FineComb: Measuring Microscopic Latency and Loss in the Presence of Reordering

Modern stock trading and cluster applications require microsecond latencies and almost no losses in data centers. This paper introduces an algorithm called FineComb that can obtain fine-grain end-to-end loss and latency measurements between edge routers in these networks. Such a mechanism can allow managers to distinguish between latencies and loss singularities caused by servers and those caused by the network. Compared to prior work, such as Lossy Difference Aggregator (LDA), which focused on switch-level latency measurements, the requirement of end-to-end latency measurements introduces the challenge of reordering that occurs commonly in IP networks due to churn. The problem is even more acute in switches across data center networks that employ multipath routing algorithms to exploit the inherent path diversity. Without proper care, a loss estimation algorithm can confound loss and reordering; furthermore, any attempt to aggregate delay estimates in the presence of reordering results in severe errors. FineComb deals with these problems using order-agnostic packet digests and a simple new idea we call stash recovery. Our evaluation demonstrates that FineComb is orders of magnitude more accurate than LDA in loss and delay estimates in the presence of reordering.