Moving Beyond End-to-End Path Information to Optimize CDN Performance
Abstract
Replicating content across a geographically distributed set of servers and redirecting clients to the closest server in terms of latency has emerged as a common paradigm for improving client performance. In this paper, we analyze latencies measured from servers in Google’s content distribution network (CDN) to clients all across the Internet to study the effectiveness of latency-based server selection. Our main result is that redirecting every client to the server with least latency does not suffice to optimize client latencies. First, even though most clients are served by a geographically nearby CDN node, a sizeable fraction of clients experience latencies several tens of milliseconds higher than other clients in the same region. Second, we find that queueing delays often override the benefits of a client interacting with a nearby server.
To help the administrators of Google’s CDN cope with these
problems, we have built a system called WhyHigh. First, WhyHigh measures client latencies across all nodes in the CDN and correlates measurements to identify the prefixes affected by inflated latencies. Second, since clients in several thousand prefixes have poor latencies, WhyHigh prioritizes problems based on the impact that solving them would have, e.g., by identifying either an AS path common
to several inflated prefixes or a CDN node where path inflation is widespread. Finally, WhyHigh diagnoses the causes for inflated latencies using active measurements such as traceroutes and pings, in combination with datasets such as BGP paths and flow records. Typical causes discovered include lack of peering, routing misconfigurations, and side-effects of traffic engineering. We have used WhyHigh to diagnose several instances of inflated latencies, and our efforts over the course of a year have significantly helped improve the performance offered to clients by Google’s CDN.
An anonymized data set is available for download.
To help the administrators of Google’s CDN cope with these
problems, we have built a system called WhyHigh. First, WhyHigh measures client latencies across all nodes in the CDN and correlates measurements to identify the prefixes affected by inflated latencies. Second, since clients in several thousand prefixes have poor latencies, WhyHigh prioritizes problems based on the impact that solving them would have, e.g., by identifying either an AS path common
to several inflated prefixes or a CDN node where path inflation is widespread. Finally, WhyHigh diagnoses the causes for inflated latencies using active measurements such as traceroutes and pings, in combination with datasets such as BGP paths and flow records. Typical causes discovered include lack of peering, routing misconfigurations, and side-effects of traffic engineering. We have used WhyHigh to diagnose several instances of inflated latencies, and our efforts over the course of a year have significantly helped improve the performance offered to clients by Google’s CDN.
An anonymized data set is available for download.
Research Areas
