We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.

/pdf/no-reference-image-quality-assessment-in-the-spatial-domain-2mnlvf25ig.pdf

No-Reference Image Quality Assessment in the Spatial Domain

An important aim of research on the blind image quality assessment (IQA) problem is to devise perceptual models that can predict the quality of distorted images with as little prior knowledge of the images or their distortions as possible. Current state-of-the-art “general purpose” no reference (NR) IQA algorithms require knowledge about anticipated distortions in the form of training examples and corresponding human opinion scores. However we have recently derived a blind IQA model that only makes use of measurable deviations from statistical regularities observed in natural images, without training on human-rated distorted images, and, indeed without any exposure to distorted images. Thus, it is “completely blind.” The new IQA model, which we call the Natural Image Quality Evaluator (NIQE) is based on the construction of a “quality aware” collection of statistical features based on a simple and successful space domain natural scene statistic (NSS) model. These features are derived from a corpus of natural, undistorted images. Experimental results show that the new index delivers performance comparable to top performing NR IQA models that require training on large databases of human opinions of distorted images. A software release is available at http://live.ece.utexas.edu/research/quality/niqe_release.zip.

/pdf/making-a-completely-blind-image-quality-analyzer-4rsald8tn3.pdf

Making a “Completely Blind” Image Quality Analyzer

Over the past decade, there has been an explosion of interest in network research across the physical and social sciences. For social scientists, the theory of networks has been a gold mine, yielding explanations for social phenomena in a wide variety of disciplines from psychology to economics. Here, we review the kinds of things that social scientists have tried to explain using social network analysis and provide a nutshell description of the basic assumptions, goals, and explanatory mechanisms prevalent in the field. We hope to contribute to a dialogue among researchers from across the physical and social sciences who share a common interest in understanding the antecedents and consequences of network phenomena.

/pdf/network-analysis-in-the-social-sciences-440fzedv4c.pdf

Network Analysis in the Social Sciences

I. the origin of species by means of natural selection

We propose ViNO (Virtual Network Overlay), an orchestration service that can be used to create arbitrary network topologies with OVS (Open vSwitch) switches and VMs. ViNO connects switches and VMs through an overlay network using VXLAN encapsulation. ViNO provisions VMs by making API calls to the underlying platform. Users specify the desired topology using an expressive Domain Specific Language that allows users to easily express commonly used network topologies while hiding the underlying complexity. An important use case for ViNO is enabling the seamless migration of Linux services across VMs in different regions with very little downtime.

ViNO: SDN overlay to allow seamless migration across heterogeneous infrastructure

The flexibility of cloud computing has afforded users the ability to develop and deploy fully customizable cloud-based applications and services. Thus far, this flexibility has primarily been constrained to the likes of x86 servers and storage devices. The SAVI application platform testbed was developed to realize the hypothesis that all physical infrastructure resources can be virtualized. Key to this work is a novel control and management framework based on Software-Defined Infrastructure (SDI), a concept which provides a unified programmable interface over heterogeneous infrastructures. In this paper, we present the architecture of the Canadian SAVI national testbed based on the SDI framework. The design of an autonomous SAVI node will be described and the multi-node deployment that comprise the national testbed will be discussed. In addition, the orchestration of applications across the multi-node testbed will be described. Lastly, we will report on the progress of our recent efforts to federate the SAVI testbed with the American GENI national testbed.

SAVI Testbed Architecture and Federation

Next-generation service providers will offer an array of services and applications that is media-rich, personalized, and context-aware. In this future environment, new services and applications will be introduced, provisioned, operated, maintained, and retired at a pace that tracks changing customer requirements and demands. In order to be cost-effective, these services and applications need to be delivered over an all-IP infrastructure, which is autonomically managed to ensure their delivery at a satisfactory level for subscribed customers. In this paper, we propose a blueprint for a generic Autonomic Service Architecture (ASA) to address these challenges.

Blueprint for an Autonomic Service Architecture

In this paper, we address the problem of robust network design. Robustness is defined as the minimizing variations in network performance, such as average delay and throughput, due to the perturbations in the network, e.g. topology, demand and community of interest. We pose an optimization problem is cast in the form of minimizing a new robustness metric that is a function of network routing, capacities and external traffic demand distributions. We use the correspondence between packet flows in a network and random walk models. We use Markov chain sensitivity analysis to derive a new measure and prove its robustness property. We show that the node robustness factor, along with the Kemeny constant, plays a critical role in the robustness of a network. We evaluate the proposed robust optimization method by comparing to conventional network design approaches.

Robust network design

Cloud data centers today use visualization technologies to facilitate allocation of physical resources to multiple applications. As cloud data centers continue to grow in scale and complexity, effectively monitoring and identifying system anomalies is becoming a critical problem. Furthermore, due to complex dependencies between system components in a virtualized data center, a single cause of anomaly can typically trigger multiple alarms. Therefore there is also a need to efficiently analyze and identify the causes of the anomalies in a scalable and effective manner, in order to reduce the overhead of diagnosis and troubleshooting performed by the cloud operator. Motivated by these observations, we present a mechanism for automatic anomaly detection and root cause analysis in virtualized cloud data centers. We first use unsupervised learning techniques to identify abnormal system behaviors, and then propose a technique for root cause analysis with consideration to anomaly propagation among system components. Using a real virtualized cloud testbed, we show that our mechanism efficiently identifies system anomalies and accurately determines their causes.

Alberto Leon-Garcia

Papers

ViNO: SDN overlay to allow seamless migration across heterogeneous infrastructure

SAVI Testbed Architecture and Federation

Blueprint for an Autonomic Service Architecture

Robust network design

Automated anomaly detection and root cause analysis in virtualized cloud infrastructures