Towards Scalable Cost-Effective Service and Survivability Provisioning in Ultra High Speed Networks

Optical transport networks based on wavelength division multiplexing (WDM) are considered to be the most appropriate choice for future Internet backbone. On the other hand, future DOE networks are expected to have the ability to dynamically provision on-demand survivable services to suit the needs of various high performance scientific applications and remote collaboration. Since a failure in aWDMnetwork such as a cable cut may result in a tremendous amount of data loss, efficient protection of data transport in WDM networks is therefore essential. As the backbone network is moving towards GMPLS/WDM optical networks, the unique requirement to support DOE’s science mission results in challenging issues that are not directly addressed by existing networking techniques and methodologies. The objectives of this project were to develop cost effective protection and restoration mechanisms based on dedicated path, shared path, preconfigured cycle (p-cycle), and so on, to deal with single failure, dual failure, and shared risk link group (SRLG) failure, under different traffic and resource requirement models; to devise efficient service provisioning algorithms that deal with application specific network resource requirements for both unicast and multicast; to study various aspects of traffic grooming in WDM ring and mesh networks to derive cost effective solutions while meeting application resource and QoS requirements; to design various diverse routing and multi-constrained routing algorithms, considering different traffic models and failure models, for protection and restoration, as well as for service provisioning; to propose and study new optical burst switched architectures and mechanisms for effectively supporting dynamic services; and to integrate research with graduate and undergraduate education. All objectives have been successfully met. This report summarizes the major accomplishments of this project. The impact of the project manifests in many aspects: First, the project addressed many essential problems that arisen in current and future WDM optical networks, and provided a host of innovative solutions though there was no invention or patent filing. This project resulted in more than 2 dozens publications in major journals and conferences (including papers in IEEE Transactions and journals, as well as a book chapter). Our publications have been cited by many peer researchers. In particular, one of our conference papers was nominated for the best paper award of IEEE/Create-Net Broadnets (International Conference on Broadband Communications, Networks, and Systems) 2006. Second, the results and solutions of this project were well received by DOE Labs where presentations were given by the PI. We hope to continue the collaboration with DOE Labs in the future. Third, the project was the first to propose and extensively study multicast traffic grooming, new traffic models such as sliding scheduled traffic model and scheduled traffic model. Our research has sparkled a flurry of recent studies and publications by the research community in these areas. Fourth, the project has benefited a diverse population of students by motivating, engaging, enhancing their learning and skills. The project has been conducted in a manner conducive to the training of students both at graduate and undergraduate levels. As a result, one Ph.D., Dr. Abdur Billah, was graduated. Another Ph.D. student, Tianjian Li, will graduate in January 2007. In addition, four MS students were graduated. One undergraduate student, Jeffrey Alan Shininger, completed his university honors project. Fifth, thanks to the support of this ECPI project, the PI has obtained additional funding from the National Science Foundation, the Air Force Research Lab, and other sources. A few other proposals are pending. Finally, this project has also significantly impacted the curricula and resulted in the enhancement of courses at the graduate and undergraduate levels, therefore strengthening the bond between research and education

Distributed Coding/Decoding Complexity in Video Sensor Networks

Video Sensor Networks (VSNs) are recent communication infrastructures used to capture and transmit dense visual information from an application context. In such large scale environments which include video coding, transmission and display/storage, there are several open problems to overcome in practical implementations. This paper addresses the most relevant challenges posed by VSNs, namely stringent bandwidth usage and processing time/power constraints. In particular, the paper proposes a novel VSN architecture where large sets of visual sensors with embedded processors are used for compression and transmission of coded streams to gateways, which in turn transrate the incoming streams and adapt them to the variable complexity requirements of both the sensor encoders and end-user decoder terminals. Such gateways provide real-time transcoding functionalities for bandwidth adaptation and coding/decoding complexity distribution by transferring the most complex video encoding/decoding tasks to the transcoding gateway at the expense of a limited increase in bit rate. Then, a method to reduce the decoding complexity, suitable for system-on-chip implementation, is proposed to operate at the transcoding gateway whenever decoders with constrained resources are targeted. The results show that the proposed method achieves good performance and its inclusion into the VSN infrastructure provides an additional level of complexity control functionality

A Survey of Quality of Service Differentiation Mechanisms for Optical Burst Switching Networks

Cataloged from PDF version of article.This paper presents an overview of Quality of Service (QoS) differentiation mechanisms proposed for Optical Burst Switching (OBS) networks. OBS has been proposed to couple the benefits of both circuit and packet switching for the ‘‘on demand’’ use of capacity in the future optical Internet. In such a case, QoS support imposes some important challenges before this technology is deployed. This paper takes a broader view on QoS, including QoS differentiation not only at the burst but also at the transport levels for OBS networks. A classification of existing QoS differentiation mechanisms for OBS is given and their efficiency and complexity are comparatively discussed. We provide numerical examples on how QoS differentiation with respect to burst loss rate and transport layer throughput can be achieved in OBS networks. © 2009 Elsevier B.V. All rights reserved

Burst switched optical networks supporting legacy and future service types

Focusing on the principles and the paradigm of OBS an overview addressing expectable performance and application issues is presented. Proposals on OBS were published over a decade and the presented techniques spread into many directions. The paper comprises discussions of several challenges that OBS meets, in order to compile the big picture. The OBS principle is presented unrestricted to individual proposals and trends. Merits are openly discussed, considering basic teletraffic theory and common traffic characterisation. A more generic OBS paradigm than usual is impartially discussed and found capable to overcome shortcomings of recent proposals. In conclusion, an OBS that offers different connection types may support most client demands within a sole optical network layer

Virtual topology design and flow routing in optical networks under multi-hour traffic demand

This paper addresses the problem of finding a static virtual topology design and flow routing in transparent optical WDM networks under a time-varying (multi-hour) traffic demand. Four variants of the problem are considered, using fixed or dynamically adaptable (i.e., variable) flow routing, which can be splittable or unsplittable. Our main objective is to minimize the number of transceivers needed which make up for the main network cost. We formulate the problem variants as exact ILPs (Integer Linear Programs) and MILPs (Mixed ILPs). For larger problem instances, we also propose a family of heuristics based on the concept of domination between traffic matrices. This concept provides the theoretical foundations for a set of techniques proposed to reduce the problem complexity. We present a lower bound to the network cost for the case in which the virtual topology could be dynamically reconfigured along time. This allows us to assess the limit on the maximum possible benefit that could be achieved by using optical reconfigurable equipment. Extensive tests have been conducted, using both synthetically generated and real-traced traffic demands. In the cases studied, results show that combining variable routing with splittable flows obtains a significant, although moderate, cost reduction. The maximum cost reduction achievable with reconfigurable virtual topologies was shown to be negligible compared to the static case in medium and high loads.The work described in this paper was carried out with the support of the BONE project (“Building the Future Optical Network in Europe”); a Network of Excellence funded by the European Commission through the 7th ICT-Framework Program. This research has been partially supported by the projects from the Spanish Ministry Of Education TEC2007-67966-01/TCM CON-PARTE-1, and TEC2008-02552-E, and it is also developed in the framework of the projects from Fundación Seneca (Regional Agency of Science and Technology of Region of Murcia ) 00002/CS/08 (FORMA) and "Programa de Ayudas a Grupos de Excelencia de la Región. de Murcia”, F. Séneca (Plan Regional de Ciencia y Tecnología 2007/2010)."

Towards Robust Traffic Engineering in IP Networks

To deliver a reliable communication service it is essential for the network operator to manage how traffic flows in the network. The paths taken by the traffic is controlled by the routing function. Traditional ways of tuning routing in IP networks are designed to be simple to manage and are not designed to adapt to the traffic situation in the network. This can lead to congestion in parts of the network while other parts of the network is far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network. We investigate methods for efficient derivation of the traffic situation using link count measurements. The advantage of using link counts is that they are easily obtained and yield a very limited amount of data. We evaluate and show that estimation based on link counts give the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. Furthermore, we evaluate performance of search heuristics to set weights in link-state routing protocols. For the evaluation we have access to complete traffic data from a Tier-1 IP network. Our findings confirm previous studies who use partial traffic data or synthetic traffic data. We find that optimization using estimated traffic demands has little significance to the performance of the load balancing. Finally, we device an algorithm that finds a routing setting that is robust to shifts in traffic patterns due to changes in the interdomain routing. A set of worst case scenarios caused by the interdomain routing changes is identified and used to solve a robust routing problem. The evaluation indicates that performance of the robust routing is close to optimal for a wide variety of traffic scenarios. The main contribution of this thesis is that we demonstrate that it is possible to estimate the traffic matrix with good accuracy and to develop methods that optimize the routing settings to give strong and robust network performance. Only minor changes might be necessary in order to implement our algorithms in existing networks

RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detection

The widespread use of face retouching filters on short-video platforms has raised concerns about the authenticity of digital appearances and the impact of deceptive advertising. To address these issues, there is a pressing need to develop advanced face retouching techniques. However, the lack of large-scale and fine-grained face retouching datasets has been a major obstacle to progress in this field. In this paper, we introduce RetouchingFFHQ, a large-scale and fine-grained face retouching dataset that contains over half a million conditionally-retouched images. RetouchingFFHQ stands out from previous datasets due to its large scale, high quality, fine-grainedness, and customization. By including four typical types of face retouching operations and different retouching levels, we extend the binary face retouching detection into a fine-grained, multi-retouching type, and multi-retouching level estimation problem. Additionally, we propose a Multi-granularity Attention Module (MAM) as a plugin for CNN backbones for enhanced cross-scale representation learning. Extensive experiments using different baselines as well as our proposed method on RetouchingFFHQ show decent performance on face retouching detection. With the proposed new dataset, we believe there is great potential for future work to tackle the challenging problem of real-world fine-grained face retouching detection.Comment: Under revie