorithms and edge prefetching systems are gained from our experimental results. First, increasing prediction window size of the prediction algorithms used by mobile applications can significantly reduce user waiting time. Second, the cache size is important to reducing user waiting time before a certain threshold. Third, the ratio of correct predictions to all actual requests (i.e. recall) can reduce user waiting time linearly while the ratio of correct predictions to all predictions (i.e. precision) will influence the traffic cost, so a trade-off should be made when designing a prediction algorithm.