Datacenters are characterized by large scale, stringent reliability requirements, and significant application diversity. However, the realities of employing hardware with non-zero failure rates mean that datacenters are subject to significant numbers of failures that can impact performance. Moreover, failures are not always obvious; network components can fail partially, dropping or delaying only subsets of packets. Thus, traditional fault detection techniques involving end-host or router-based statistics can fall short in their ability to identify these errors. We describe how to expedite the process of detecting and localizing partial datacenter faults using an end-host method generalizable to most datacenter applications. In particular, we correlate end-host transport-layer flow metrics with per-flow network paths and apply statistical analysis techniques to identify outliers and localize faulty links and/or switches. We evaluate our approach in a production Facebook front-end datacenter, focusing on its effectiveness across a range of traffic patterns.

Large cloud service providers have invested in increasingly larger datacenters to house the computing infrastructure required to support their services. Accordingly, researchers and industry practitioners alike have focused a great deal of effort designing network fabrics to efficiently interconnect and manage the traffic within these datacenters in performant yet efficient fashions. Unfortunately, datacenter operators are generally reticent to share the actual requirements of their applications, making it challenging to evaluate the practicality of any particular design.