Optimizing Tail Latency by Critical Window-based Dynamic Cache Space Allocation

Data read and write tail latency in distributed storage systems affects the quality of service of applications. In this paper, we focus on requests with latency around 99.99th tail latency and design a critical window. By analyzing the target storage device distribution of requests in the critical window, we design a simple but effective cache space allocation method to optimize tail latency. Unlike traditional methods, it schedules target cache space allocation instead of requests. Since it does not change the processing of requests and I/Os, it reduces the extra time consumption incurred by the scheduling algorithm. At the same time, it solves the problems of lag, tail latency fluctuation, and high resource consumption of the load balancing-based tail latency guarantee algorithm on request scheduling. Finally, we verify the optimization effect of the method's tail-latency metrics.