Savera Tanwir

Dynamic scheduling of network resources with advance reservations in optical grids

Advance reservation of lightpaths in grid environments is necessary to guarantee QoS and reliability. In this paper, we have evaluated and compared several algorithms for dynamic scheduling of lightpaths using a flexible advance reservation model. The main aim is to find the best scheduling policy for a grid network resource manager that improves network utilization and minimizes blocking. The scheduling of lightpaths involves both routing and wavelength assignment. Our simulation results show that minimum-cost adaptive routing where link costs are determined by the current and future usage of the link provides the minimum blocking. For wavelength assignment, we have used a scheme that reduces fragmentation by minimizing unused gaps. We have also analyzed approaches for failure recovery and resource optimization.

A Survey of VBR Video Traffic Models

We have seen a phenomenal growth in video applications in the past few years. An accurate traffic model of VBR video is necessary for performance evaluation of a network design and also for creating synthetic loads that can be used for benchmarking a network. In view of this, various models for VBR video traffic have been proposed in the literature. In this paper, we classify and survey these models. In addition, we implemented four representative video traffic models and compared them using the H.264 AVC video traces available at the Arizona State University video traces library. These models are: the Markov Modulated Gamma (MMG) model, the Discrete Autoregressive (DAR) model, the second order Autoregressive AR(2) model, and a wavelet-based model. The results show that the MMG and the wavelet-based models are suitable for both video conference and IPTV, while the DAR model is good for video conference traffic only. According to our results, the AR(2) model is not suitable for generating any type of H.264 video. A brief overview of SVC, HD, and 3D video is also provided.

EnLIGHTened Computing: An architecture for co-allocating network, compute, and other grid resources for high-end applications

Many emerging high performance applications require distributed infrastructure that is significantly more powerful and flexible than traditional grids. Such applications require the optimization, close integration, and control of all grid resources, including networks. The EnLIGHTened (ENL) computing project has designed an architectural framework that allows grid applications to dynamically request (in-advance or on-demand) any type of grid resource: computers, storage, instruments, and deterministic, high-bandwidth network paths, including lightpaths. Based on application requirements, the ENL middleware communicates with grid resource managers and, when availability is verified, co-allocates all the necessary resources. ENLpsilas domain network manager controls all network resource allocations to dynamically setup and delete dedicated circuits using generalized multiprotocol label switching (GMPLS) control plane signaling. In order to make optimal brokering decisions, the ENL middleware uses near-real-time performance information about grid resources. A prototype of this architectural framework on a national-scale testbed implementation has been used to demonstrate a small number of applications. Based on this, a set of changes for the middleware have been laid out and are being implemented.

A QoS evaluation of video traffic models for H.264 AVC video

In this paper, we evaluated and compared the QoS behavior of video traffic models for H.264 AVC video. The H.264 AVC models that we evaluated are: the Markov Modulated Gamma (MMG) model, the Discrete Autoregressive (DAR) model, the second order Autoregressive AR(2) model, and a wavelet-based model. These models were used to generate synthetic packet traces which were used in a simulation model to estimate the 95th percentile of the end-to-end delay, jitter and packet loss. The QoS metrics of the generated traces are compared with those of the original traces available from the Arizona State University video traces library. We observed that none of the model produces accurate results for every type of video but the MMG model produced the results closest to the actual traces.

Modeling live adaptive streaming over HTTP

Video streaming methods have evolved greatly over the years. Today, the most prevalent technique to stream live and video on-demand is the adaptive HTTP streaming and is used by several commercial vendors. In this paper, we present an approximate analytic model for live adaptive streaming over HTTP. Using this model, we propose a new rate control algorithm that makes the rate transitions less frequent and increases the quality of experience for the viewer. Also, the model can be used to characterize the departure packet process at the video server. To the best of our knowledge, this is the first video traffic model for adaptive HTTP streaming to be reported in the literature.

Modeling 3D video traffic using a Markov modulated gamma process

Three-dimensional (3D) television and video streaming has become very popular over the last few years. Multiview encoded video is used to support 3D video applications. The statistical characteristics of multiview video are significantly different from the traditional single-view video and therefore existing video traffic models are no more applicable for this type of video. In this paper, we propose and evaluate a new model for multiview video that is based on a Markov process. To the best of our knowledge, there is only one other model for multiview video that has been proposed in the literature by Rossi et al. We compared the two models using Q-Q plots and the autocorrelation function of the frame sizes along with QoS metrics of the resulting packet traces estimated by simulation. The comparison results show that our model has less complexity and better accuracy.