Ratul Mahajan

Measuring ISP topologies with Rocketfuel

To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, the elimination of redundant measurements by exploiting properties of IP routing, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the interdomain peering structure. As part of this work, we release our maps to the community.

Measuring ISP topologies with rocketfuel

To date, realistic ISP topologies have not been accessible to the research community, leaving work that depends on topology on an uncertain footing. In this paper, we present new Internet mapping techniques that have enabled us to directly measure router-level ISP topologies. Our techniques reduce the number of required traces compared to a brute-force, all-to-all approach by three orders of magnitude without a significant loss in accuracy. They include the use of BGP routing tables to focus the measurements, exploiting properties of IP routing to eliminate redundant measurements, better alias resolution, and the use of DNS to divide each map into POPs and backbone. We collect maps from ten diverse ISPs using our techniques, and find that our maps are substantially more complete than those of earlier Internet mapping efforts. We also report on properties of these maps, including the size of POPs, distribution of router outdegree, and the inter-domain peering structure. As part of this work, we release our maps to the community.

Controlling high bandwidth aggregates in the network

The current Internet infrastructure has very few built-in protection mechanisms, and is therefore vulnerable to attacks and failures. In particular, recent events have illustrated the Internets vulnerability to both denial of service (DoS) attacks and flash crowds in which one or more links in the network (or servers at the edge of the network) become severely congested. In both DoS attacks and flash crowds the congestion is due neither to a single flow, nor to a general increase in traffic, but to a well-defined subset of the traffic --- an aggregate. This paper proposes mechanisms for detecting and controlling such high bandwidth aggregates. Our design involves both a local mechanism for detecting and controlling an aggregate at a single router, and a cooperative pushback mechanism in which a router can ask upstream routers to control an aggregate. While certainly not a panacea, these mechanisms could provide some needed relief from flash crowds and flooding-style DoS attacks. The presentation in this paper is a first step towards a more rigorous evaluation of these mechanisms.

Diversity in smartphone usage

Using detailed traces from 255 users, we conduct a comprehensive study of smartphone use. We characterize intentional user activities -- interactions with the device and the applications used -- and the impact of those activities on network and energy usage. We find immense diversity among users. Along all aspects that we study, users differ by one or more orders of magnitude. For instance, the average number of interactions per day varies from 10 to 200, and the average amount of data received per day varies from 1 to 1000 MB. This level of diversity suggests that mechanisms to improve user experience or energy consumption will be more effective if they learn and adapt to user behavior. We find that qualitative similarities exist among users that facilitate the task of learning user behavior. For instance, the relative application popularity for can be modeled using an exponential distribution, with different distribution parameters for different users. We demonstrate the value of adapting to user behavior in the context of a mechanism to predict future energy drain. The 90th percentile error with adaptation is less than half compared to predictions based on average behavior across users.

Understanding BGP misconfiguration

It is well-known that simple, accidental BGP configuration errors can disrupt Internet connectivity. Yet little is known about the frequency of misconfiguration or its causes, except for the few spectacular incidents of widespread outages. In this paper, we present the first quantitative study of BGP misconfiguration. Over a three week period, we analyzed routing table advertisements from 23 vantage points across the Internet backbone to detect incidents of misconfiguration. For each incident we polled the ISP operators involved to verify whether it was a misconfiguration, and to learn the cause of the incident. We also actively probed the Internet to determine the impact of misconfiguration on connectivity.Surprisingly, we find that configuration errors are pervasive, with 200-1200 prefixes (0.2-1.0% of the BGP table size) suffering from misconfiguration each day. Close to 3 in 4 of all new prefix advertisements were results of misconfiguration. Fortunately, the connectivity seen by end users is surprisingly robust to misconfigurations. While misconfigurations can substantially increase the update load on routers, only one in twenty five affects connectivity. While the causes of misconfiguration are diverse, we argue that most could be prevented through better router design.

Augmenting mobile 3G using WiFi

We investigate if WiFi access can be used to augment 3G capacity in mobile environments. We rst conduct a detailed study of 3G and WiFi access from moving vehicles, in three different cities. We find that the average 3G and WiFi availability across the cities is 87% and 11%, respectively. WiFi throughput is lower than 3G through-put, and WiFi loss rates are higher. We then design a system, called Wiffler, to augments mobile 3G capacity. It uses two key ideas leveraging delay tolerance and fast switching -- to overcome the poor availability and performance of WiFi. For delay tolerant applications, Wiffler uses a simple model of the environment to predict WiFi connectivity. It uses these predictions to delays transfers to offload more data on WiFi, but only if delaying reduces 3G usage and the transfers can be completed within the applications tolerance threshold. For applications that are extremely sensitive to delay or loss (e.g., VoIP), Wiffler quickly switches to 3G if WiFi is unable to successfully transmit the packet within a small time window. We implement and deploy Wiffler in our vehicular testbed. Our experiments show that Wiffler significantly reduces 3G usage. For a realistic workload, the reduction is 45% for a delay tolerance of 60 seconds.

Achieving high utilization with software-driven WAN

We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the networks data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.

Measurement-based models of delivery and interference in static wireless networks

We present practical models for the physical layer behaviors of packet reception and carrier sense with interference in static wireless networks. These models use measurements of a real network rather than abstract RF propagation models as the basis for accuracy in complex environments. Seeding our models requires N trials in an N node network, in which each sender transmits in turn and receivers measure RSSI values and packet counts, both of which are easily obtainable. The models then predict packet delivery and throughput in the same network for different sets of transmitters with the same node placements. We evaluate our models for the base case of two senders that broadcast packets simultaneously. We find that they are effective at predicting when there will be significant interference effects. Across many predictions, we obtain an RMS error for 802.11a and 802.11b of a half and a third, respectively, of a measurement-based model that ignores interference.

Controlling high-bandwidth flows at the congested router

FIFO queueing is simple but does not protect traffic from high-bandwidth flows, which include not only flows that fail to use end-to-end congestion control, but also short round-trip time TCP flows. At the other extreme, per-flow scheduling mechanisms provide max-min fairness but are more complex, keeping state for all flows going through the router. This paper presents RED-PD (Random Early Detection-Preferential Dropping), a mechanism that combines simplicity and protection by keeping state for just the high-bandwidth flows. RED-PD uses the packet drop history at the router to detect high-bandwidth flows in times of congestion and preferentially drops packets from these flows. This paper discusses the design decisions underlying RED-PD. We show that it is effective at controlling high-bandwidth flows using a small amount of state and very simple fast-path operations.

A first look at traffic on smartphones

Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.