6860435
On the stability of network distance estimation
Overlay network distance monitoring and estimation system can benefit many new applications and services, such as peer-to-peer overlay routing and location. However, there is a lack of such scalable system with small overhead, good usability, and good distance estimation accuracy and stability. Thus we propose a scalable overlay distance monitoring system, Internet Iso-bar, which clusters hosts based on the similarity of their perceived network distance, with no assumption about the underlying network topology. The centers of each cluster are then chosen as monitors to represent their clusters for probing and distance estimation. We compare it with other network distance estimation systems, such as Global Network Positioning (GNP) [1]. Internet Iso-bar is easy to implement and use, and has good scalability and small communication and computation cost for online monitoring. Preliminary evaluation on real Internet measurement data also shows that Internet Iso-bar has high prediction accuracy and stability. Finally, by adjusting the number of clusters, we can smoothly trade off the measurement and management cost for better distance estimation accuracy and stability.
15722545
Dynamic Replica Placement for Scalable Content Delivery
In this paper, we propose the dissemination tree, a dynamic content distribution system built on top of a peer-to-peer location service. We present a replica placement protocol that builds the tree while meeting QoS and server capacity constraints. The number of replicas as well as the delay and bandwidth consumption for update propagation are significantly reduced. Simulation results show that the dissemination tree has close to the optimal number of replicas, good load distribution, small delay and bandwidth penalties for update multicast compared with the ideal case: static replica placement on IP multicast.
5913277
OceanStore: an architecture for global-scale persistent storage
OceanStore is a utility infrastructure designed to span the globe and provide continuous access to persistent information. Since this infrastructure is comprised of untrusted servers, data is protected through redundancy and cryptographic techniques. To improve performance, data is allowed to be cached anywhere, anytime. Additionally, monitoring of usage patterns allows adaptation to regional outages and denial of service attacks; monitoring also enhances performance through pro-active movement of data. A prototype implementation is currently under development.
5375789
Clustering Web content for efficient replication
Recently, there has been an increasing deployment of content distribution networks (CDNs) that offer hosting services to Web content providers. We first compare uncooperative pulling of Web contents, used by commercial CDNs, with cooperative pushing. The latter can achieve user perceived performance comparable to the former scheme with only 4-5% of replication and update traffic. Therefore, we explore how to push content to CDN nodes efficiently. Using trace-driven simulation, we show that replicating content in units of URLs can yield 60-70% reduction in clients' latency, compared to replicating in units of Web sites. However, such a fine-grained replication is very expensive. We propose to replicate content in units of clusters, each containing objects which are likely to be requested by clients that are topologically close. We describe three clustering techniques, and use various topologies and several large Web server traces to evaluate their performance. Cluster-based replication achieves 40-60% improvement over per Web site based replication. By adjusting the number of clusters, we can smoothly trade off the management and computation cost for better client performance. We also explore incremental clusterings that adaptively add new documents to the existing content clusters. We examine both offline and online incremental clusterings. The offline clusterings yield close to the performance of the complete re-clustering at much lower overhead. The online incremental clustering and replication cut down the retrieval cost by 4.6-8 times compared to no replication and random replication, so it is especially useful for improving document availability during flash crowds.
166109
End-to-End Inference of Router Packet Forwarding Priority
Packet forwarding prioritization (PFP) in routers is one of the mechanisms commonly available to network administrators. PFP can have a significant impact on the performance of applications, the accuracy of measurement tools' results and the effectiveness of network troubleshooting procedures. Despite their potential impact, no information on PFP settings is readily available to end users. In this paper, we present an end-to-end approach for packet forwarding priority inference and its associated tool, POPI. This is the first attempt to infer router packet-forwarding priority through end-to-end measurement. Our POPI tool enables users to discover such network policies through the monitoring and rank classification of loss rates for different packet types. We validated our approach via statistical analysis, simulation, and wide-area experimentation in PlanetLab. As part of our wide-area experiments, we employed POPI to analyze 156 random paths across 162 PlanetLab nodes. We discovered 15 paths flagged with multiple priorities, 13 of which were further validated through hop-by-hop loss rates measurements. In addition, we surveyed all related network operators and received responses for about half of them confirming our inferences.
1284213
An algebraic approach to practical and scalable overlay network monitoring
Overlay network monitoring enables distributed Internet applications to detect and recover from path outages and periods of degraded performance within seconds. For an overlay network with n end hosts, existing systems either require O(n2) measurements, and thus lack scalability, or can only estimate the latency but not congestion or failures. Our earlier extended abstract [1] briefly proposes an algebraic approach that selectively monitors k linearly independent paths that can fully describe all the O(n2) paths. The loss rates and latency of these k paths can be used to estimate the loss rates and latency of all other paths. Our scheme only assumes knowledge of the underlying IP topology, with links dynamically varying between lossy and normal.In this paper, we improve, implement and extensively evaluate such a monitoring system. We further make the following contributions: i) scalability analysis indicating that for reasonably large n (e.g., 100), the growth of k is bounded as O(n log n), ii) efficient adaptation algorithms for topology changes, such as the addition or removal of end hosts and routing changes, iii) measurement load balancing schemes, and iv) topology measurement error handling. Both simulation and Internet experiments demonstrate we obtain highly accurate path loss rate estimation while adapting to topology changes within seconds and handling topology errors.
2475006
Content-based video indexing for sports applications using integrated multi-modal approach
To sustain an ongoing rapid growth of video information, there is an emerging demand for a sophisticated content-based video indexing system. However, current video indexing solutions are still immature and lack of any standard. This doctoral consists of a research work based on an integrated multi-modal approach for sports video indexing and retrieval. By combining specific features extractable from multiple audio-visual modalities, generic structure and specific events can be detected and classified. During browsing and retrieval, users will benefit from the integration of high-level semantic and some descriptive mid-level features such as whistle and close-up view of player(s).
16055310
Step-by-Step Regression: A More Efficient Alternative for Polynomial Multiple Linear Regression in Stream Cube
Facing tremendous and potentially infinite stream data, it is impossible to record them entirely. Thus synopses are required to be generated timely to capture the underlying model for stream management systems. Traditionally, curve fitting through Multiple Linear Regression (MLR) is a powerful and efficient modeling tool. In order to further accelerate its processing efficiency, we propose Step-by-step Regression (SR) as a more efficient alternative. As revealed in experiments, it speeds up for more than 40 times. In addition, inspired by previous work, we integrated SR into cube environment through similar compression technique to perform online analytical processing and mining over data stream. Finally, experiments show that SR not only significantly alleviates the computation pressure on the front ends of data stream management systems, but also results in a much smaller stream cube for on line analysis and real-time surveillance.
16583184
Using cluster skeleton as prototype for data labeling
A new approach, designed for clustering data whose underlying distribution shapes are arbitrary, is presented. This study is concerned with the use of the skeleton of a cluster as its prototype, which can represent the cluster more closely than that of using a single data point. The given data set is then partitioned into those skeleton-represented clusters without any prior knowledge nor assumptions of hidden structures. A novel function called cluster characteristic function (CCF) has been constructed and the associated theorems have been proposed and proved that the proper number of clusters can be determined with the approach.
41090805
Distributed policy specification and enforcement in service-oriented business systems
Service-oriented computing (SOC) and Web services (WS) provide a flexible computing platform for electronic business and commerce. Introducing policy-based computing to service-oriented business systems adds another dimension of flexibility and security. While service composition and re-composition in service-oriented business systems allow major system reconstruction, policy-based computing can better deal with the small and routine changes of business processing. This paper reports our latest research on integrating policy-based computing into service-oriented business system and discusses its feasibility, benefits and cost. Under this research, we designed a policy specification and enforcement language PSEL for specifying system constraints and business rules. We implemented a runtime environment in which a service-oriented business system can be modeled, analyzed, deployed, and executed with policy enforcement. Automated tools have been developed to facilitate the entire development process. The cost of policy-based computing is experimentally evaluated
9429595
Relay feedback tuning of robust PID controllers with iso-damping property
A new tuning method for proportional-integral-derivative (PID) controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PID controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given frequency called the "tangent frequency" so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. At the "tangent frequency", the Nyquist curve tangentially touches the sensitivity circle. Several relay feedback tests are used to identify the plant gain and phase at the tangent frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PID controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PID controller design. Only several relay tests are needed. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for robust PID controller design with an iso-damping property.