DNS-based Internet Client Clustering and Characterization

This paper proposes a novel protocol which uses the Internet Domain Name System (DNS) to partition Web clients into disjoint sets, each of which is associated with a single DNS server. We define an L-DNS cluster to be a grouping of Web Clients that use the same Local DNS server to resolve Internet host names. We identify such clusters in real-time using data obtained from a Web Server in conjunction with that server's Authoritative DNS―both instrumented with an implementation of our clustering algorithm. Using these clusters, we perform measurements from four distinct Internet locations. Our results show that L-DNS clustering enables a better estimation of proximity of a Web Client to a Web Server than previously proposed techniques. Thus, in a Content Distribution Network, a DNS-based scheme that redirects a request from a web client to one of many servers based on the client's name server coordinates (e.g., hops/latency/loss-rates between the client and servers) would perform better with our algorithm.

Safe Compositional Network Sketches: NetSketch Tool Implementation

Numerous problems exist that can be modeled as traffic through a network in which constraints exist to regulate flow. Vehicular road travel, computer networks, and cloud based resource distribution, among others all have natural representations in this manner. As these networks grow in size and/or complexity, analysis and certification of the safety invariants becomes increasingly costly. The NetSketch formalism introduces a lightweight verification framework that allows for greater scalability than traditional analysis methods. The NetSketch tool was developed to provide the power of this formalism in an easy to use and intuitive user interface.

On the Size Distribution of Autonomous Systems

This paper explores reasons for the high degree of variability in the sizes of ASes that have recently been observed, and the processes by which this variable distribution develops. AS size distribution is important for a number of reasons. First, when modeling network topologies, an AS size distribution assists in labeling routers with an associated AS. Second, AS size has been found to be positively correlated with the degree of the AS (number of peering links), so understanding the distribution of AS sizes has implications for AS connectivity properties. Our model accounts for AS births, growth, and mergers. We analyze two models: one incorporates only the growth of hosts and ASes, and a second extends that model to include mergers of ASes. We show analytically that, given reasonable assumptions about the nature of mergers, the resulting size distribution exhibits a power law tail with the exponent independent of the details of the merging process. We estimate parameters of the models from measurements obtained from Internet registries and from BGP tables. We then compare the models solutions to empirical AS size distribution taken from Mercator and Skitter datasets, and find that the simple growth-based model yields general agreement with empirical data. Our analysis of the model in which mergers occur in a manner independent of the size of the merging ASes suggests that more detailed analysis of merger processes is needed.

How Well Can TCP Infer Network State?

The Transmission Control Protocol (TCP) has been the protocol of choice for many Internet applications requiring reliable connections. The design of TCP has been challenged by the extension of connections over wireless links. We ask a fundamental question: What is the basic predictive power of TCP of network state, including wireless error conditions? The goal is to improve or readily exploit this predictive power to enable TCP (or variants) to perform well in generalized network settings. To that end, we use Maximum Likelihood Ratio tests to evaluate TCP as a detector/estimator. We quantify how well network state can be estimated, given network response such as distributions of packet delays or TCP throughput that are conditioned on the type of packet loss. Using our model-based approach and extensive simulations, we demonstrate that congestion-induced losses and losses due to wireless transmission errors produce sufficiently different statistics upon which an efficient detector can be built; distributions of network loads can provide effective means for estimating packet loss type; and packet delay is a better signal of network state than short-term throughput. We demonstrate how estimation accuracy is influenced by different proportions of congestion versus wireless losses and penalties on incorrect estimation.

Angels: In Network Support for Minimum Distribution Time in P2P Overlays

This paper proposes the use of in-network caches (which we call Angels) to reduce the Minimum Distribution Time (MDT) of a file from a seeder – a node that possesses the file – to a set of leechers – nodes who are interested in downloading the file. An Angel is not a leecher in the sense that it is not interested in receiving the entire file, but rather it is interested in minimizing the MDT to all leechers, and as such uses its storage and up/down-link capacity to cache and forward parts of the file to other peers. We extend the analytical results by Kumar and Ross [1] to account for the presence of angels by deriving a new lower bound for the MDT. We show that this newly derived lower bound is tight by proposing a distribution strategy under assumptions of a fluid model. We present a GroupTree heuristic that addresses the impracticalities of the fluid model. We evaluate our designs through simulations that show that our Group-Tree heuristic outperforms other heuristics, that it scales well with the increase of the number of leechers, and that it closely approaches the optimal theoretical bounds.

Angels: In-Network Support for Minimum Distribution Time in P2P Overlays

This thesis proposes the use of in-network caches (which we call Angels) to reduce the Minimum Distribution Time (MDT) of a file from a seeder – a node that possesses the file – to a set of leechers – nodes who are interested in downloading the file. An Angel is not a leecher in the sense that it is not interested in receiving the entire file, but rather it is interested in minimizing the MDT to all leechers, and as such uses its storage and up/down-link capacity to cache and forward parts of the file to other peers. We extend the analytical results by Kumar and Ross (Kumar and Ross, 2006) to account for the presence of angels by deriving a new lower bound for the MDT. We show that this newly derived lower bound is tight by proposing a distribution strategy under assumptions of a fluid model. We present a GroupTree heuristic that addresses the impracticalities of the fluid model. We evaluate our designs through simulations that show that our GroupTree heuristic outperforms other heuristics, that it scales well with the increase of the number of leechers, and that it closely approaches the optimal theoretical bounds.

On the Origin of Power Laws in Internet Topologies

Recent empirical studies have shown that Internet topologies exhibit power laws of the form for the following relationships: (P1) outdegree of node (domain or router) versus rank; (P2) number of nodes versus outdegree; (P3) number of node pairs y = x^α within a neighborhood versus neighborhood size (in hops); and (P4) eigenvalues of the adjacency matrix versus rank. However, causes for the appearance of such power laws have not been convincingly given. In this paper, we examine four factors in the formation of Internet topologies. These factors are (F1) preferential connectivity of a new node to existing nodes; (F2) incremental growth of the network; (F3) distribution of nodes in space; and (F4) locality of edge connections. In synthetically generated network topologies, we study the relevance of each factor in causing the aforementioned power laws as well as other properties, namely diameter, average path length and clustering coefficient. Different kinds of network topologies are generated: (T1) topologies generated using our parametrized generator, we call BRITE; (T2) random topologies generated using the well-known Waxman model; (T3) Transit-Stub topologies generated using GT-ITM tool; and (T4) regular grid topologies. We observe that some generated topologies may not obey power laws P1 and P2. Thus, the existence of these power laws can be used to validate the accuracy of a given tool in generating representative Internet topologies. Power laws P3 and P4 were observed in nearly all considered topologies, but different topologies showed different values of the power exponent α. Thus, while the presence of power laws P3 and P4 do not give strong evidence for the representativeness of a generated topology, the value of α in P3 and P4 can be used as a litmus test for the representativeness of a generated topology. We also find that factors F1 and F2 are the key contributors in our study which provide the resemblance of our generated topologies to that of the Internet.