1184325
Profiling internet backbone traffic: behavior models and applications
Recent spates of cyber-attacks and frequent emergence of applications affecting Internet traffic dynamics have made it imperative to develop effective techniques that can extract, and make sense of, significant communication patterns from Internet traffic data for use in network operations and security management. In this paper, we present a general methodology for building comprehensive behavior profiles of Internet backbone traffic in terms of communication patterns of end-hosts and services. Relying on data mining and information-theoretic techniques, the methodology consists of significant cluster extraction, automatic behavior classification and structural modeling for in-depth interpretive analyses. We validate the methodology using data sets from the core of the Internet. The results demonstrate that it indeed can identify common traffic profiles as well as anomalous behavior patterns that are of interest to network operators and security analysts.
14141879
Improving VoIP quality through path switching
The current best-effort Internet cannot readily provide the service guarantees that VoIP applications often require. Path switching can potentially address this problem without requiring new network mechanisms, simply by leveraging the robustness to performance variations available from connectivity options such as multi-homing and overlays. In this paper, we evaluate the effectiveness and benefits of path switching in improving the quality of VoIP applications, and demonstrate its feasibility through the design and implementation of a prototype gateway. We argue for an application-driven path switching system that accounts for both network path characteristics and application-specific factors (e.g., codec algorithms, playout buffering schemes). We also develop an application path quality estimator based on the ITU-T E-model for voice quality assessment, and an application-driven path switching algorithm that dynamically adapts the time scales over which path switching decisions are made to maximize voice quality. Through network emulation and experiments over a wide-area multi-homed test bed, we show that, with sufficient path diversity, path switching can yield meaningful improvements in voice quality. Hence by exploiting the inherent path diversity of the Internet, application-driven path switching is a viable option in providing quality-of-service to applications.
3065647
A core stateless bandwidth broker architecture for scalable support of guaranteed services
We present a novel bandwidth broker architecture for scalable support of guaranteed services that decouples the QoS control plane from the packet forwarding plane. More specifically, under this architecture, core routers do not maintain any QoS reservation states, whether per-flow or aggregate. Instead, the QoS reservation states are stored at and managed by a bandwidth broker. There are several advantages of such a bandwidth broker architecture. Among others, it avoids the problem of inconsistent QoS states faced by the conventional hop-by-hop, distributed admission control approach. Furthermore, it allows us to design efficient admission control algorithms without incurring any overhead at core routers. The proposed bandwidth broker architecture is designed based on a core stateless virtual time reference system developed recently. This virtual time reference system provides a unifying framework to characterize, in terms of their abilities to support delay guarantees, both the per-hop behaviors of core routers and the end-to-end properties of their concatenation. We focus on the design of efficient admission control algorithms under the proposed bandwidth broker architecture. We consider both per-flow end-to-end guaranteed delay services and class-based guaranteed delay services with flow aggregation. Using our bandwidth broker architecture, we demonstrate how admission control can be done on a per domain basis instead of on a "hop-by-hop" basis. Such an approach may significantly reduce the complexity of the admission control algorithms. In designing class-based admission control algorithms, we investigate the problem of dynamic flow aggregation in providing guaranteed delay services and devise a new apparatus to effectively circumvent this problem. We conduct detailed analyses to provide theoretical underpinning for our schemes as well as to establish their correctness. Simulations are also performed to demonstrate the efficacy of our schemes.
14053219
Distributed Algorithm for Service Replication in Service Overlay Network
The service overlay network (SON) is an effective mean to deploy end-to-end QoS guaranteed content delivery services on the current Internet. We model the content delivery service on a SON as a service delivery tree (SDT). Previous studies have addressed the optimal distribution tree formulation issues. In this paper, we focus on the problem of maximizing the total operation profit of the SON. In [1], authors introduced a cost model for optimal bandwidth provisioning in SON. In this paper, we extended this concept and propose an alternative approach to maximize the total effective throughput of SDT as well as to minimize the QoS violation penalty of the SON by service replication. We present both centralized and distributed algorithms for the placement of replicated servers on the SDT. Experiments are carried to quantify the merit, effectiveness and the scalability of the proposed service replication algorithm. In particular, the performance gain is very close to the exhaustive search. The algorithm performs well when we scale up the service overlay networks. Finally, we show that one only needs to perform a small number of replications to attain the optimal gain.
33893889
Supporting stored video: reducing rate variability and end-to-end resource requirements through optimal smoothing
Variable-bit-rate (VBR) compressed video can exhibit significant multiple-time-scale bit-rate variability. In this paper we consider the transmission of stored video from a server to a client across a network, and explore how the client buffer space can be used most effectively toward reducing the variability of the transmitted bit rate. Two basic results are presented. First, we show how to achieve the greatest possible reduction in rate variability when sending stored video to a client with given buffer size. We formally establish the optimality of our approach and illustrate its performance over a set of long MPEG-1 encoded video traces. Second, we evaluate the impact of optimal smoothing on the network resources needed for video transport, under two network service models: deterministic guaranteed service (Chang 1994; Wrege et al. 1996) and renegotiated constant-bit-rate (RCBR) service (Grossglauser et al. 1997). Under both models, the impact of optimal smoothing is dramatic.
5876943
Adaptive packet sampling for flow volume measurement
Traffic measurement and monitoring are an important component of network management and traffic engineering. With high-speed Internet backbone links, efficient and effective packet sampling techniques for traffic measurement and monitoring are not only desirable, but also increasingly becoming a necessity. Since the utility of sampling depends on the accuracy and economy of measurement, it is important to control sampling error. In this paper we propose and analyze an adaptive, stratified random packet sampling technique for flow-level traffic measurement. In particular, we address the theoretical and practical issues involved. Through theoretical studies and experiments, we demonstrate that the proposed sampling technique provides unbiased estimation of flow size with controllable error bound, in terms of both packet and byte counts for elephant flows, while avoiding excessive oversampling.
14080651
GcpSensor: a CPU performance tool for grid environments
In grid computing environments, CPU performance discovery and allocation is a serious issue. Before they run their applications, grid users need to choose grid nodes based on available CPUs performance. Hence a tool is needed for measuring CPUs performance. To address this need, GcpSensor (grid CPU performance sensor) was developed. GcpSensor runs a typical benchmark program in every grid node to measure CPU performance and returns a metric named WMFLOPS which can be used to compare different CPUs performance directly. An algorithm, which can choose a moderate computation cost dynamically, is designed to ensure that every test is comprehensive and the added overhead is comparatively small. Our experiments have proved that GcpSensor can indicate the CPU performance sensitively and that users can use the GcpSensor data to compare different CPUs performance straightforward, thus the accuracy and efficiency for selecting computing resources are increased.
758376
Applying data mining in investigating money laundering crimes
In this paper, we study the problem of applying data mining to facilitate the investigation of money laundering crimes (MLCs). We have identified a new paradigm of problems --- that of automatic community generation based on uni-party data, the data in which there is no direct or explicit link information available. Consequently, we have proposed a new methodology for Link Discovery based on Correlation Analysis (LDCA). We have used MLC group model generation as an exemplary application of this problem paradigm, and have focused on this application to develop a specific method of automatic MLC group model generation based on timeline analysis using the LDCA methodology, called CORAL. A prototype of CORAL method has been implemented, and preliminary testing and evaluations based on a real MLC case data are reported. The contributions of this work are: (1) identification of the uni-party data community generation problem paradigm, (2) proposal of a new methodology LDCA to solve for problems in this paradigm, (3) formulation of the MLC group model generation problem as an example of this paradigm, (4) application of the LDCA methodology in developing a specific solution (CORAL) to the MLC group model generation problem, and (5) development, evaluation, and testing of the CORAL prototype in a real MLC case data.
13300239
Mining Inter-Entity Semantic Relations Using Improved Transductive Learning
This paper studies the problem of mining relational data hidden in natural language text. In particular, it approaches the relation classification problem with the strategy of transductive learning. Different algorithms are presented and empirically evaluated on the ACE corpus. We show that transductive learners exploiting various lexical and syntactic features can achieve promising classification performance. More importantly, transductive learning performance can be significantly improved by using an induced similarity function.
5218256
Instruction set extension with shadow registers for configurable processors
Configurable processors are becoming increasingly popular for modern embedded systems (especially for the field-programmable system-on-a-chip). While steady progress has been made in the tools and methodologies of automatic instruction set extension for configurable processors, the limited data bandwidth available in the core processor (e.g., the number of simultaneous accesses to the register file) becomes a potential performance bottleneck. In this paper we first present a quantitative analysis of the data bandwidth limitation in configurable processors, and then propose a novel low-cost architectural extension and associated compilation techniques to address the problem. The application of our approach results in a promising performance improvement.
5700504
Localized algorithm for aggregate fairness in wireless sensor networks
For data-collection applications in sensor networks, it is important to ensure all data sources have equal (or weighted) access to network bandwidth so that the base stations receive a complete picture about the monitored area. We point out the fairness problem in the current design of sensor networks, which may cause extremely biased bandwidth allocations. It is a challenge to design a fully distributed fairness solution due to the lack of global knowledge about the distribution of data sources and their routing paths. This paper proposes a new aggregate fairness model and a localized algorithm (called AFA) that implements the model. AFA is designed to work with any routing protocol. In particular, it allows the packets from a data source to follow an arbitrary set of forwarding paths to the base stations. This flexibility makes it considerably harder to allocate bandwidth fairly among different data sources. AFA solves the problem with only localized operations at the sensors. It is easy to implement, which is an attractive property for sensor networks. Moreover, the algorithm automatically adjusts a sensor's forwarding rate to avoid packet drops due to downstream congestion, which helps improve energy efficiency. We perform extensive simulations, demonstrating that the proposed algorithm can effectively improve end-to-end fairness.