In the near future extremely large scale mobile adhoc networks of thousands or tens of thousands of mobile nodes will be physically feasible and desirable for a host of applications. However, routing within these networks is challenging, especially at high data rates and when node movement is highly-dynamic. In this work we present Topology Aware Geographic Routing(TAG), a position-based routing protocol that strategically uses local topology information (when available) to make better local forwarding decisions, decreasing the number of hops required to deliver a packet when compared with other geographic routing protocols. In addition, TAG is able to reliably deliver packets even in topologies that violate the often used but unrealistic unit disk graph and quasi-static assumptions. We present empirical results from a variety of simulations, illustrating how TAG outperforms GOAFR+, GFG, and OLSR in both theoretical environments and in a simulated, real-world, continental-scale airborne network.

We have developed education modules for topics in networking, security, and cloud computing. A networking instructor could use our modules to enhance the teaching of basic concepts by demonstrating these concepts with real experiments on GENI testbeds. Any systems instructor could use our security or cloud computing modules to begin teaching new topics, or enhance existing topics by adding hands-on experiments on GENI and CloudLab testbeds. Our NSF funded projects to develop these curricular modules have been successfully used by several instructors. Attendees at SIGCSE would comprise exactly the kind of audience, from varied institutions and dedicated to enhancing their curriculum, for whom we've built these modules. Our modules are freely available, and we are committed to helping instructors use our modules in their courses. The underlying testbeds, GENI and CloudLab, are also NSF-funded and thus freely available for instructors to use. Attendees will be provided a handout that contains relevant information, including contact for help, as they go back and begin using our education modules in their curriculum.

Civilian Airborne Networks capable of providing network connectivity to users onboard aircraft and users on the ground may soon be viable. We propose a novel airborne network architecture consisting of commercial aircraft and ground station gateways inter-connected with Free-Space Optical Communications (FSOC) links to form a high-bandwidth mesh network. The use of directional FSOC links necessitates explicit topology control, where a protocol must manage which links will point at one another to form connections. The algorithm used by the topology control protocol to form topologies must have low computation time, and must compute topologies that are robust, inclusive, and contain short paths between nodes. We use FAA flight path data for the aircraft en route within the continental United States during a 24-hour period to analyze the properties of airborne mesh networks, and compare candidate topology algorithms. In our simulation an airborne network with ground stations was able to continuously connect over 98% of the air-craft into a mesh network using FSOC links. We propose two new topology algorithms (DCTRT and DCKruskal+Long) which are extensions to existing algorithms. DCKruskal+Long appears to perform best for the metrics measured.

As HTTP-based streaming video applications have grown to become a major source of Internet traffic, and as the new ISO standard Dynamic Adaptive Streaming over HTTP (DASH) gains industry acceptance, researchers need the ability to both study real-world viewing data and simulate realistic DASH streams. The first effort is complicated by the fact that network researchers are often restricted to anonymized, header-only traces. The second effort is difficult since researchers are currently limited to three undesirable options for generating DASH traffic: (1) encode and store movies, which is both labor-and storage-intensive, (2) parameterize DASH traffic, which is open to criticism, or (3) stream movies from a service such as Netflix, which is prohibitively expensive if a researcher wants to simulate many simultaneous viewers. In this paper we present our work towards developing a model for DASH traffic and show how the model can be applied to identify DASH streams from anonymized, header-only traces using a combination of Hadoop and Hive. We then describe how the insight gained from our work will be used in a simulator that can recreate the DASH payloads of real movies using a format that requires only a few kilobytes of storage per movie.

Over the last decade web content has evolved from relatively static pages often delivered by one or two servers, to websites rich with interactive media content served from numerous servers. This content change has affected the associated network traffic. Quantifying and analyzing these changes can lead to updated traffic models and more accurate web traffic simulations for testing new protocols and devices. In this work we analyze the TCP/IP headers in packet traces collected at various times over 13 years on the link that connects the University of North Carolina at Chapel Hill (UNC) to its ISP. We show that while the decade-old methodology for inferring web activity from these packet traces is still viable, it is no longer possible to infer all page boundaries given only the TCP and IP headers. We propose a novel method for segmenting web traffic into Activity Sections, in order to obtain comparable higher level statistics. Using these methods to analyze our data set, we describe trends in the HTTP request and response sizes, and a trend towards longer connection durations. We also show that the number of servers supporting web activity has increased, and present empirical evidence that suggests the number of unused connections has risen, likely due to new speculative TCP preconnect features of popular browsers.

Networking research would be well served by the adoption of a set of traffic benchmarks to model network applications for empirical evaluations; such benchmarks are common in many other areas of computing. While it has long been known that certain aspects of modeling traffic, such as round trip time, can dramatically affect application and network performance, there is still no agreement as to how such components should be controlled within an experiment. In this paper we advance the discussion of standards for empirical networking research by demonstrating how certain components of network traffic, such as the structure of application data exchanges within a TCP connection, can have a larger impact on the results obtained through experimentation than other dimensions of traffic such as round-trip time. Such findings point to the pressing need for traffic benchmarks in networking research. Through testbed experiments performed with synthetically generated network traffic from two very different traffic sources, and using several models of TCP connection structure, we demonstrate the strong effects of connection structure in traffic workload modeling on performance measures such as queue length at routers, number of active connections in the network, user response times, and connection durations.

In this tutorial, we will introduce the SIGMETRICS/Performance community to the vast testbeds, tools and resources openly available through the GENI (Global Environment for Network Innovations) project. We will present details about the distributed computing resources available on GENI for researchers interested in simulation as well as measurement-based performance evaluation experiments. We will demonstrate simple experiments on GENI, and leave them with information on how to run experiments for research and education using GENI resources.

Active queue management (AQM) in routers has been proposed as a solution to some of the scalability issues associated with TCP's pure end-to-end approach to congestion control. A recent study of AQM demonstrated its effectiveness in reducing the response times of Web request/response exchanges as well as increasing link throughput and reducing loss rates [L. Le et al., 2003]. However, use of the ECN (explicit congestion notification) signaling protocol was required to outperform drop-tail queuing. Since ECN is not currently widely deployed on end-systems, we investigate an alternative to ECN, namely applying AQM differentially to flows based on a heuristic classification of the flow's transmission rate. Our approach, called differential congestion notification (DCN), distinguishes between "small" flows and "large" high-bandwidth flows and only provides congestion notification to large high-bandwidth flows. We compare DCN to other prominent AQM schemes and demonstrate that for Web and general TCP traffic, DCN outperforms all the other AQM designs, including those previously designed to differentiate between flows based on their size and rate.

We measured and analyzed the variability in round trip times (RTTs) within TCP connections using passive measurement techniques. We collected eight hours of bidirectional traces containing over 22 million TCP connections between end-points at a large university campus and almost $1$ million remote locations. Of these, we used over 1 million TCP connections that yield 10 or more valid RTT samples, to examine RTT variability within a TCP connection. Our results indicate that contrary to observations in several previous studies, RTT values within a connection vary widely. Our results have implications for designing better simulation models, and understanding how round trip times affect the dynamic behavior and throughput of TCP connections.

We present an empirical study of the effects of active queue management (AQM) on the distribution of response times experienced by a population of web users. Three prominent AQM schemes are considered: the Proportional Integrator (PI) controller, the Random Exponential Marking (REM) controller, and Adaptive Random Early Detection (ARED). The effects of these AQM schemes were studied alone and in combination with Explicit Congestion Notification (ECN). Our major results are: