Estimating the performance of hypothetical cloud service deployments: A measurement-based approach

Abstract

To optimize network performance, cloud service providers have a number of options available to them, including co-locating production servers in well-connected Internet eXchange (IX) points, deploying data centers in additional locations, or contracting with external Content Distribution Networks (CDNs). Some of these options can be very costly, and some may or may not improve performance significantly. Cloud service providers would clearly like to be able to estimate a priori performance gain of the various options before sinking significant capital expenditures into major infrastructure changes. In this paper we take a measurement-oriented approach and develop methodologies that accurately predict the performance improvement for making major infrastructure changes. Our methodologies leverage active web content, existing large-scale CDN infrastructures, and the SpeedTest network. We then apply our methodologies and a CloudBeacon tool to the problem of locating satellite data centers throughout the world. The results show that for North America, a deployment limited to 11 locations will be sufficient. However, in order to provide good latency and throughput performance on a global scale, somewhere between a total of 36 and 72 cloud-service locations with good peering connections is most likely needed.