Following the long-held belief that the Internet is hierarchical, the network topology generators most widely used by the Internet research community, Transit-Stub and Tiers, create networks with a deliberately hierarchical structure. However, in 1999 a seminal paper by Faloutsos et al. revealed that the Internet's degree distribution is a power-law. Because the degree distributions produced by the Transit-Stub and Tiers generators are not power-laws, the research community has largely dismissed them as inadequate and proposed new network generators that attempt to generate graphs with power-law degree distributions.Contrary to much of the current literature on network topology generators, this paper starts with the assumption that it is more important for network generators to accurately model the large-scale structure of the Internet (such as its hierarchical structure) than to faithfully imitate its local properties (such as the degree distribution). The purpose of this paper is to determine, using various topology metrics, which network generators better represent this large-scale structure. We find, much to our surprise, that network generators based on the degree distribution more accurately capture the large-scale structure of measured topologies. We then seek an explanation for this result by examining the nature of hierarchy in the Internet more closely; we find that degree-based generators produce a form of hierarchy that closely resembles the loosely hierarchical nature of the Internet.

Load balancers choose among load-balanced paths to distribute traffic as if it makes no difference using one path or another. This work shows that the latency difference between load-balanced paths (called latency imbalance ), previously deemed insignificant, is now prevalent from the perspective of the cloud and affects various latency-sensitive applications. In this work, we present the first large-scale measurement study of latency imbalance from a cloud-centric view. Using public cloud around the globe, we measure latency imbalance both between data centers (DCs) in the cloud and from the cloud to the public Internet. Our key findings include that 1) Amazon's and Alibaba's clouds together have latency difference between load-balanced paths larger than 20ms to 21% of public IPv4 addresses; 2) Google's secret in having lower latency imbalance than other clouds is to use its own well-balanced private WANs to transit traffic close to the destinations and that 3) latency imbalance is also prevalent between DCs in the cloud, where 8 pairs of DCs are found to have load-balanced paths with latency difference larger than 40ms. We further evaluate the impact of latency imbalance on three applications (i.e., NTP, delay-based geolocation and VoIP) and propose potential solutions to improve application performance. Our experiments show that all three applications can benefit from considering latency imbalance, where the accuracy of delay-based geolocation can be greatly improved by simply changing how \textttping measures the minimum path latency.