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In a software defined network, the controller needs to obtain/collect traffic measurement information (i.e., flow statistics) from switches for different applications, such as traffic engineering. Existing solutions seldom consider the per-switch cost, which may lead to heavy statistics collection cost (e.g., high CPU overhead) on some switches. Due to limited computing power on most commodity switches, heavy statistics collection cost on those switches may seriously interfere with the basic rule operations, especially when some switches need to deal with many new-arrival flows or update routes of existing flows. To address this challenge, we design and implement efficient flow statistics collection (FSC) with limited interference on the basic rule operations. We formally propose a cooperative flow statistics collection with per-switch cost constraint (CP-FSC) problem. We prove that the CP-FSC problem is NP-hard and present an efficient algorithm with approximation ratio 1/2, based on dynamic programming. To reduce the time complexity, a greedy-based algorithm with approximation ratio 1/3 is also presented. We implement the proposed FSC algorithms on our SDN platform. The experimental results and the extensive simulation results show 36%-59% performance improvement compared with the existing solutions. [ABSTRACT FROM AUTHOR]

Motivated by the breakthroughs of AI in both theory and applications, we are perceiving a great potential for network innovations from a new dimension of intelligence. However, as the engine of networks, switches are designed as "dumb" network elements with the sole purpose of forwarding network packets, and thus a barrier against the entrance of AI as an intrinsic part of the network is unconsciously erected. This article proposes an evolved switch architecture aimed at breaking this barrier and accommodating in-network intelligence. We enhance the current switch architecture by embedding an intelligence plane, which is externalized as an intelligent computation pluggable module of an evolved switch. The module employs an integrated solution of "X86 CPU+GPU+DPDK," which provides a high-performance and high-throughput open platform for hosting in-network intelligence. We further conceive a flexible processing framework for an intelligent traffic measurement, recognition, and traffic regime. We also carry out extensive experiments to demonstrate the capability of the evolved switch by deploying a prototype in a campus network, with two promising application scenarios: in-network application identification and in-network anomaly detection. [ABSTRACT FROM AUTHOR]

Traffic volume estimation at the city scale is an important problem useful to many transportation operations and urban applications. This paper proposes a hybrid framework that integrates both state-of-art machine learning techniques and well-established traffic flow theory to estimate citywide traffic volume. In addition to typical urban context features extracted from multiple sources, we extract a special set of features from GPS trajectories based on the implications of traffic flow theory, which provide extra information on the speed-flow relationship. Using the network-wide speed information estimated from a travel speed estimation model, a volume related high level feature is first learned using an unsupervised graphical model. A volume re-interpretation model is then introduced to map the volume related high level feature to the predicted volume using a small amount of ground truth data for training. The framework is evaluated using a GPS trajectory dataset from 33,000 Beijing taxis and volume ground truth data obtained from 4,980 video clips. The results demonstrate effectiveness and potential of the proposed framework in citywide traffic volume estimation. [ABSTRACT FROM AUTHOR]

Traffic measurement is a critical function in transportation engineering. We consider privacy-preserving point-to-point traffic measurement in this paper. We measure the number of vehicles traveling from one geographical location to another by taking advantage of capabilities provided by the intelligent cyberphysical road systems (CPRSs) that enable automatic collection of traffic data. The challenge is to allow the collection of aggregate point-to-point data while preserving the privacy of individual vehicles. We propose a novel measurement scheme, which utilizes bit arrays to collect “masked” data and adopts maximum-likelihood estimation (MLE) to obtain the measurement result. Both mathematical proof and simulation demonstrate the practicality and scalability of our scheme. [ABSTRACT FROM AUTHOR]

Network-wide traffic measurement is of interest to network operators to uncover global network behavior for the management tasks of traffic accounting, debugging or troubleshooting, security, and traffic engineering. Increasingly, sophisticated network measurement tasks such as anomaly detection and security forensic analysis are requiring in-depth fine-grained flow-level measurements. However, performing in-depth per-flow measurements (e.g., detailed payload analysis) is often an expensive process. Given the fast-changing Internet traffic landscape and large traffic volume, a single monitor is not capable of accomplishing the measurement tasks for all applications of interest due to its resource constraint. Moreover, uncovering global network behavior requires network-wide traffic measurements at multiple monitors across the network since traffic measured at any single monitor only provides a partial view and may not be sufficient or accurate. These factors call for coordinated measurements among multiple distributed monitors. In this paper, we present a centralized optimization framework, LEISURE (Load- EqualIzed mea SUREment), for load-balancing network measurement workloads across distributed monitors. Specifically, we consider various load-balancing problems under different objectives and study their extensions to support both fixed and flexible monitor deployment scenarios. We formulate the latter flexible monitor deployment case as an MILP (Mixed Integer Linear Programming) problem and propose several heuristic algorithms to approximate the optimal solution and reduce the computation complexity. We evaluate LEISURE via detailed simulations on Abilene and GEANT network traces to show that LEISURE can achieve much better load-balanced performance (e.g., 4.75\times smaller peak workload and 70 \times smaller variance in workloads) across all coordinated monitors in comparison to a naive solution (uniform assignment) to accomplish network-wide traffic measurement tasks under the fixed monitor deployment scenario. We also show that under the flexible monitor deployment setting, our heuristic solutions can achieve almost the same load-balancing performance as the optimal solution while reducing the computation times by a factor up to 22.5\times in Abilene and 800\times in GEANT. [ABSTRACT FROM AUTHOR]