Internet users can be classified as two types: (a) active users-actively contribute to blogs, publish opinions, write comments in Youtube, tweet messages, etc. and (b) passive consumers who only consume Internet information without contributing to it. While the majority of current social media research deals with active user analysis, there is very little work in understanding the dynamics of passive consumers and their influence. Our global scale Internet measurement of user access patterns of a diverse set of Internet media services indicates conclusively that majority of consumers are passive. In this paper, we develop a spatio-temporal mathematical model and the corresponding stochastic analysis to understand the passive consumer dynamics. Both discrete and continuous time analysis are presented. We also show how the analysis can be used to identify spatial points of influence, i.e., spatial locations that have maximum expertise or influence on a topic. The analysis takes into account, the initial level of consumption at each spatial location and the influence of different passive consumers at different geographic locations on each other. The effect of information noise is taken into account to derive fundamental limits of passive information consumption. Theoretical results are verified using real Internet measurement data. The large scale data have been made available for other researchers.

This paper presents large-scale Internet measurements to understand and improve the effects of Internet path selection on perceived video quality, or quality of experience (QoE). We systematically study a large number of Internet paths between popular video destinations and clients to create an empirical understanding of location, persistence, and recurrence of failures. These failures are mapped to perceived video quality by reconstructing video clips and conducting surveys. We then investigate ways to recover from QoE degradation by choosing one-hop detour paths that preserve application-specific policies. We seek simple, scalable path selection strategies without the need for background path monitoring. Using five different measurement overlays spread across the globe, we show that a source can recover from over 75% of the degradations by attempting to restore QoE with any k randomly chosen nodes in an overlay, where k is bounded by O(ln(N)). We argue that our results are robust across datasets. Finally, we design and implement a prototype packet forwarding module called source initiated frame restoration (SIFR). We deployed SIFR on PlanetLab nodes and compared the performance of SIFR to the default Internet routing. We show that SIFR outperforms IP-path selection by providing higher on-screen perceptual quality.

With the proliferation of multimedia content on the Internet, there is an increasing demand for video streams with high perceptual quality. The capability of present-day Internet links in delivering high-perceptual-quality streaming services, however, is not completely understood. Link-level degradations caused by intradomain routing policies and inter-ISP peering policies are hard to obtain, as Internet service providers often consider such information proprietary. Understanding link-level degradations will enable us in designing future protocols, policies, and architectures to meet the rising multimedia demands. This paper presents a trace-driven study to understand quality-of-experience (QoE) capabilities of present-day Internet links using 51 diverse ISPs with a major presence in the US, Europe, and Asia-Pacific. We study their links from 38 vantage points in the Internet using both passive tracing and active probing for six days. We provide the first measurements of link-level degradations and case studies of intra-ISP and inter-ISP peering links from a multimedia standpoint. Our study offers surprising insights into intradomain traffic engineering, peering link loading, BGP, and the inefficiencies of using autonomous system (AS)-path lengths as a routing metric. Though our results indicate that Internet routing policies are not optimized for delivering high-perceptual-quality streaming services, we argue that alternative strategies such as overlay networks can help meet QoE demands over the Internet. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

This paper presents large scale Internet measurements to understand and improve the effects of Internet path selection on perceived video quality. We systematically study a large number of Internet paths between popular video destinations and clients to create an empirical understanding of location, persistence and recurrence of failures. We map these failures to perceptual quality by reconstructing video clips obtained from the trace to quantify both the perceptual degradations from these failures as well as the fraction of such failures that can be recovered.

Recently, there has been increasing demand for video streams with high perceptual quality. The capability of present day Internet links in delivering high perceptual quality streaming services, however, is not completely understood. This paper presents a trace driven study to understand QoE capabilities of present day Internet links using 51 diverse ISPs with a major presence in US, Europe and Asia-Pacific. We study a rich collection of their intra-domain links and inter-domain peering links from 38 vantage points in the Internet using both passive tracing and active probing. We provide the first measurements of link level degradations from a multimedia standpoint. While we observe that ISP are internally well connected and their present peering policies are highly conducive to streaming services, the overall perceptual quality delivered is below "acceptable". Our results indicate that the fault lies with poor intra-domain traffic engineering, BGP, and the usage of AS-path lengths as the routing metric. We also provide a comprehensive case study for every scenario we investigate. Streaming services apart, our Internet measurement results can be used as an input to a variety of research problems.

To prevent subscriber churn, network service providers of VoD, SDV and IPTV have a pressing need to pro-actively detect, isolate and fix outages within an access network. Network induced degradations prove to be detrimental for streaming applications. This typically leads to a poor quality of experience (QoE) for subscribers. By monitoring key functional points of the access network for traces of degradation, service providers can devise mechanisms to mitigate the problem. In this work we propose a hierarchy of exporters, collectors and ANCON (ANalysis and CONtrol) nodes that can semi-autonomously monitor, detect and isolate impairments within an access network. Exporters on the data plane gather and disseminate statistics for individual subnets, which are streamed onto "collector" nodes on an orthogonal plane. Collector nodes aggregate traffic from various exporters, and stream them onto the root of the tree (ANCON). With an even placement of exporters, root cause analysis can now take the granularity of loss rates or delay rates in individual segments or subnets of an access network. As an extension to our architecture, we show that the overlay can support instrumentations of quality evaluation for streaming video. As an example, we use a simple MOS model that is in part an extension of the ITU-T Erlang model to predict the quality of a video stream much before it reaches the end user. Extensive simulations are presented to justify the design choices made in the process.

We present a scalable, lightweight, no-reference framework to infer video QoE. Our framework revolves around a one time offline construction of a k-dimensional space, which we call the QoE space. The k-dimensions accommodate k parameters (network-dependent/independent) that potentially affect video quality. The k-dimensional space is partitioned to N representative zones, each with a QoE index. Instantaneous parameter values are matched with the indices to infer QoE. To validate our framework, we construct a 3-dimensional QoE space with bit-rate, loss, and delay as the principal components. We create 18 video samples with unique combinations of the 3 parameters. 77 human subjects rated these video samples on a scale of 1 to 5 to create the QoE space. In a second set of survey, our predicted MOS was compared to 49 human responses. Results show that our MOS predictions are in close agreement with subjective perceptions. An implementation of our framework on standard Linux PC shows we can compute 20 MOS calculations per second with 3 parameters and 18 partitions of the QoE space.

Numerous routing protocols have been proposed for wireless sensor networks, each of which is highly optimized for a certain class of traffic, like real time, reliable sense and disseminate, network reprogramming, energy efficiency and so on. However, a typical deployment demands an arbitrary communication pattern that generates multiple traffic types simultaneously. Arguably, no single routing protocol can completely cater to a deployment's various flavors. In this paper, we propose a dynamic routing framework that can replace the traditional routing layer with a collection of routing decisions. We allow application packets to carry a two-bit preamble that uniquely describes the nature of communication sought for. The framework dynamically wires the appropriate routing component from a set of well-defined suite. We conduct extensive simulation experiments that generates a concurrent mix of different traffic types - each having its own, and often conflicting, communication demands. For such an application, we show that we could meet each traffic types demands for reliability, delay, path distribution, link losses and congestion losses. We also show that service differentiation can indeed be met successfully, and practical deployments can be an imminent reality.

(MANETs) clearly a key ingredient to this. Primary constraints to system design in WSNs are bandwidth, energy, memory and processing. One pattern very evident from the very incipient stages of research in this area is the dominance of Internet design being transported to these environments. Though the Internet architecture is extremely appropriate for its environment, we argue that transporting it to WSN could only prove disastrous. What is to be learnt from the Internet is more a philosophy of building successful stacks. To that end we investigate ”Stack Aware Architectures ” and advocate its inclusion in stack designs. We also validate this concept by analyzing the Internet architecture for this property. Another pattern very evident in WSN research is that individual modules are validated over a set of workloads at least thought to be representative of the entire application domain. The class of applications and their requirements in WSNs, however, are varied. We go on further to analyze classes of applications that use WSNs, their individual requirements from the layers beneath, and a basic example to a dynamic and adaptive stack for WSN, suited for all application types (based on a per-packet mapping of functions required for it), which of course is also stack aware.