Joel Sommers

Power Awareness in Network Design and Routing

Exponential bandwidth scaling has been a fundamental driver of the growth and popularity of the Internet. However, increases in bandwidth have been accompanied by increases in power consumption, and despite sustained system design efforts to address power demand, significant technological challenges remain that threaten to slow future bandwidth growth. In this paper we describe the power and associated heat management challenges in todays routers. We advocate a broad approach to addressing this problem that includes making power-awareness a primary objective in the design and configuration of networks, and in the design and implementation of network protocols. We support our arguments by providing a case study of power demands of two standard router platforms that enables us to create a generic model for router power consumption. We apply this model in a set of target network configurations and use mixed integer optimization techniques to investigate power consumption, performance and robustness in static network design and in dynamic routing. Our results indicate the potential for significant power savings in operational networks by including power-awareness.

Cell vs. WiFi: on the performance of metro area mobile connections

Cellular and 802.11 WiFi are compelling options for mobile Internet connectivity. The goal of our work is to understand the performance afforded by each of these technologies in diverse environments and use conditions. In this paper, we compare and contrast cellular and WiFi performance using crowd-sourced data from Speedtest.net. Our study considers spatio-temporal performance (upload/download throughput and latency) using over 3 million user-initiated tests from iOS and Android apps in 15 different metro areas collected over a 15 week period. Our basic performance comparisons show that (i) WiFi provides better absolute download/upload throughput, and a higher degree of consistency in performance; (ii) WiFi networks generally deliver lower absolute latency, but the consistency in latency is often better with cellular access; (iii) throughput and latency vary widely depending on the particular access type e.g., HSPA, EVDO, LTE, WiFi, etc.) and service provider. More broadly, our results show that performance consistency for cellular and WiFi is much lower than has been reported for wired broadband. Temporal analysis shows that average performance for cell and WiFi varies with time of day, with the best performance for large metro areas coming at non-peak hours. Spatial analysis shows that performance is highly variable across metro areas, but that there are subregions that offer consistently better performance for cell or WiFi. Comparisons between metro areas show that larger areas provide higher throughput and lower latency than smaller metro areas, suggesting where ISPs have focused their deployment efforts. Finally, our analysis reveals diverse performance characteristics resulting from the rollout of new cell access technologies and service differences among local providers.

A learning-based approach for IP geolocation

The ability to pinpoint the geographic location of IP hosts is compelling for applications such as on-line advertising and network attack diagnosis. While prior methods can accurately identify the location of hosts in some regions of the Internet, they produce erroneous results when the delay or topology measurement on which they are based is limited. The hypothesis of our work is that the accuracy of IP geolocation can be improved through the creation of a flexible analytic framework that accommodates different types of geolocation information. In this paper, we describe a new framework for IP geolocation that reduces to a machine-learning classification problem. Our methodology considers a set of lightweight measurements from a set of known monitors to a target, and then classifies the location of that target based on the most probable geographic region given probability densities learned from a training set. For this study, we employ a Naive Bayes framework that has low computational complexity and enables additional environmental information to be easily added to enhance the classification process. To demonstrate the feasibility and accuracy of our approach, we test IP geolocation on over 16,000 routers given ping measurements from 78 monitors with known geographic placement. Our results show that the simple application of our method improves geolocation accuracy for over 96% of the nodes identified in our data set, with on average accuracy 70 miles closer to the true geographic location versus prior constraint-based geolocation. These results highlight the promise of our method and indicate how future expansion of the classifier can lead to further improvements in geolocation accuracy.

Network Performance Anomaly Detection and Localization

Detecting the occurrence and location of performance anomalies (e.g., high jitter or loss events) is critical to ensuring the effective operation of network infrastructures. In this paper we present a framework for detecting and localizing performance anomalies based on using an active probe-enabled measurement infrastructure deployed on the periphery of a network. Our framework has three components: an algorithm for detecting performance anomalies on a path, an algorithm for selecting which paths to probe at a given time in order to detect performance anomalies (where a path is defined as the set of links between two measurement nodes), and an algorithm for identifying the links that are causing an identified anomaly on a path (i.e., localizing). The problem of detecting an anomaly on a path is addressed by comparing probe-based measures of performance characteristics with performance guarantees for the network (e.g., SLAs). The path selection algorithm is designed to enable a tradeoff between ensuring that all links in a network are frequently monitored to detect performance anomalies, while minimizing probing overhead. The localization algorithm is designed to use existing path measurement data in such a way as to minimize the number of paths necessary for additional probing in order to identify the link(s) responsible for an observed performance anomaly. We assess the feasibility of our framework and algorithms by implementing them in ns-2 and conducting a set of simulation-based experiments using several different network topologies. Our results show that our method is able to accurately detect and localize performance anomalies in a timely fashion and with lower probe and computational overheads than previously proposed methodologies.

Accurate and efficient SLA compliance monitoring

Service level agreements (SLAs) define performance guarantees made by service providers, e.g, in terms of packet loss, delay, delay variation, and network availability. In this paper, we describe a new active measurement methodology to accurately monitor whether measured network path characteristics are in compliance with performance targets specified in SLAs. Specifically, (1) we describe a new methodology for estimating packet loss rate that significantly improves accuracy over existing approaches; (2) we introduce a new methodology for measuring mean delay along a path that improves accuracy over existing methodologies, and propose a method for obtaining confidence intervals on quantiles of the empirical delay distribution without making any assumption about the true distribution of delay; (3) we introduce a new methodology for measuring delay variation that is more robust than prior techniques; and (4) we extend existing work in network performance tomography to infer lower bounds on the quantiles of a distribution of performance measures along an unmeasured path given measurements from a subset of paths. We unify active measurements for these metrics in a discrete time-based tool called SLA M . The unified probe stream from SLA M consumes lower overall bandwidth than if individual streams are used to measure path properties. We demonstrate the accuracy and convergence properties of SLA M in a controlled laboratory environment using a range of background traffic scenarios and in one- and two-hop settings, and examine its accuracy improvements over existing standard techniques.

A machine learning approach to TCP throughput prediction

TCP throughput prediction is an important capability for networks where multiple paths exist between data senders and receivers. In this paper, we describe a new lightweight method for TCP throughput prediction. Our predictor uses Support Vector Regression (SVR); prediction is based on both prior file transfer history and measurements of simple path properties. We evaluate our predictor in a laboratory setting where ground truth can be measured with perfect accuracy. We report the performance of our predictor for oracular and practical measurements of path properties over a wide range of traffic conditions and transfer sizes. For bulk transfers in heavy traffic using oracular measurements, TCP throughput is predicted within 10% of the actual value 87% of the time, representing nearly a threefold improvement in accuracy over prior history-based methods. For practical measurements of path properties, predictions can be made within 10% of the actual value nearly 50% of the time, approximately a 60% improvement over history-based methods, and with much lower measurement traffic overhead. We implement our predictor in a tool called Path-Perf, test it in the wide area, and show that PathPerf predicts TCP throughput accurately over diverse wide area paths.

A geometric approach to improving active packet loss measurement

Measurement and estimation of packet loss characteristics are challenging due to the relatively rare occurrence and typically short duration of packet loss episodes. While active probe tools are commonly used to measure packet loss on end-to-end paths, there has been little analysis of the accuracy of these tools or their impact on the network. The objective of our study is to understand how to measure packet loss episodes accurately with end-to-end probes. We begin by testing the capability of standard Poisson-modulated end-to-end measurements of loss in a controlled laboratory environment using IP routers and commodity end hosts. Our tests show that loss characteristics reported from such Poisson-modulated probe tools can be quite inaccurate over a range of traffic conditions. Motivated by these observations, we introduce a new algorithm for packet loss measurement that is designed to overcome the deficiencies in standard Poisson-based tools. Specifically, our method entails probe experiments that follow a geometric distribution to 1) enable an explicit trade-off between accuracy and impact on the network, and 2) enable more accurate measurements than standard Poisson probing at the same rate. We evaluate the capabilities of our methodology experimentally by developing and implementing a prototype tool, called BADABING. The experiments demonstrate the trade-offs between impact on the network and measurement accuracy. We show that BADABING reports loss characteristics far more accurately than traditional loss measurement tools.

Harpoon: a flow-level traffic generator for router and network tests

We describe Harpoon, a new application-independent tool for generating representative packet traffic at the IP flow level. Harpoon is a configurable tool for creating TCP and UDP packet flows that have the same byte, packet, temporal, and spatial characteristics as measured at routers in live environments. We validate Harpoon using traces collected from a live router and then demonstrate its capabilities in a series of router performance benchmark tests.

An active measurement system for shared environments

Testbeds composed of end hosts deployed across the Internet enable researchers to simultaneously conduct a wide variety of experiments. Active measurement studies of Internet path properties that require precisely crafted probe streams can be problematic in these environments. The reason is that load on the host systems from concurrently executing experiments (as is typical in PlanetLab) can significantly alter probe stream timings. In this paper we measure and characterize how packet streams from our local PlanetLab nodes are affected by experimental concurrency. We find that the effects can be extreme. We then set up a simple PlanetLab deployment in a laboratory testbed to evaluate these effects in a controlled fashion. We find that even relatively low load levels can cause serious problems in probe streams. Based on these results, we develop a novel system called <scp>MAD</scp> that can operate as a Linux kernel module or as a stand-alone daemon to support real-time scheduling of probe streams. <scp>MAD</scp> coordinates probe packet emission for all active measurement experiments on a node. We demonstrate the capabilities of <scp>MAD</scp>, showing that it performs effectively even under very high levels of multiplexing and host system load.

Fast, accurate simulation for SDN prototyping

Thorough test and evaluation of new software-defined network (SDN)-based applications and configurations present many challenges. Examples of these challenges include scaling to large networks, accuracy, and efficiency in evaluation along with the ability to easily transition between prototype and test environments. Current methods for test and evaluation include new programming languages and frameworks, debugging and static analysis techniques, and VM- and container-based emulation tools. In this paper we describe a simulation-based tool called fs-sdn that complements and expands upon these existing approaches. Our work is designed to address the problem of prototyping and evaluating new SDN-based applications accurately, at large scale, and in a way that enables easy translation to real controller platforms like POX and NOX. We describe the design, implementation and use of fs-sdn, and demonstrate its capability by carrying out a series of experiments using fs-sdn and the Mininet platform in nearly identical configurations. We show that the measurements derived from fs-sdn are accurate compared with Mininet, but offer significant speed and scalability advantages.